{"id":104,"date":"2026-10-17T10:51:35","date_gmt":"2026-10-17T10:51:35","guid":{"rendered":"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/?page_id=104"},"modified":"2026-10-18T18:25:45","modified_gmt":"2026-10-18T18:25:45","slug":"stress-and-scenario-models","status":"publish","type":"page","link":"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/stress-and-scenario-models\/","title":{"rendered":"Stress and scenario models"},"content":{"rendered":"\n<!-- Nexus network page \u00b7 https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/stress-and-scenario-models\/ \u00b7 source authored \u00b7 generated by integrations\/wordpress\/network\/build.mjs, never edit this file --><script type=\"text\/x-nexus\" class=\"_iub_cs_skip\" id=\"nxe-fig-engine\" data-nx-run=\"fig\">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<\/script><script type=\"application\/json\" class=\"_iub_cs_skip\" id=\"nxe-gl-sf-climate-stress\" data-nx-pack=\"deflate\">7LwHlBzXdSD6KrzKVR2nJw8GjcA0GFXnbsiyREpWsrSSTZH2Hq6ONQB6AFggQCH1UFp512ubPWN5\/+73fps9\/PbRfq+9Mign2V7bijZNy5IpUUwAASLPYHLnUDn9X10NCAqWQXHA\/bsHVVNd9d6976Z3732vXr0zfzYCwKeiJ6O74xPRj+cfj+6OHpvevffQwUenjud3HTt+NH\/sWHQi+vGDh\/dFd0d\/oH7P1LF8dPenoo8dPfKL7v14dHf00FR+KjoR3Rvd\/UgqPhETP\/rpiej00alH89Hdj+yKxSYSqYlEbDI1kY5Npj46ET0S3f1IMuviTUQ\/Ed0tToqxieihKZffp6Lb90R3PyJOxMRMLtmlc2TviWM3AN3qiZyYjLuwowdP5o\/eBMxlE+mJWDKX7gLzhw7mp11gIbo7nhAnogeiu2PZ1ER0Rozu3hXLiZnJ5ET08W4hHs9OZiaiM7Ho7lgu5coafdx9zmYTLtKeGzzisUQiPhGPxTMpl8n+qYOHXQ2yqU9\/+nsV25s\/dKgr2tH8sejuxET0cHR3MpGdiH5ib1dlV+dPnPQeMxPRj0d3Z27mk4ynxcxERhSzLpuD+45Fd0ezCTE7FZvOJmKxuDjdLaW8UtIt5bolMZfd45WmuijZKa9yr4uZi8e9UtaD5T0qCe8mesTiXinpldIeMdG7eYw8mtlczivFvJtHLLfPI+3RzHpyZpMezR5pj2av+fXSPk\/AvR6xXnOvQbaLkt0T826emjlPzVzWu\/X080rddrFYwmOb60mW8m5TXUyxR8wTcE8P02Ob61piOu2ZIJH2Sl7zPXGv+V6vlPZK+S5KPOVheuLmvHbxfV5lt+NymV6lJ67oweIeTMx6JdErTXklr0GmRzruVea8UsIree3SPSHyHqOenB5mPOGV9nglj0rCo5nxtM10hZhOJr2bB4t7xsp4jMRuZS7loYhJr+QJmOg2z\/ZMkOjR9NolPZSetkkP1rVgbFr0dL+O4ume6jHqYXpGTsU9Dp6cCU+Hnixeg+metkkP5tkzG+8J79kl3WPrGeQ6pneL9dh6DWJJr7nXoCdLT9tUT1vP5J5Zc2lPv17HxTyHTnudE\/M6PNnTtsfBK6U8WMojFvMYxXqMekp7VDzJ8p6R83FP91jOY5TzMHvcs16l1+GxLunsdVt7lkh54sY8S6R6cnpuk5ryOHi6T\/X4xbzbPu8mejdPzpjHaKonkuc2SY\/DlGeJpFeZ8vpoqqd0r51nz0zSo5n3blnv1lM65928rvKSVd5z4XzCYzvlyTLlGSTZY+QpNuWZIN1D6dH01BR7+u3xSp4QnkNPd3NWLJ\/o8fOa7\/HcbcoTfo\/nKOle5ZRX8roxm\/JKHhWvA\/I91896jDyfz3tenk94kvXce48nRNYz5J4eTa+5l3uy6R6KZ8FeSujm3Vg22ePg2dPz+bwXFnmxJ5JnpazH3YuAvBdVeS8CprMeyp4eac+eXh6czvbMute7eS6c7WF6HLxAyHthkfcCIe+FTN6LgHyqx93TL9cTwrOLFwj5VE9Nz+S5nmQ9W3vm8YaQbC\/icj2Remw974n1YB7bfE9Nzzx5j60XD\/lUT3jv1ksJ3sCQ78VDz9l7eT7nxZ8XhtM5L\/f04i\/ncfCCJJ\/vaetJlu\/R7Ongcc97AnpBks97t6me1J49p3rG8gTsun4i1\/PB7tiYyKV7AnpW6kZjopel8tOeDtOeCfI9Kp6V8p4QXuTk857w3QBK9PJnPu\/BenGU99p1YyWR63pkIteNgEQu7UnWzVmJ61nKi838tKf7tKfYtGeXvZ5IXnBlk57N9nri5j2bZT1ivUyU91B64bSnxz3hoXji7unJ4gkY91AyPSoerJd7pj2Npj2D7PVE8uIvv9cTd6\/XvJeQvMDL7\/UYZT1i3dSVyHXjL5GLe6bL9Np5knnDbn6fx3afJ0umZ6we997NE8kLtfxe75bt6ef1dC+R5Tx+XesmcmKPpocpejBvXpDf1+PuCe\/FUX7fXg\/T03af5y\/JXnMPlvQs2EtkOU9c0VM65XEQe9w90+3r8etSmY57KKkeB0+VfZ6xenlpn9e8OygmvDlYwptgJXLdJJDwMm0iJ3qSpXpCxD0OPR32eKWeEFPdyl522+f5SzcsEtM9\/xR7KJ7SMY9KNz0lvAlWwpt1JbKeDtMZ75bolVKeZJ7w3USWmO65W6xX6VHxutibISW8yVfCm3wlvMlXYjrjqeJ1nDt7in56IpqP7o6KcTElxsWkKIrxhBhPZkQxlk6LqVgyEXO7QYyls6lkPBmLpeNiKi7GkzFRjMfcVtl4LCamEm4zMZ5KiIlEIpnNpsVMIp5IxWPZbCqZSKbcYjYWyyZT8WwmFksnMplELB2L5zK5WCYXT6Tj6Uwsl0mms5lcLJvKJrLxbDKXzuXSsVwuk8tlk7lMNhvP5rLdylQulU3m4olcNpVNZdK5RCaRTWRS8XQy7aInsvF4JpvIJpOJXC6bTmUymWTGbZLIphLpeCqZzaRz6Uwmk0ml0vF0LJ1IxGKpZDqZSeUSrgzZVDaTSeWS2VQym0yl0ulMPJtMZWJZMZtIp2Mup0wiG0unEulcIpdIZ9KxdCqTzKWS8VQmFUum4vFYLJPMplOxdCaZdjWJxeLZXErMpZIZN3QS8Xg8KSaT8URSFHMpMe7OGd2qtCjGEhnX5BnX3K4BsslsLhFLJJIxMZaIZ9NiysVJitGJ6LTbeW\/sSNz0nBYz3y3E\/tkmKVHsIqYyXiMxmRCzYs5rEs+KyVg8lxXTmXQPP5kRk67ePYo50c1SSTET90piwrWrKCbFeCaV6FIXxWQyl0rlcknRxRRzbqO4GBcTYiqby7goyRsypmOe2Ddkz914SMfjHk5OTN\/QNy12TZj5Xg1d6XLfLea+xzppMdvjeF0lT8y02FXcfZ9+nWaPdtcN3mjn\/a9xxFxL32TsmHvEY27GuFGKx7wzEbt+xBPxRLz74waLdyTc9JJIxuPJeNIrJZLJZDKRSKYSiVTixpFMppKplPvrHd3ndCrt3rpHOt0tXi+kM+l0JpPOpLPp7pFJZ7JuHnCTRDabyWZyme5fNtc9ohPd9SnXHWOuWyYTYiIpJsVYPOk+xOKJZCodTyVS8UTSpZtIxeKJWCKdTSTjsXg6no3lXISu7+TEXCadSWbdPJtz1x2ymVS33n1xz+ZcKWK5WDwXj6fd5Odmq2w8l02mk9l0LpFy6WRjbnrqpq9UKhVPpGOZWDyRSiUzmXjK5RRLx9LxZDIV72apZCydiWXEVDIRTybSyVQsk41lxFgqlowlXFO62S6WEbtR0rV3IpXIZOPZWFYUM6lEPBlPxdOJeCbm1mTFjJhIJuOpWJdrxqWcdmmn45lELBPr8o6lRNcwbvJ1z3gqlorFYm4wJ9OJtJhIuNBEzI3BmBjr9p2bRbtjihhPpWJiOu3mj1jcNXk8nU65CTmejMXd1JPLppPddJ+JJ+OxpJhNprJiJpFOxGOim\/ldQol4PJ1MJWKJeMxdUTwc3f1I9MFCfl\/+cHQi+u6Dh7vLhBPRf3XkaGHq8ehE9KHDB4\/n943\/9MHD+\/cdedTFmcofPZIff9+xyehE9Nlf6+H\/1LHjRw4fdBcpPzB1\/GT34V35w49OHf24W3Xw+IETUx74Pfmjj04ddil\/+Eiv7fuOHcqPH5ke\/+CUK8P7juavy5A\/fiB\/1C2466IfODGTf3TPkRNH9\/9\/fN\/5yfzeA1167z46dXhv3iV8In\/08LG8S\/n9+aPew4OHjpyc+ngX7\/4Tx44f7T6998Th\/VNHXfDPHnm0J9WDhYPHP+nxckU4PnXIhb\/z6JGp4x780JGT+R5q\/uie7sMDR44dPjg1PnV43\/h780c\/6ZrjgROH9k95XB6cOjz+wamjBw8fcQuPucuYE9EPHjk8tdet+Okjx46cPOLVHM8fzu8\/esQz+vED4x+c2pvf1zPmw1PHD+7tmuXDR44eP7F\/6pCryqE91415NJ\/vKv\/BqUPHp6LuquhBL5e6OcX1q3j37s5oevn\/BiTRg7i\/qRsZqetW3eu7NfFeTbo7dKR7Q4H37A2Uie6V7Q08mV7Zq\/Pqc72RxMO+Xt8LezHXK3vXVPdya\/d0IVO9ur3d331iXtzTPV34PnGPmO9heL\/7elAPskec6sETN36nelDvzIv7ejj7bmC5154unnd68JvpeBjXz2lxv7hfPCBOda8p8aA43YO49S50qge9\/vuL4sfFj4sHxI+Lh8RDN6A3YxwQH+2dh8UD4iHxcBfTgx7pwo\/cwDncvVzo4S7mY+KB7nWkd7nwAz2sT4iHxaNdmo\/dwHusS++YeFg83r0O9PA+cROtm\/GOd6\/rOAfEE+LJHqZ7FsSZLtYx8RPiCfETYkE8IZ4QH+vePZ6F7nX9fFz8ZEzs4p4QC+Jj4olY7DosFhcLMXdCUojFY8lYUnzMvbqwx7x7D8ODFNyrdz52nYLXzoVdx+hhXcdwS4\/Fki6mR+WmdoXrPLvQLnZ3fLhxRifczx+fih45ui9\/9NDj7qMbLe43lsn4hDiZcj\/IuO8H3ZrEhDiZ6H7Y2Hfw2D\/XJP69TVIT4mSu2+bAkeMHjpzwPg\/daNGFi9mbm+TcqthH3S8mx\/J7jx9xP+E8Ev2pw\/mj+7sp\/fjBQwePH8y7WfUjR6cOH3vsyNHjbjrMTx0azx87PnXcTSn37z96cO+JQ8dPHPUSzOET01N7j584evDwfjfRHMsfdqk+Ik6mJ2KT6Y9OuHwnYl3hHxEnsxOxSdF9dGsmxC5CbDLlPmY8hNREbDL20Y+6H6b25A91aUU\/cOTwviOHoxPuByIxM7FLnIzFs25SO\/549KMTj0Q\/PHX04LHoRDI7mU1lJuKTiVT8JugHp\/YdPbgvOpEUJ5OxzMSuxGRGTN4E\/9kjj+ajE8nYZE5MTMTik8lc+iboA\/mjh9wknYpPut\/YEpNJMXUT+Oemjh6bKnTB8cREPDYpxm7m\/fDB\/OHDU13R4mJ2IpaeTGQSN8EfPH5k78cPHDn0aHQilZtMxHMTseykmM7dhHL\/8QOuTScSmclcNjkRT0xmvkf5+48fmjp8\/ODe8Q\/tzbtDQjI9sSvrdsSUp31+38Hj+aNHpw7npw6PP+h+MEyk3c5JuR\/brmN5Q0wXmkpPJHqAj34agk+Bf2UYCFinUTOMg98Co4Zu4jIqUODj6C\/o7QpS91PBERvv4Kp2Bvwiwhvrf48uB6\/Yl2mBWgRlaZHa8KmozgXC7+H7+wKkTOOcSqPyP1I4NyDwvkl8J8Io\/YKGsR0BBPCGv0kBQcAlAUA+ghmL0XO03ccLIbwfPF8\/C+pkR1mmFxtKYIM3AZvYyZhMvZ9ABtkVFm+3zYbcCTr+SPm8aZax83VdwTpbON6HdAaREDUsDITXX8bKvIQ1+LthOzzMY4wM27RDRVCZ0ft4sCu0GiDRrZwaIkGfzhARnRBO2H0buiJf\/HPJsIKtDdngP2IOYlNKuEWX186tbay1a81n8PvUfs02ZKfcOE+c0YJaluoDA+1dV+LP9V8JVbLn3yq3z27lX7Nfs3Zs3NsWavTqgtyPMkLQeE\/9rjolIZca7Zpda+N6HWupZdNnYqopvwwCKm2FQbLmtC92VFCtfxOeNf+R\/zuVVpbBGZm0VHzBVNlLyIZybqGmyo7kP9d+pr2uaLZMle0N6JA07B\/ZNbglTMCIn8W3We9Ov6ezRoIwelx3fLWrfI23sSGZhWAvPmgAiSCQkNYiwDX8zwy6zVEWY+otJAxW8N8yNBSFGoWyZ5ptGdMHSNyyG9BGW7gNRwUV7XB9gNAcCBD4LaNtoDILgA+CDH5Rdzq8BLgOi4M43tYpGeEViwcfwld0bUzTYMOuUZqlQJvFEcIMm5hlkuAXcNKwVNogqxjZGTQBDh7BfkUDOhVS2xRYR4d0bF3QIYpDW+hQHcCAB7BXNeNbEEE5HqdqVKTjM0NNmwaD2F9oZVK3jDKBsZZa9VHUCAV47FkNaJRF+xGdwMjIwmoAcPgfafW2vOH4dHv5OaLDRqgKpHCJRnBojbFGON3CCfDL2O9rtkaqGGsSph0Af439uVYB5n0mXr3qN8E1voOtUwpBIBw08QEStdkWS4BvYu\/RDE5WyCsQx4Yg+M\/YOzWkPWg5wiqKg1\/G4hroQNrAdoAp9GnNOsMA2ORUqi1VdSjVkWvUGmEFVoS72Aa5fShA+E2O9xGiHV0ckn06otZNgI41WN1hq3XEYWTD2qhW8QoEPPLHhlyjFphBdeD\/Ae9wJmlb49blvwmeRsvQwPpIFF+kmtqHhYi+Ti0IStjpry35lkzkMoc1G+o\/LLXOnW6dw1qm0bF0skKoNtuC9fbzqKTaxIJ\/jRz1OcEBGmH7eCKoMhjR30JoVhuDjMxjAzjkykGU1fmFgC5gkXMjZFi1y4EmtqZrhOmoJILpaMW\/TCxLV9Hz7XV9odxUv6Rc02Sr1reAyuYi9TxYYZZVVa\/5JbRK6kTdJ4OOH+H8OwesMRyhWzw6oJLE6FbxndgQx9EgIFEcKe24QqOcRgMsaIRGdpKPWA\/ISf2nmn7rLgVh\/ZYPBrR7VWhRLEcPrg4yP6P6NFSPUNSgTMO+oC9CvFXPEH4\/hVP6DjrqbPsVcP0kwAececoUmK+zhsxrOyComW8jzHVcYwZleAD8gjFJG9\/Q28Qy1dx2cfTP4T9o+rK9Ilk62Wg+53+JUkdlTDOa\/GWk1pH1Gkc4S1oLL1NBfEd4wPmcqTAOhNgAbQuLfRqhkr5BhLokVAyZv0ZDQIUMAkEJ1dcCKkURBk+NYOv9zeAgWfVjgXp\/P+aoX0iSGL\/zPjAMWSSg4roPGYQc08F8NivpDoYexCC7zATYnydf5FdJvY\/RPgjvld6C7oK0nx1+uxAZmQKRz4DPgF8H7m8U\/IFFkx17sU\/1rb1nQzZr7LPKmn2mrTXr1RfOnzbb\/VAI08M0aX+F0yKjo4AdhljY4FYgQgQFhnt7fYyM2vw7QFp\/EtRM8Jvg\/wS\/SavBS9n1QSQw4Kd9IwF+EIuwYW0ICFqO8bVYP0+SftYmBsLjQsbyyboKIUna5njNaQiKaXA+f0isUO3Gar2+3DSW2JV7ruEy9c0z577RUlWkzHYEzYcN1RA0yBAaXyV0Vte1LTh4Rf\/3QGsIgoZS4JD+MWDJsOVDQVBjTFRX3w1edF50DHaxKSHGdg1TBvmIyq7pEoY0CM2WOsYaDRRONs4GZLRtm1VnebUFDTlQp3CsJtTCZu2qhLVacoVYlc+i2tA1Fvp05IpcVTFdM9dJQPAMCvUg4xsMQapvYISEUGZ8egT1MRTOWVvwJOTwOh3obEHGmtEgOIf9BtZeXrdVzux0FHWj1tKrfiFUI\/GIM3w3FrRoitaCHHgb9h9RQyatIWRLBGXuHQacqWng3PK3V\/8j+DD+GdCm0MB2\/1iH9fkZOBAejzITS6xN3DsaCC0FX8n5xmpbHYod3ejrA78EPg1+yfs1FbyhAqLZlqQyWrYti7RWO6q+4YQUU7oGZIg6LcLUHYnUNV1ovkasGVYHSmG9s9iu0nBZQ1WlgmiEZV7BW5Rk6azTL5PEWC3cuFu2tcHVsGZXXyv\/vRmgd6qsWZZMTV394\/KLzl8Yu2VeX97gDG0NOLJsLtDr9Qq8WHvujKkQFf33ZYhWNi7LdkVo17GL6gvEGRIOY+nv6AMYHWwhYZJgTHdOQTfHlyOyrQyqeHPQwpqcHLGxGovepXM6Dc0QAa5i\/xem21AmGTtk1iH4EP5+zNFIiNCaRoBP4Y9glo3bEAW8LkHwiPnXJqjhDoHLviD4lvmg5QzaUEcWykqNWvbLqKa26TYOBu7ldji4aVq4bBDgRfPrFvcqY1s727RuUeC4ecrUTbwa5PE+CxpIp14CKTtvyJQ2VtcsSPAqr97TCmCjrSHwTh\/ZvF+hGigxpEU0guhTd0GUjFjjLcZqybZB1dH62uKXauvrT64tyAH52l999eLKy3w9oIc0\/PTfdCxDblgbbd1RGUO2n68vNBpLrXrdVKkO0pIu4xeNJXjOfK686Kyoa9eqqKraZsP\/6kA1YOISbQVDQFZVThl4jUZVyT59jyIQI0O4XyCc5sj6EBHq90Gy3tfi7VAZvZfzRUhfOxBhzCgypMbew8rYetgmdfxbqq5V4be1dVrjaFZFdOVqZyVgBsJIm6cJNQCMDRIIDCIFqiHo34JsZUYZE8dCWSvcHDDD+B6iXws2jjH\/ppmhgp0Uso0Mx8HHwQp+fq3TqFnma+Fl8zxesc+vN+pn6GWpoX\/T6Oimgum6\/V\/xTvAqXN+2Psr5\/U6UGAlsDZP9E0hUxkCQZehQNWQKdcfBMc4UBIYII0PaVoZHhpV0m28ld4FfdH4DxSqGKjmgobTUa7qhUFW7fUEjVU0iLJlYsuvBjVoFO69KcAU3mTGhAVsBFJEgEASsDE0CFUYItPNQXxTNUONWmEMJFoQ2mJbwMHjWfgzvjJjD7WDgHjliIlbNMCSsFTA0ydJq68aV0Mq9irmxbq6VrSbdlLCa9BXKJOgRgbpIIVQjWKM2aFOvybhmqi3FsdoEgFr\/10ZeoyxI2GEKEs7wGjoGsfGXA1hACBKYZK5jDkb3WQgJEcsPaBwJneMIqkVgQYkmKb\/T5x+03nUujhJLaLO\/\/paOZmtS0KxbAAfTtgpwFTKKfRQIxCT9q8A7fwVcf0IuKZeJC6baUPUFWO6rDeA+mYBDC0E\/GFSC9ig3SuBkP4Xwa6RBYvQq9kqECpwZLOOSQPou8Zjgo22hydFhG20EVuA6uNZ8mW4zkLCpS+q6b81aotocHJWhMtgA5T6KZxkgXKMstCFAXiAHSGmkHsG2yUNb+wV6kG2RMGhAqIz4g2Y\/Gg6E21MYTiBkNbDi\/G3ojL9p8BxGtqQr1vntb7vrIz8x9dapu6OkPXRl9H+AabAfTPeBc+h78cUL2hg2Ng5R38a7QFwh5PBCs0Jjv40\/if12YIijAjiZHtvpEDz4A3uSRg0KxgEhIRXfsr1Aytvezm7\/Gvgq2AMQZ5RsDOhbKNRm+YQRkIRXm+rzOMZbEEckTZHw1Q5KLa1dRdY07AKsB+pWi1jwXW6cal2TJfkq85+Y\/8NcwA0K8P7A5VCVgagPosxG\/Sxfw5e5JQzgAbaGkhwZeMm3PmLbutBmN\/gOMWwynM8ZwSmWRCnK5kg6SDsBP4Vsj7IgMoqPj6fkYTCkJtC7y1uwIMnQo9S7+3zE3fLwt7cvDl+cvByoghdbVwlDbfLvBb9nPa2yIXrcISSqoygk2W8kyRBFmCmNqKBWv8S88MUV6rIj4\/WNJr+hdZr2q9W+NloWtEDTqK6v6Ev9iK+lm42llZZ+lq6\/yv6Fcu7u0Bjar0SoYdXqY9ra2T6T7h+kKJ\/eWKqaG5iU5Gg7HNm9hTAoNBaMYm9p0gBCoJBmgH0UfAz5GdghFfvCVo1VO3VWAZ2OtVJefOYv16i2rQi6GfoC3yZXkW+qqtWmVp0VvdKS0cUAgrdCVaUqyaDGWIjE2L4NzsJWuRVjQ6jgJilQlnGNW4HLVbZ1afyMr49uGcv8Ennep8DljmPV7+rrk4gqi4Zbgz7MFCy2HSA5qf\/8+EiqqYwHbXgJOqF9wb1DO+BWfgf7IOhvTug4e3GhX92C30eGrIfNh6yHnSzuX80+5\/vG6BWk9sL6evVeHa7bsqo4MvklIBCXQLl8LaJKV5DF4DeatV3VkWt1\/VUJPT1yWa5cNdYastRc5C427HpIpp7rq+40NU3CtboKG\/byKipra\/6yo8mCaUqXDPr5P2xean8JvAwlEkEsRFU0rIVaDI9byDJ7RbjUVx3EMdiK8UPGClvGAU2x9tCu5T\/qVJwqaUZgiCf9xhjJ0Nvs1wg5EvWRwZ0GLJvDIXpE+VutUllVbb1Wq1Qv0SaD+BjSwHF7FIboc4wkmAOJzlmyQ2pbTWrKnCC2qyGJRHHO5LJIhTIEWpOqhiPhz1N6W7IkfFF7hr5MKEGbiMJ6YC0A0LSUQXcRQ\/Y\/0E+jEqnzOMaYnD9AhvCQNkbsIFj9n3Db34++TX0bzqENAiGyuKH\/HV63vtMXDrzdSSls854tL1MopOEO2kAr0ALvNSmcAA9IAhFAcrgF3g\/eT4BRSNlOg0FRxoC6Q4AX8D67AxCF1INoiyeADR9wkDKhEMMmqZg5EEKO2MgaJbGsQBI2fxUskxKnaTVa0dpE25A0m7poVfF1ukWMtu5GeH4FCYTXg8L4XW8ftTgYEMYCATZK\/ILkM\/qXnEsNe0l2X5kGm2h10QQ6penqeVhVNy4Nrnc02FnuhBo6CR5E\/tZSdXmZumo2eRX5eRwMo7ts0IJ3GasQGAhiazKuCkEJQHAS+W+WwtttahUbxMEk+iUbyNBv6DuBrO822vTG1pYhsW2jSp0DZWrVuCqAWVAEs95vyB8cG37\/1gd+JpO\/Dx9jIhZn6h3QoS\/fo\/UZY9w7tDEsGBhY4q2f3ALut5\/EVGzpLmVra6tCKQ3QKiPask8H6ssXZKpiU01WC63zKwTqtGmVYwU14mD+MOSCenBABQMJMAR+ieD77Z3suD7cGLbDFqE5CKXjBGkN1H1oX4Va0jbubll1or1FImp2p880rDWmbcjkoiIJjr4uWzWqrUtlxamtNRHtmq8NqXFdFEIgWt\/+8thy8iJTBXUC3G\/8E2K1ocICm5E4HDxo\/CmCtVHKgEPgGetXcfZdK1Etci18XjNqbdtsO8\/WymoFr+mnoYVUt68OmgQ5giHBMGRGtH6OwQI+cBr8W1txOow0ogBJapqwYdTpDZb2N1lkcMAQiO7YgugsG2HaTrmVBv8WtfCwssXHVGgdfCfyFbtJmSy+vTbkDPwEM+F8SPVDv\/k2bQu6jd\/hEJWA\/NbKfc2QuuVKWxtv1ZCFlqRerbQayHpNC6uoqaB1AiW1xlm0VpdBU1kk2tYzqBY2CTX0otWB4PecVx1cDsAGZVg42K7\/FxDEazHwrQxw0E8R1IKvGj7DfD1cR\/T2BfsSuYA\/azSoBvIF+fTaP+TsIB4eGWMHh30Ptunl2Lqvsx0fGdj1k3roQzqHs5X717bofjKhRewRhWyNyqAtlVXQt+o36GtMx9BX6yuSjn3LluW2uT4GDIvBMZ5GKIHEGZMIKrgaqQVVv+rI21dauqLiVr0jk+vyUsUATQH0KdxqX63PAQKOYdxfgZjzqm2wzD36IBMWBNYKjlfwVtAKqmY9ABnBGB4MIShu83UG3tMWUKJf5iDK6qTjZ60+qKT9As4iafouG1M1E10bUB5Q7tft1pJuG7jV12oZwGHrtiG0\/FYDJ4Sm3iBW2jV5WbtCscCQN8pmxa\/xbd1UMVmyvuNbH331514beKHvldTC25EPmWEFM8zWmuQs3V\/PbOyoiacTHawiqwa3Et0gXiZajIo0DfnqylJLciRKj1RGz5OqijZbV1R9wWksL2yAK7QqODSBc0GKtwlIDwcRSt6qvOMKIaPXApdImyRoM1zTzvAc1QTLBDJUZdvwRaMRWCM23vJNYaWvzl8GFZwm7eELgoJV+8GWSlRjdLJFXY2cZr6tbbz0nPRtxukLU6Z9NfxqQO8LsSa96pOhgxLAxwwI5PaLJMJFCGBbd4EmBlW7jNvUCAR3Wb+OAA0Cm7ZsCH7HeB6gEgQOpWNRMGlOoqcvXW13UJm19CZfb1Qs9Aq9FGjz62Bt51\/Tq+gSJfmUfpYPNgPQx7JOyMCa9xBDyCQ3Ym8VdtqCoQ+DrHXBOR9Yb6ySn29aGnNmSTEUdH1kfdgegAjNIlRrB9HX8TWGMF0AfczglnvW7lI0g8LBr5gHHFnndYvDwXuthxxT5VjVguBDaEolr+K0HVTgJ8DX0XdremOlrl2U\/K3X6kM6pg44o2yAMNiQyncowycDbJvJWajT1MyLfWXnNFdtVYzKmiLoa+iGIdGqamnfkhRjVaoSl2RS1cqfwxrGBY6jQoiuPDeclF\/EVkhz8C3IixRG9lvbfS3fCP8QVW7X0NfUi4OTFAk1n5\/dgZedC3QHWSUo5BqJkxq8m6iSCDVOAvtKP3Q01rAkBoNxZ0AJUD5jF6vRPnKAZ6wr5GLfTt7hOTxJOAjdYpsyFQybMEJonXuaK+8G94Cv4oBrMnpAI+xQg60tm1erqy1TbjdUua069YaCtCsNq76OLHWauKwvE8\/Ur6BVpWGvtNbRZ7gKvRg97b8ktCIyqLJ+PMzijAZx3oZBmiBNYoMKaGFshDcDnB3YmEA\/RhC0ReCQI7KsXxkO3NfvR3eaYXxnZZi4BwdFQycIDSMVwIKfNHfgEtr2axa+xmmG7Ccc1ofLkATQvB+XGVTVlWCFIjthCB4yd+FWO4hRzDqAIGcmccfwKxbVIWjwObOBGSi1sWUdhgxaViuMyYfBjPmTsMk03iaRVymdcsJt1FBhi2kO1+FrrDV4gdywFEzz36X5cVA090Bb4lQ5hIPfMB+FjsHxkkqAg+ZXoN3CMUKisXqIAmPW16HZIds4YBjAKKRq4yBg\/jQEbYLQSQLsNQah6ZBtP9726QYFho2\/InQzpIVojKjqTdIxCPBnxn8nHJlESUPxmTYB5o3PEsDCcX7I4lvnCPAFI0SYiiDzfoOsmRB82VChquIKx6s4BAWDIwwT1VhcJSH4LwZK0B0Mdx3EtdpvEmZER\/UNcoUgsCAB1o13EsBgWHxIbrqrHp814gTo+IZMQqqQ4DPGYUJt4zoENklgBg1BzfgdaGg+DBCyTYBXjd+H7mIOQWhQMwnwO8afQNWxKxRmkZDFwbLxKpQtDtgYBYB5AWpW3yrhYLRKAqaJQdAwZKiZqM431CEc\/JHxd5AxNcoyWTBpvo\/QFUrCbIrztwiA+XXfGlC1EPhd8yCh242QCqCiEHaDWIcG53cURWLWaRIJYiQOkJBBQMBaWwmrQ3VwTuFYcNmkiLqKyKSja2jHB5CAQZB+w4HgsoETjkIB22foEDxj\/BEECoEAWndY8D5DJWwFWizOmMEQNb7Y0FCn02mPga3GQQj8pF\/HTMUSVNSiiB3SB\/DBMmrjzAAQaN+KamnmV4wvo4ZgkqvMKb\/JojiuEz6yzX4O\/Ib1MaIzVIGGJS2voatrtsbW6GeJZnuBXlg\/h13V7JagGzru+NVgjedr1KpVM536oIwZBpTCNXzdNGW8MWCa5Hq\/EjxLGwypXHvZXBtcEyqDVUarYyaPk3VbM1QFtiiEaS1b6+V3tTkz3GLV0TY8g1vKtpeaL6tsDZrhFi1d8bXYVcP88taKXeGo2lAF6I5U7nQApmNGCGxdNDurGteMNDjAspTjk\/g1xuQaQjBAW0P+gMNdnKIttJ8gSAVfCZ9VZXhJ+U6g1bHsDqYNd\/xlwiEQhDUjKM5a9uXBBbIekfxVg0c5jgBkUBO2bfXhAk\/1a8SEGaTCwT7kbTjvg85OgkLuJneQEJ2UKDWAUbbQHqLTNk9FeODTCNzp+6vumq3vsrPILSh1M2B0zG\/Di4QZo8XI4Bjl0N+mG+ZZ0sQkozlQhlUfzrI0INeZDmcJJB3kbbJBEb5VP8FVuWHS6Oe2rVIRI0TtJAlCxUFozGfjq6TOEOjIuG8LyjSDBMNgH276Nd3m\/bTs74C7cIa237qy0wzhzaGr5BVSHUJg22kxCDlGIoNLrAP6le34dm1Y4hVEaqDDKon5OjuIERnKhlxRWDSoj4XvRreMTeqTba41hgy3mTpU\/Wb\/t3xXl9Z3dti6JvEtzMJXmxVBstVIlWroy1W8ARFKg+umLrOVP5U7qC5V7IaqonVzo7zMN02zzlXGGp2lgYsPdFjb14oYeM3WJKK6ITVeVFobobph8yZaM4kKY2KqVaaWZdt8qfGqtYLonISdqWlKlUKxNuEQCrmhr3Pgl9BjiAyIdaXDVQIoQxBBldQDsqryJHgQ\/QNEBcgS52KD2k4I\/gP67wFcJihLUCENPof+nGPbAuJjAGxozqC5pJs4+Cx6zAFVyBrUINCsbztNS6JeRSvNinOp0neepYi6H0U6AYqQCJtgKSkkEAzsJxk7sAWaTB09Dc46QaexBFqEbtZ1TWOuXVHLVaO52lRUKNt6W8VhiCApiwnZ21q4ZKwPdjY05\/L2hiavgaatG0iTUQYlvFytE5V70XEVL1dkBMhS+drL9Wb7FWyBasEVVsFWKUe9xL7A1q3Whb9dKl\/FGxx2VdswbOoVre67SmC4hZdNjVZ9XwdK0wRfFVasa0KdOku1dQ1RQpBcZFTMz\/n29TMURpOaosTgDrY5eE1QBFrZ2Rw4SzhWMKjdZeBrgXOjy6zFkVyNoRHKGKXXrfNYk7RohujYG5hFkJhu1ZkKssS\/bJ\/1dchVfhu3ikTJpnOFNmmcCtFvAduYAWzA6F\/dwg6AHRrF0RqNTkCZVe+yR9cZnUbAT1QxmVb2+HajKL4AbYHy3ye8fTCKm7swgWYH2J\/cINt+kyKHO3wbM7bWaYwAYaVfMg2oGCYOdoSGHbym1Snpnno\/NqJE8H5ldK0P+ixWw\/F7FL+SVJHVxRc3XlBUGoJJ5\/cM1fI1KLvZ5wev4KsYYJSApjsNxUGWAjpKqwhCUChUcQx1Aw88i\/8jpquEDjGE0jEc\/CW+AwdlnDa59wGBMMhrP72yVUPPXf76Z\/\/hs6\/90cVntcvkSvtrhM1TmFWuOjVirU1pS4Tc+o76TaVp\/W5zyW4gutAPEarMqojZ6FjXeJv0QZWh0CvsWcrUK2zlXivChwEgSA1nxzeodj\/NWWRzxGAo\/xCFIEG\/jGxj7uVVwBIWON49j3l3HLyM\/ymO12mk7SdBB+7BkbABjDpfB37TpsAZ\/O9xJ6gTliRDIHM1iiHAMv44bjigxak4lIIQfA3\/LG61aQIwuoWDL+OfwdEWLuggDr6IfYOgzmvPE42Bhb4vGjbRrH5hQPBhkMBtrKrIdpu+7FN9NQIlfaDGaxSNGRSkLXIUHaMshKfD8BdxinKUx+Rt5iMkJwnK6npr5colZsNaCmisttZutYwg2InXcCfa2O5Uy+eci7pc7ZhNwvGH2QXconm2g9xDEvioAN6Kc9BQcU2S7A6q+XTqEnY3F9IoK9hggALBFjwNiSWStfwyAkEe\/gfNNPiyGTFwAtwPP6mZKFdmTD3UwQnwBHyfpvF2FWsTJuinwH+Cv6y1LKxW54Rr4QbWp2yF4Dfhk5pmoorPkYUtgMEQpGNr6x1JDciYfPW8aVaFzoiCXOQ7AspruERZWJQXCAR1502OxYc5UvNt07dbmKS28BT4PLrioB3zIn0RrPfLGDNkBF\/YSfrVt+DGXTrHk4yPCCr8FgJr2p1rG9oiXSHA2DbiZ+5J4WPctk5TdyS6bK9tnLflPrBdpVqaLpllStKkyy1rSeWWz2xQ6xt2yKBbCAs09B3gWrBqqXVQY18Jt\/2Avs+IQJ4CX0BngbqzjRkLwqsBFAnrDAT\/DX0fcExW27L17AYOvog6QD\/j8ykjEKgoi5gdwiIYFeLgj1ERIC2mJY1A8AWUAOiqz9T6ZQICBnsbAGofKrGyAcHvYgcBUoMkMqC1CfA11EKoswJGEM3tbfMD4ElwDLPX109XtPoVfQm5vFZlNOayXkcXmDXhj9c1IMuSZmMVowNqaIvWh08Dace3gUS1t17zc+HFcMehJaZvGI\/YAYpC\/WzYJhgu\/DOMhKHmsEoajsFSQxxiDDdpbWd7BA6blEEYply\/ep+wLSQZw50hOKpiWsvEW2EY6QxZgx2sGZFRFtT0SdoxaUCoSFVdpspIoI8wsldBCFj6FUrto4adkE4pAME3QPu0bCAapTk1ai3S5m2LZgOI70FAOdOIMkax1s5guBZxMqxOkaTG7+AwXiUQ\/i6SBH4WgZR036tZZawTNjMwwArogKLVWN1ShY7e3qqSyIrhOFoZ05t4kzOhQnxNr1INswkvsGW1ja6U14IrWMuUzxqr2xdZhX72Q1qwhS5u1\/RdX\/Y9b58TjMHTQwu0hmzb+gKrDKGsEVnA12ra1WUgDa\/vkHCCAJw9j5oTJmNfxpqkAcGg\/QLqPE8vooL8Tgjusj+Hwgskpk6YMg5O2ixqoXxN9kPwsr2A2B1hledXByD4in0NMTGqgtMqRYDftEnUsoKrffVLccfAwQGbRB1jaHl1HAeP2oOoRrJ1k6HBn9vPIKYD9X6Jw3gDwavCAooDydJQjafP2cMMuGp9AGtTO74U+QskUItDA\/Dl8fb4e7o7BdC2djmw2HmmJVtN1UTKutkk20DdQNvKc\/wK2rJUqs5J1hnN0C4453QVXbubwvSIzK70raEUy4TUPgwfiPTT+NYXh9ohjA2zGCQbW9sNFZgQ3yIflT\/BjDOsMOr\/uWZU34IJ9YARsHasWZKCN61quXLaNFtrK53I+bEJ8E37c1hNr7fq1tVJw7IXlwckR\/\/WGkXUOrWKodeqWItSJf5q31W8TBJjFwWbrL3tC7QyJg1evI9y+mpDfQP2EMVLfcYO3Kf6iZD+QCWI3W2xme56qyWQKGR1nuQVmgx2SI0Fw81tNVWSVNYZaA4amhxoo0pN0mFrdU1yGj5ZNxcC6+zZgBokEbO6jJzjcGiwOL0OKpYeUOAV3uLa2jKu8o2INvKzbQr3qePMMEUcB08h\/54CZ4021qpdXVxfZVSV2CBtqin\/6eCqsip1MJUyyFeNOtaUGrSDveZr0F+lvoL8jf7FpfNw2ZIVwGg0wlD9KGGEXu3vF+5jyagRW4lK1gA+qfP0GPAFGDvov0cjdRsMMICs9Af9TligxgXW2SIEyatbebyf4VHGSrFEYIiMKH5spDlA9avbdFQ2FN43CUaN\/k5AHqk7HaXlU9BWs201t7U0k13tq9+3oVq7lEHN0mB7QHW0dd3sXEOv0TWwWmusbNGNJrNmloP\/Bvy185ITZqBE9GsRndYRi1dtOhQkrfs6rENgQ\/578bcwqDqM9VmUjOvhII7baDsAQ1UKhnMR1uyHZBBVgwoB3kGiDAuYfrL1oDxgbyECclQmy8r50XqnjVtLZEWt6xENV3Sj1kYNUyZlyyi3y6besbGKetX+p\/JlYZ4vYcrAGm5TJGoprznPmVJLtmRqDZRZDdXbG\/QqacFliPlVCpy3njX1FdYkIcJagonp1jvBnxhHHQnVGm1NajSFb8gtcgVUyA1p1aq3m0sNpVNpGivYK+HaNoUCRVAET3R\/u\/fx1iDsU+8GIZWHrCTAyBVecbbQA8GtkcGRB6yd\/WHGv\/0X8HfiDwR3ohlmCOxZTfvuI6JUkKVwv0Kr2+AWzrT6KyL1zh3g\/7b+OxZGGVW8t06Tw+WgFpDZVcJNXbq2il+O6IOrZFs1W1earUs1EmhNmSj7rUAncCHcAMJgZ5jbggbNiOAD1NqdnaJ3dore2Sn6v9dO0Tu74O7sgruzC+7OLrg7u+BubRfcnR1dd3Z03dnRdWdH1\/8\/d3S98R1Yd\/ZFGdadvVB39kLd2Qt1Zy\/Unb1Qd\/ZC3dkLdWcv1J29UHf2Qt3ZC3VnL9SdvVB39kLd2Qt1Zy\/Um7kX6s4+pjv7mP5X3sf0v9vens3dUUOCzzhfdxiB1yDG2AN+wo0M5985ECVCTJwBzyF7bGlAw1RdoRC8iVqKienlBPhLdBtNn1NXVcVAWhW0RZqwBTrmorKON7RvyhVUVWQdtDsNponopFnfQAzzMm+3WyjWlKVrtORXUdtSONNSI7rRCUk4gBIqNyXTMWRdJzXctHSi2YLgm+BjlLXSdyZYH9Bw8LjzGiUk7QApTIIXnLoVMNk2hXMKL9EbW67BZvMl+xXUIBBmmZEJSBvu5zmeUuuYRRKopbdJ3argLUjZTV8FYqaEmDRqr5EsZakN3nF0xeEVoKo6bO8EfwJ0VNrK9YcC\/rYfH1jkEL7DXqJrjEZuCBXrMqmTMGyzFbvJXsIsgWQa6P+AVR6Bbd6kaAoZ\/jbECZuN8NjYAGP5wGXnPzv4WkslruhVfENvYQqrjTK8MRAcIUIjfgGC30B22fi6\/Cq4TK\/dB6bAhxzB\/V8RNI+iep\/KckAPqVRbuzKyiJCETqDkhq\/GmEIVkqjGObBDAbyD45hGYJiK0bSO4dBCZQogsq9MLptNeFWpIJeiQLAP6vZAA7Uh5jM4Fge05LPulUIqqg7glHxvI6KGO3R15OJ7ywHjbohiLIECSucVx4k06WajiakMMjAAfhXRgfMi04YAaVFKhGfDjB0hWZuooiiuOBqpo9eYmqUFLhEWcdGuQZkAjyBvR\/A2bZE1n0NcdveCTKIyNBgNkYMYqIIPow5AwGD3qS23bQu3dOs5fw2c56q0RcmmRJtIgwPQx+CMTV5lZLhGN2gbtjgdBy+Zf6fpYbjDikAwYB01GIwxmPXWV1nQZ5HG5YXWqn2heRUsGKb\/nCU1akiF6LbpnCaqTlVt2Y1J8Az4JQwL0BAlFZGMgFE6jAR9Qfxu3xDmCw9M6Ayk\/LyOKJSDNpkyNAiEq0KI64w9vEisGwvIlYFn4OWtryEb9MJAP78jgNAsbZMwpPl4DkDweTxLKaq2Ebhk1knwDfyjlMAwAr2N65cSMoTgCr5KwWGORViKzoFPYf+VMm3LrzBV3ZTrchO32jLqvCTXNHKdtJabZe2F+msmadlgA5csSXPQttqmLCDTunCNwIXXrBZxiauwlv0SvehUcNNq4KZe55t+g2z5LN8F2jTrsGY2sNXQOeIK3WR1Evyd+WENvGK1G2X7+Ual3NwNCPASur5dHcIehow1LiErmibXNRnVyarZJuWWRCw6tY2OoKBtVtL1PkdWMVu30M52exckFdbpl3hFNzBFQIKqI\/P+nzAiCmVtbfiDPhhojpo61tHkDq75VaQV1R2z3bawSQzk7SQECHD6u0+tj8At+gAyKE9QO4ejMMS\/Vw+j\/ULCLzASgwU2SDJKBGnY12LVu0OABu93GqPIqG\/U3hoMOD46wvLEOEn4dhIf4QSsn0HsERM1g4Nd7SgdbxhrWIVs+zvWJfKy3ao39XWirFd8F8AKbfjOw\/PMOkRJDVQJBavTYMP+E5J36CuN1\/CWYVm4Yxg8AVbsj5HSEMSaAvCpPgL8O\/sZIpSkOdLy4bSOg9\/FTlMLjDOpbgmBP3Z+yvK3ORisBl4hTHhBb156Qb5iX2xdttuh15ANo4G81LjsrEfAs\/aXLcNkypyB4X5AhXycD2V0wfE1WJIEfRLnuNlcaNMrfBPpzP7Io\/TDq2fmioWZmdnZmeJMtzh7+4\/igSf2F2bmSrOzhdk355gpPjw7PTszNztbLLxZHGdOFqa954JbvP0sCzOF2WJhZnamWJxx+\/H2a+rympmfLZRm3qyunJt9qjQ7O1cozM2WSoXZ4pvBszg\/Xyw+XirMzc66sXL7FZ2bL87OFqcPPF18fLYwUyq8Cdade6o0N3\/qiYfnnpwpzc6U3pzenJ9\/6unSyULpwPx8ca70ZnjP\/Pypp06VSsW5wnzXmf6FdLAZybDU7cL54tyTc70UfLtZzpWKJ\/fPnpyZnS3Nzd6CWTchiOaePjXvJbn5uVLpX9ZiM+L2qZKbCoqFN2XI6jls6alT3QxQmL0ly26KlsW5YqFQnLu1zizObIaWszNuXMzP\/IhpwyZHyamnZmcL7lhyi0PdJrjsqfm52ULhloXfpCiZL\/bsdQsJb3NYFueevD5m\/YssS5vgP3NPl1wVb53S62VZ\/OEuOztXcid4t8V\/ioXCDw4lM8XCTGn2dvlP8QfsMjc\/\/\/qs9cb9x2N5a2ngx8k\/PwR9\/tTtZflD+n7+1PzrTOavi2XpRxp29na4bKlYKPwQly294UD7EaOGy3LmhwTmbWPZnb796ARe3GT\/mevmgjeeI18XhfnZ2ULhTWVZmp2bn33jR+F1qDj\/+n3ljfrPvNefbyrL+c2ZNb6Oztwkjq+HZekNI\/wYKa9YKLxZnTlzPRcUNiEVvB6Wm7U+d4spr7doVdyUNbOZW\/Ibl2XJTT6FTXDGW7bV3Pzs\/PymvJQWbrmz5zcnx77eWV6x8CazLM5szpLr68k\/M0\/O3H6W35PE5udKr3OJt\/iGWc7fommLmzh+lWZKmzCnep0s52ffHJbF1zc8\/Ys+OXPrUm\/WYtLrYDlfnLn9LIvfXeooFGbnntycdeXirTmCu1o\/W3oTWN40kpS8D1tv4uGym5u97f5zs1Zzmyn\/zC3lu7nNeO3a1EnU7fCfuf8ZLAtvriXcpY\/Cm+M\/m\/8W9D+N5S2ngjf5KGze1HRT3l6K+29HcP\/ISU\/pZGHmTcwXha5v3QaWpZl\/9s26cHtSaWn+lhyrOHOrry234HDz\/2LozLgMCz9S603x8bnealDBXeovFmaKs9PTt9StPy7LYuH6iFrofjUtzLiOVLido2HxZOEGc29XyuxTp+aLt5VnoTBXckfSx13dXPalufn54snpmduYlwozs0X3y3e3R71PN3Ol4nShB9ucD7M\/GHouy7n50lyp2FV0bn7enWG7a\/\/dmLktI3up91PsqjYzO\/eH7raj\/V2ms5voTfPfXR8p3QjcwmxxpjAzM98L0aK7veLk\/s3Sc643aBevs52\/kYjc3T8zpWJ+f3HGXSqafkM8v9cZSu5fca73PHcTVml+rjT\/2zOFmbn5U7NPPPSGhraZH5idFB8veUrP3QCWXOXcD6nFwuPz8\/Olkw\/v\/zETzT8DuP4Naq7k5oD5HvuufIXZx+dPzc8\/OT39YznRLQRY6eT+6+adm3eP2dLM3FNPP32q6PK8LROyp56cvr5a7LKcdVnOzs0\/\/aXS9Mn9m5kF3R1xPZZPu4FfnJ2dmyvNz82XSt3wmS\/9+pee+DFM+8\/NYt34K3xXzdL1Uum7H6nnSoWnntx\/cr\/74r8pY8mBueLsDfld9\/QK3jjqsX+qNHdq9uTDm7VJr3hg\/snvWmzODcNC8fte1p46Nf906WR+\/8zmDCtFd0PuzSPV\/HcNfaMv3I1zhcJmTXVPzlxfJJnpTfemp\/f\/wBvp3FOnZgvTm8Jyfr7Q8xHXUYszbvydnP4+2t25wpdmi\/n9mxKJp9wtht09xzMn95\/cXzwxvb\/wxPelN7er556enT2xKSxn54u\/fqrkbcc7OV14aH\/h5IHZktuZN30FeMIdSJ4qzT3xRln2vtW4KhQLsyenH9p\/stAdlt3B5XtYPukOJE89XXry4cLmZB538jNdPLnf68PCXLG7LWbuZuOf3D\/jOtD89BvzWW+W7ipZKBamC4X9009M51PTs3PF\/d+3fDL\/+VLXn+bnfm3\/zOt\/LZ7\/IS8GJwvTD88+kX9X0Q284uOl6e9v8\/Spk16a+K2HZ35c3bpuX7qeCk4+PD1TOll8+KnPl+bn\/\/DXn\/61me9\/hXja69i5J99AZN5IL8Xiyf35d+cPzM4+PdNNqqXH57\/fMedK80+enDnh+tAbmNO689PZovsJrFR86OEDT757tlB4\/HOF2dnS9P79s4XvnbLMFWeKH3n3yYeuqzjzY\/rpTHG2MNsdn+bmZz9fmp058a9PzZYef7r4+JNz30d1rrh\/\/omff\/r6Lp3Sj8XS3Z3iTsznS7OF+fm50ue\/8qXP\/+q\/np\/7w8e\/\/MW5UzPf52lzp\/Kf\/MMDTz91qvT\/tvLdcW1kWbogER1xtjHJ5JwEzjYgk5UjyW3P2zUSPbM76bVVovf17E5opKJne3eSUaln33TbJqgklBEgonEm5xyNcQ5gbBMUeK+KjO1Zz07\/+MM\/sFTn3u+ce8J3zq2N3OffJjI7BxEpFOYAzKOQXlr+Tf7nEkgCwnrBu1FYcvGH5X\/XUAKar\/Kyc4QAKAFFOZ\/mHQZhuOibwlwRKBJCsGBpRH9NQMstytWL\/p7OJySCRNBFPrpLESz66i6LD1fqYQEoAtHhQx5XsBjUVgw2twiEhH+PSDRb\/BIUZCFTseJyDggIIRjWwyIUOTGUA6xnUkEJlLeS2P6PZYrAnGwBgB4\/ERKPkXF5aRm6LbG4iIPkG6tTZN9LMxkSiQC+QMCFhDl8aAnA5TIMgqQ53HVHHlnhx3NBwIe2CQsBVKQoS3yRv2E5XyHbRL66mITxkZsRoo+mH3lZHxbJ53GFfAkgQH0KsMbvI7EKQEY7F0kKlCaBPtbDIIXh+0WKkaFqvlhYIfiNF3IMVj8HCkEJn4OkCFlL82zgx1fuSKoIZL4\/qorRSUpYJCv6424hwBdkro33eqEAYAM8NlK2cxHG5GMrAxCd0gA47Peii8w3gxAk1efv5gOAkMdirz5WWiQEeXwBl8fhCpBCRPLXGDZe7Fr0UL4hM47DTH3nYApBUCAEYUgsuYTjAkKpBMhkrxTnYr0EyuMKARHARtrziEjucj22cUJSwGesy90QcHmxLKYHe\/3n0MIRTSjFcB4+ggvCFeJLLPZK\/IVhJDfngEu8KZQHsLhCPsqMAMB6gwWEn61jHxACQsIDAEYEi71OpRDqS0BY9E0FnMPiCoVfweU53BW1g5BUIuLnZAmEQg6Hw+VD4GfxiDEC74xkgtB6kSIRCIklEATlZHI4HDRdXPbswKJIiVgi1i8m4xUSSLAchEFIDEvQqo\/P43DiAQgsYnEAHsDhboxciOtcKzJPCAj5EASJIQEf4HC5HM7yPS4QvQUEQpBYgoTnJTAlKyQEKBbDkmwIIUkBkAPkisR5PC6Hk7WU1q9hGUTQpbUic\/gI\/YcqTsBHQMlkcVDlLNERIHoTSbyS4EhEvGVoITEMixGdgILsHE52Hh8lTtA9rsVWkL3OMYGggC9BnQe0zEwILuBi45Frj0vRFkIH5dcenGV1ghCMLEfAAQVZIgFXyAX4kGQ5812tuYV8HrCOapDwxTC0bNwI7GIRAxcRx14p+qH1aYwIWqw+Fn8RIWeFl5WDlJ8QIOTxEe4ArRw4a+wVAATrMl5hrmR5E5lcRKa0\/CIO5xnLXqQ+kUi4LqmGwLxVzwdBElgsAnKykaUAuZeYi7EOYWv5y5oUbBg\/AyGUZ13SKwvR6jeN+pwLsTgcm\/9uKETABosyU1Y2LoYluXDexU\/RoiT70pk4VjxSmkDQ4tUHADEeQAgBa3UJCVbGtUFRJjMFhCBYWl8kZJ6J47\/LsqHtTcmX8au0AawHpWW8rDxuNpzzqT7nDCsCdSng4j0JxPcKBXxIsNYTgKu3fVAaTigWw7kV9d9ILuJS3kPsIZqF8uLWeOSKot\/WCwBeFggX8fXZPD6Hu2KuIGJU\/FzJBm5HgF6PXT6yeSw+BMEQLIX1RZkbnAiKFAgjp4cRv\/rn3PLPG\/IATpZQLMmBs8uFAFewKhIAQAiEoXWHZIlbWTZOUWaKaPEowvBGKuVC6lLCAUN\/il+3GlgkzMxMgKC83xZJYQEf5K2YDmJIQghcL1KymsEAfCGYk4KwvBIIcTMbko8v2UvGBEtyYtlrz41YAl8EWNkwnF2WLRaBYmTaDC3b+CJQhOZN4LoQtSqSx+WjUAhASAKBog3xT1aQsnROxJK8zMh1AQCG84Q5WUJYpEcDkGglTCGeR7C+wgWXGywoy4LcBuYBi1xvNrQB2K90CUvoi2EhL5azlBCh6oSEIkDA4UBC6SIpvDzHkyngLh7DdaFMBAqWyCOEQ+YBPE4WehHwnVsHuaWJy\/5HirC9XHAFH4R7yRHyM7MRanrtSj\/jctGUfn0iBiGXVhEeAEQOiQBAgs97L+bllqeufEVaLwQ4q2sHpeXgnz4V5mVLlglUNDHgi4uQf8H1w2eIPcGQOAcxZggSIQedx+HywfdUUGA5fyUNy9UXATzuGk9dzs9LuwSC0ErGLECv+CH3jIENd5ggEaLub\/TwYtYBCXl8HofLee9djtViAIYgWChYZXtBSA+Isv6UgtzSWHUcoAi5VChcT0Qv90DEFRXItRikpcXjA5lczn9fI4khEWe1GwOKEd8dwRcv2xwiG5QgsiARlwOsc3crAIsWJ2NzsgWZH2ixbChBhDyuEACylkUCQvCzlBV0EF\/MR86fQAi9c3EDpSBWERJkQ7wP8MYC7nomEaVBF78JiSVfZgsvea0pLkR8AHHfgsXC\/12KBzEv\/iJrzhfn8d+fdovW8TlivTCHu7wrASi+lCqEGKtPRX5Qr4Awf9mCtXQxGgxB0WLmgBiq6MNsGGcdslIJEvlRm8qVgGAhW5hbkLWaYkmEuRIYEiNXt\/M4AG9jlokmWwDa9hCJBR9qtwnXO10QEq7YRQUMSS5mC6GcVXVBwlwJ+ty8bEjAz3mHFoDQmVgQWuyIfqB6AdezVmJYtJqQwGL9n1JFoChrRV2gCMlgENyQ9lTO+6YI0BQDEkEC8IOFfOZ6bwTrL67EEzEkLoxDUt81lQfyUgXEwQhEWWtEgoshiy8ULYsH87gf6H5JRBfWV2dgxepnQUis\/8eEtZYJ5nEX+0eQSMwD89YTYcioALhCjwvfbQwse9YvN\/zlq6I1Lg+u+FNEVmbWCoPHh0A4jy9GNiLeMBW6Vndom4n7DhW77OZyNoosvJC46qxh\/cXsPP5ybbFolnloIJRCHx4oEXCFgtXWysZtvmvL0GerkVqUC19i8\/lCQdZS\/xjJQURIc1HwTYWk8EOuUwCAQgHnAx5WAL0jEi5am3jCeRciucLlT6GGA4rAPAD4l4qKhjX9AfT0C1FHgfhZxHWy+O9fD\/iOyK\/0a5YnkuolgnVRgy\/iQ3wRj\/MpXNG4VnmIqMVeK4RmuiD0gXO5qJcNItcRKFIkKwT4q7wjhLxVIYeZCoorVo8af6m1ix5KAS8zG\/rQtUcQes\/m89Y2VNCsGlqmDkVCsQSBkC+gJ67PCYRL9KIAOUQAL+vDhOr7iKSL63o4ImZE1trugxjhGSXgzxPf\/xiAx8mWiID\/LjhvWMUapyLiC2HhhfVkAwxJG+5++mXWhw4I73OJiP+3kf\/rDQqEoRz+WtePRDC44XPB+ykuJDEC\/kaBQv6G0AJLcrIX6981rrYR+lCKwYdyeCl\/Uwclk\/15wQZfAV9KBDeQ+WDF+0WiacpnKejs0sdtL4vDyWTnFm3IdCHRhXfLw\/c+UiTgw\/rcMiHI+0jiT5jH5fFF7HdnHKBLqR\/FAyMdZHFlZb8+9yP7Kch5hiDpOx8Gc8vAjSM0H3oCwk\/A+oqP7XEg5AuEvmRoY+JQnp33cSI\/EDn+ikz4AyNx4Ed2Lv8HjQbEk\/G438ujPmCg7zL0yHga93t45HtOD+oAeSs0\/NoTIRF8nyIFAEJfA1xAmMkRcpYYDoC\/Tnug5O\/p47Pe09kXcHgcDo8ryGSzuTxgibn+vqaHQNGF1Hc4fqSwRiI8Us8h8yAAb5FM+JgH8rIEbF7qX+sngPCliI0ikXIV7UKgxDVS5SIThR\/ZCUHf7wb81dMCSS7EcQQb7QZpNiMJJiIZSfuQkVHg4w7t0huI\/opECM7Dxa50I5brPqSoWKkMlyZuPlqX\/40fQ2j5HKQBwkfl8QDUuaN3TSG0L7GmNf19nXSE9EUGIgEOl4+MZK4EaoAPSvUQOpEPLLYjvjfXAiJvDuPyAcQuF7vsSycR4KP\/hTZbgO\/tdX9oKweEJeiL7gAeIhUhFXho54IHIJENHa5D526\/lxu2ixPoIDqCCvB4fESXwPJrdZAV8JERMD7aUxJ8P64AWLQYpP5ZHBAH0HIT7Rog5BrARzpACFkjEH5Pb6QT8AFeJjNFwOLy+OuzY1RxAr4QlOphiQglMP6eXZZEhDk4lndXVWInEvxiz5iyI\/KiW67LrIZiwqKYRvfqqIhEu\/3lndWVVg8TAs6cMdpFZB5rvllsMxITfpIx71bzGBfWSXrlpeu9odnan+RzUobjW+wpb6+ptHmcEIQ\/M1+AEzwhzvpXDN\/Q2HWdOXTyV7j\/Fd54R243FoM7Tp91qfkEVzZFnA+sGr2htemMcT0ZirOtIj1zUbXd1OwaIwefNIXfmA2qGbt5\/WWV7dOEkJiH4V8HtN1QbBqPjjhKe+tUsz\/8\/G6n8p6KCsx4gm98rFkTJtxyoKyrqsJqIsE\/NtaICwNiW+ukmAF8aDTT5FkdGnbbal9ZR3WF9cPEwDOxhp7QT0613JBZD8WEnWYa3KtvhNr3kaa9db031Fv6En1OKUPz2sivPEu6b6i3DST7nfpdqJtpT1lbTYXNk8RgfOz8hdCfHG6+VWw7EhN+gjHnVvMPoRUvSHMBlcM3NLad+EMnd4SWvCEaAqtHb2isx+N8Tg+FFIf0VmmxNQfHYnDH6DPlIc6+7uWDZfqFoQSPxHjLv4SccXMt6yvXW4wkeCXGW3BDfrbfuaxHr8eMJfgkxJt3hsgdDpZ1Veix9xP94uNMfwk22x0o66jSWz1IDIiNMzoEX7DcV9ZWrbd+lBh0Jm5eGPRpN3naq6Tnumprf5LvqcygB3N7ylpr9LZPEkPwcXOZQbtayFMe2q4bqu2DBP9TO4MqX5HmA6uGb6htrnsOxjYEenoeKusvK7MYTvBKSrC8EvgjJ9ey3vJyy9FEn8SEhU8CK3c7l3bryzH3Ev0SEszzAc5bD5Z2VpRj7ycGxMcb\/yXg99YHSturyq0fJAXFxhtG\/d3M+0pbq8ttHiWFnImf9\/e\/+9a5vLmm3PZJUhg+fs7e\/2ojZdJD03Fd6TBIDDyF9e99SZ4LqBy6rrLtPON+6rXfgdOtN2RWAzGhp5lGj+phv9+HeJUN6UpN\/QnuyYnYer9kt0OlvWWlFsOJ3kmJFhV+R4\/4lI\/oSo298QEx5AWan6mK+sxV0XJDuWuUEnoq2s9rh1Npp74Mey\/JPyHR1OYL2zuWdlSUWd1PCoxPND70Fgd4lg7odOb+RM\/kJMyQt4One2lfqc5iMMk7Ocmi09sL51M2pNOZehPcCUnYq97lMd3VUmw\/PiSKZfKqvuItP+5XPlKiM3bHG5K8En7uvXuPi66rXIcZS\/JPTDJHeR+8Tn3hpmy5rtg5Qgk5tdMb3uqk69CXYseTAxOSjLe8Riqoz1zkTdcVu8eoYafyvBg2jrq2ilLrieTg+CRDr0dwiHfpQEmJuS\/Rg5CMvelBc3PX9ZSWWAwl+SQnW\/ynx7P9brqushLLkWS\/pGQz32PLaf\/yUW2JoSvBlZhs\/c8e9i20KQ91e518+yAx6HS0xx93uOg6y0uwY8kBickmZw+rO7SX7qrW6\/IdQ+SQ0x3uxE1Ouna9zmo8OSgh2ah2n6qlPXdTNF+X7xqhhp0muu\/HOpa0VuisJ5JD4pMNAe4d5bSnLvLG6\/I9YzTcKekhg2G\/rrlKZ\/swOSw2aa7ejRHgresv0Zr7kryIBEypmzTct3RQqzX1JHoQCdhv3OROh0q6S7WWQ8l+yYQFgZvFcf+yYa3W2JV4iESwTnPL2uNa0lmmxYwkByQRzCfdqDGBZaMa7XxHgiuJYGN0Dd3mXNJersWOEYISCabnrhWN9EkPVWud3GGQFHz6a9e2m\/QXh5QtdcU7hymhp3\/jam97sKRVr7UeJ4QkEAx7XPur6c9c5U3Xi3eN0sJPv3KZXXAsaakssZkghMYT5uNdzLP7S5qqSmwfEsJjk+eKnH\/v46XrLdEs9Cd7E4mWV5xbg310\/VqNuTfJk0jE\/Np5+2G\/0kGtxtid5E4iWsU7J50KKB3WaAydiW5korWDs3mHa0lHmQY7SghMIpqUTvg2xpSnuq2uePsAKTiqyOnBJmdtW7nG6h4hOJFo\/MTp3+oZL92R9TsMkUOjvJzGsAe1LXqt9X1CaALR8PKg4jrjuZuiqa545wg1\/LTw4JTxQElzpdbmASE8jjjHPdhZyXjmUtxQJ9s9Ro843eHo+HZfSWOV1u4hARdLmP3C0SHAp6RPqzb3JnuTyJgfOR4O99MNaNSm7mRPEhlLdWx08tB26dSWgwR\/AmnhpGPGsQDdkEZt7ExyJ5OsdjiW7jmk7ShVY4aJgckkc+kBxTZXbVuZGjtKDE4imbIOiLsYr7w0bddk2\/qJwVFnDuhsnbWt5Wrre8SQRJLB5YBFC2PSQ9VyTbZ9kBwSVbmffYf50l3RXCfbMUwJi9Ltb7c4qG3Wa2zuE8MSSPPR+\/9yjfncTd5YJ9s1QsNFSfYN65lPXWT1dbI9Y4zIKPo+yvQ+bUOVxu4RMSKWOBu3L8vHW9ujVS30EXxJFMuofUCwb0mfRmXuSfYmUzDB+865eWq7SlQW\/QQ\/IsXCdd+Xkf66AbXK2JXsSaZYmfdmHHDXdupUloPEAAJ5YXbvPi3zsZP0Tp1s7zjzyGn13n\/c6aZpL1Vhh4lByWTTF3tLnnmUttbW9WjsHxMOnyHt5W921bSWqaxGiSFJZGPXHkYHc8pT3XpNum2AFBLVsudbK2dNc7nK+h4xLJFsOLpnwHRQ01ShtrlPDE8gz23ec+oW88UhReM16c5hKi4K3B08c0DbUKm2e0CMiCPN\/nL3tzWsZ67F9XXSXaP0yCjM7lZ\/X22vRmnuIfiQqZj5Xf0e3tpurdKij+hHolq83HUnzL+kX600dRG8yFTs4K65MtYTZ9mdOumee4wjUT27njh5ajpLlJYDxAAideH6LvHkPm19ldr+ETEyljQj3oXZ665p1ykxQ6QgAtUcu8tnu5umrVSJHSEFJ1NMD3e6P2HM+JV1X9NPaTY9JnbuzLBz0bSUKa3HSKFJFEP5zjtDzNc+2vZr0i19yUHRv9z5f3pYr7w0rdekW\/uJIdHEnd9ZOmmaypU290jhiZT5sJ3ObawpT1XzNen2QXJodP0OZT3rpbui6Rq8Y4gaHi3dYZw\/qGmsUNneJ+HiKXOvHX7y+oCmvlJl94AUGUee1TjE+PhoujXyhR6iH5lmqXZQVrGeusruXoN3jzEORxU7uAf7aXvVcnM3wYdCw\/xfhwtu3ppOrcKij+RPoln8xOHrA56ajhKF5QApkEhb8HGo2+mubtMpsEOkYALN5ONgfL5Pe7dKZf+IeDiWPGPh4FnKeuIsvV0H773HPBp1czt2i5u6pVRhNUIKTaYaf7pd\/ZA541faeU1qP04IiyFuZ1m7qJvLFNZjpLAkqiFi+++VrEcHJTfr4P332cejPLd\/PsCa9ta0IXgRgqOHtsV0sV55qVtq4W0DpNBo\/Ta92UndWK6wGSfhEqnz\/7pttpk96aFsugY7DFLCo3dsi5g9qGmoUNrdJ0XEU2ftt317h\/3ikLzhGrxjmBoRnbb1D\/5+mh51sbmb6EuhY+hbb00d0NytVNo\/IB2Oo8zQt9Z7+Gi6NMUWvSR\/Mt0iYev5sABtn6rY1En0ptCxYVttnL3UHVq5ZT8pkERfmNmSvNdD3VYixwyQg4l085Ut0HZ3datOjh0ihxBopl9s+fnTfZo71cpNj0hHYilvj26ZsXNTN5fKrUfIYck0w5PNjAnWW19dR6100zW\/oeSSzRyMi7qpTG4zRg5Pos3\/2+ZTfexpdP9b+4mh0f6b93WwpzxVTbXw9gFyePTBzY8NTuoGvcJ2nByRQJur2nSvkf3SXdFYK3EYouKif7jpV28c1fUVCrv75Mh46uzxTd\/eSnlxSF5fK9k5TIuMnrZ38vFTd6llC90kfwrD8oV9W5C\/pkclM3cSfakMzKj9D9181J0amUUvOYDMsGi2F0QEavqUMmMH0ZvKsNLYVx\/wUrdriy37yUEkxsJVe9xLBE+F\/QPSkTjqzI\/tnXd5qFpLirGD5BAiw7TDXrjlkKpFV2w1TA4l0I31duZHztrbKD5Hz1Df\/tkOY+Omaiotth4hhyfTDSw77Dj7jU9JWy28qTc5ONrB7usFF1VjWbHNGDkiiT7\/Y9vgOSd1vV5uO06OTKDPZtmaWlMmPZSNtZLtgxRcdKnNxCtH9d0Kuf0E+XA8bebXNmn+\/upuldTcRfKjMjFf2Og9fNWdaqlFDzmAwrT4mY0hNFDTo5SaOkg+VCb2nI2bs7eqXSO17CUHkZkLPjZf7\/VStWllmH5KMIlpfmKtfXZAfadSvukB+Wgc7W2PtY2Dh6qlRIYdpIQSmaafWv\/W\/pCqWSezGqaEExiGzdb\/9cC9pLGmplex+RHp2JlpK3esq6qxVGYzSsElM+ZVVtdH2W98tK21ks19hJDoLKt7\/SnTXurmWsnWfmJYzHmrBqOLqqG82HaMEpnEmNtsNdWZ8spT1Vgr2TZAxsWUYIVvnVR39cV245TDCfRZEXaqOWXSXdFQW+QwRI2IscPOevurOlXwQhc5gMqyxGB\/ExSg7lbC5g6SH5WFmcGcnXRU36kotp8gH4mnz7zF\/MTNV9Whhi16yIEUlkUj5vkBb1WbRmrZRwkmsxa+wvzvXZ6qVq0U208JJbFM4RibrR7K5hKp1SAljMg0emESnxxQ365C8Ymlv62zLLA5pGzSSa2HKTgC0xBjSbnvU9Jaq5uSb35EPnbmpGWqhauqoVRqM0qJSGbOmy1+N5zy2lvTXCvZguy\/0KK2N2XaS9VUW7R1gBQe852F07yLqr5chuCRyJw7aTHWnjrloWyoLdo+SImImVp4O43gIbMbpxxJYMxULEw3pr50l9+tLXIYph2O+fWCh3+AqkspMXeS\/WlszBcLMg8\/VYdKYtFNCaSyLX668OfQIHW3QmJqJ\/vS2NgfLBxz9lW2qyWWPZRgCnshfmH3kRB1r0JibCX50NlWQQsPnzuqblcUb5qgHI1nvA1caNnrrWzVwJg+SgiZbcYugKdCNf1yiaGF6EVn24yZf+zgqWzWwtgBahiJbfpXs+Mmd2VTCWw1SA0nsoxs8+TDA+pbVcWbH5CPxdLf2Jq7sIeUjTrYZpgaQWDN\/4fJ6V7KG29tS61k8zgpEp9tumxyVdaXSW1HqZFJrLnTpn8eTH3tpW6qKdrSTwyP6TMWd6e+8lQ11BRtGyRHxPQYfzXjorxbLrUbox5OZM4eMvKnnFR39FL7ccrRBObMtKHbO0DVqSxa6KQE0FIsnxlOBAWquhRF5g6yPy0FM274zM1P2a4qsuimBFFTLJoN3+KC1T2KImMb2ZeeYlVpOODoo2xVSyx7qSGUlAWRwep4qLpPXmRoIXnTU6y\/MNza5a1s0UiwfdRQcoop1fCbp46q25WyTROUY3HMtwTDP0aHIXjNNxO9GCk2foZfbPVUNmklVgPUcBLb+HC+Ih6nHSyWzDUSPBgptjXzZ2zdlY0lEushKo7INnwxD08g+Mk2P6Acj2W8iZo\/YHlI2YDiF0lgz7fNzYykvvbWNNUUbb4WMEySzlkYXJV3y2DbUerhJNbcz+ZS+lOnPVWNNYi94WKwc4bXzso75bDdGPVIImvWNFvYmTbloayvKdw2SImMSZt94Reo7FQUmjsoAbRUDGv2vzz8le3KIosuahAt1SJhdstLJ+VtBF\/q0QTW2\/jZmNBgVbei0NRG8aOnYo\/P0pz9lG2qIstuajA1dcFp9g+HQ1Q98kJjK9mXkWplP7t1n4+iRV2E6aWGUlLNgzP3Toap+4qLDM0kb0aqdd1Mm4OXollThO2jhpFTTF\/OBJ7BafqLi+aaiJ6MVNvPZyIeO6puVUoX8X2TOXN5k6eiUVtkNUDDkVKM029\/aeWuaCiR2AzRIogp89+8vTjurL65hB\/z9T+9\/an5kKK+VGI7TItMTpmzfjs6hNhXY03RlgEiLqb9zc9mEfwkdqPUI0ns2bw35N401N4KtyL25vlG8ArFz\/4e9Wgie6b0dYl3oLJDUbjQQQ2kpVkqXr8JDFJ2ygvN7ZQAehqm6HW2m7+iTVlo0UUNpqVZ\/O51JC5E1Y3gQfFjpFn9+vUZRz9Fq6rQspsWQk1buPBa\/cxJebsC3jROPRbPenv29WfHwlS9xYWGZrIvI8066fXu3T6KZnUhtpcWRkk17XgtjwpX9xUXzjeRvJlpNnPTU1u9FE2aQqt+Wjg51aiavhcXoRmQFc41ED2ZabZ\/nO6zRfAqtB6gRZBSDX+YfvPgIILn5vuU43GsNwen2yzdFfW6IpshWiQhdV79ijZ2SH23urJXuuUB5cSZ\/3jVbzikuFtaZDtMO5ycMhf0qmsgbdpT3VBTuGWQFBHzcurNG1fFHQQ\/2pGklFnVFKM77ZWH8m5N4bYhymH8ySmvSRflbb0EwS+BPWM\/VekXpOyQF5jbqYH0dIzN1H96BCraFQUWndRgWrrF28knISHKLnmBqZXiz0jHvpjMcvZXtCoLLLtoIbT0habJ\/YdDVd3FBcYWih8j3ap2MnGfr6JFVYDpoYVS08xfT545Ga7qLS4wNJF9mOnWX0yG7vCRN6kLsL20cEqaKXvyh0+clLcqJCje7LfHJj\/F49R9soL5RpI3M93Wa9Jjs5e8EcUTR04ztr4UJEZqBmQFs\/UInnblLz2tPeUNJQiekcS0+fyXt+8fVN2shDffpyJ4XntxagRBR\/NqEb\/kFz19adMeqnrUniLxB1646dOfOEmu1eTvHWefjsG++GraVXG7rMh+lHY0KWWm9vmFzvQpd8WdmoLtQ9QjeNbzS95BinZ5\/kI7LYieYUl8rg4MVnTK881t1EBGBibx+S\/cAhRtinyLTgQfi+DnLeGhyq7ifFMLxZ+RYeX9\/EeO\/vIWZb5lFy2Ulr7g8fx+c\/rkIfntmgKHEdpRvOHZk6Nhqp7ifEMzxY+ZYa1+ZhmFU\/XK8ucbEfxs8p+d2+Yjb1QXWPXRcZR048+fpd1Nf+Emu1lTsHOUcRzPerYzLkLdL8ufayB5sdJt4599auclb9AWWPfTI0jphs6nv8J4yOtLCmwG6QheiqfwvYOqm1WSLfepJ2LZr+VPb15Pf+YivVGTv2uMeRJPeQob3eV3dYW2Q\/TDhLS50SenhtJee6rrawq33KMcxXc98a\/OeOosqavJ3zPOPhWT8eRBT\/ord+XdmoJtQ5Qj+EePhWUZjw8W1dbk772fEhVz4\/Ev29OnDilu1xRsH6Yexf\/p8W\/9ghXt8qvmNloQIwPzx8e\/9giUtymuWnTQgukZFr98nBcSqugsvmpqpQYwM7C\/ePwL5wB5q+KqZSctlJaxkPb4cmSYskt21dhM8WdmWDk9lp4IV\/XIrhqaKL6sDGvHxzua0l+6Fd+qKdgxQj+O3\/M4d4evvEmVj+2hh1MyTPWP5DERql7Z1fkGsg8rw+b6o79s9pY3avKt+ugR5Azj2UeXbme8cJXdqMnfOcY4gdc8\/PPoQeWNJbxSXn\/1cHddxjNn+HpN\/u57rFP4Nw+SBxD7ultTsLUuaJRc8CC3MmPRvvaMs0\/j\/+GBRXf6K3fFneqCbcOUo\/i2iWzvYHmb\/MpCGy2Ycdby9sTPA0MUHcVXzK20QOZZTPUE3Jo+6Sa\/VY3YxzF88YRRh+BZU5O\/byI1OkYy8U\/hYYpO2RVTCzWAeRYLTHCOhiu7ZVcMTRQ\/1lnrixNFu\/2Km1RXsd30cOpZU9hExmmcqkd6db6R4ss6axM6cXubb3Gj+qpVDx1HyTDaTCQ1ZLx0ld2szt8xSj+BP3l\/5GbGcxfp9SV7eDH+c5OH\/G5Jvu0g\/Qgxfe4H4\/Uz7vI7pQV2Q\/Sjyemz58f\/uTbjqTNcV5O\/e5x1Gr9w7x\/60qfdlXeqC7YOk4\/gf3zvjT7jyYo94H9wz7sTsYdb1QXbR6jH8Jb3zvuFyNvR\/QcxP8E8H0sMCVV0IPujBTI\/wd4e0+3zL25WXsF00cNoZ803x8IjwxVdsivGZmoA6xMr+dhwc\/qkW\/HN6nyHUfpxPDh24ARO2S29Ymik+LE+sWaMxT50UdysKNw8Rjsen\/rm6ei\/30X1W43o9yQ+Z\/TZmLPyZmXRlnHqibjU17tHcTcynqP72YXod3IEa\/CQ39Gh+ydkzAaMtFdnPF3WbxTecuTLnvRXh5S3qwu2jfz\/88EaTmlPn3KT36wu2D5KO44PGf5R+ZL+9k6kRONDhs96h8jbii8vtNGDGecsPYcjA0Pl7bLL5hZaEPMcZvOwzjGwuFlx2bKDEUb\/ZGHTMPjEVXFLX7BplHY8Ie2NzfB2VP+XTc20QNY5bNfQwyM4RZf0sqGJ6s86Z31pyKUp46Vr8Y3q\/B1j9BP43w1VnYpQdksvzzdQ\/Nif2CQP4SaW8TgRn\/b6+NC\/n4lU9cKX5+opPuxPbG8NDt7OeOEivV6dv\/Me8xT+i8HpEWfljarCLfeoJ2JTp58O5M96FN\/RXbUbZBwlZMzeH\/gdeh6uVS\/qf75f25U+5Ybodxui38n+mKqMpwcltTX5e+6zo\/DUfpxfqLyt+DtzKz2YeQ4T0\/+H1oxJVwQvh1HaCbxr\/5N9Acj+MZ2McPo58+7+WxHhik7pd8YmWgDrnNXjvqelZx87FlbXXN03kRqD7+\/79+MR6P4bqf7sc9ZX+gRb\/IobVJetehgR1E+MX\/fRoiOVPfDl+XqKH\/ucDdCX1ZDx0kV2HcGHcRL\/TW\/krYznztK66vxd48zT+OKe+dqMZ05wbU3+7vusKLy8W1+B2O+y\/jjd8wFh8nbZd+YWejDrPCaxuyQsXN4h\/c7URAtkncf6dPc1Z0y6IPg7jNFP4vd3\/\/gIDl1\/Iy2Afd76Zddvt\/vLGpWXrboYEdRzxu4uz1MRim74u\/kGqj\/7vM2Vrtf2vrIG9WXrHkYk5Zzhz10XdWcfH0D3+yAVj\/9VVzf+sLIH\/m6unuLLPmdL66q6m\/HCWVaH6ItxCv9pp+hGxnMnRB+7EH0Q2s9F4OQd0m+NTbRA9nmr4PZf7wyQNSq\/w3YycbTzpl3ttgh+8LeGBnR9d9uKoyKVyHrqqX4p521+3EaKW5R3l+Kbct42rnn+cIS8E\/7W2EgLZP\/Ayrr59ycjke8j60\/5gY22yQd\/WNkNfzuHft\/2\/wE=<\/script><script type=\"text\/x-nexus\" class=\"_iub_cs_skip\" id=\"nxe-sf-engine\" data-nx-run=\"sf\">pX2Lc5u68vC\/kjJnfhfZMgHn0RSieNI0PU3bND1Ne3paLreDQRhqDBTkVxz+929WEi\/HaXvv15kakFav1b60WilqME88FqWJijZRoC6jxE+X2rdvyer2JdrklM3zpFy4+d5Xcu2yUHt\/tW+c6Dih5PjgqaHp+smJBdlTSjYlf7vmb1W1e+\/Vqpq9VuUvo4nmhW7OyhrwFVVd3odrarvOE0IWaeTv6XVpnsy7khI\/9eYzmjBtQtllTOH1+frKV12EC5LM49hi+XpTkHSUaqFbnDOWR+M5o6riu8wdJKtB5npTBY1aXfqUQJqaaoyu2EWaMJowZL6+vXmnZW5e0O0caKb0XOaFaoY2otmSd5IUluxz0QxvzYcnuu+ydCz7Spd7n6KEnZznubtWUy2myYSFyArSXAXgjOhWdlqlW1m\/jzaFnTkk5fi7SH16ztQMlQ9bTKnq4hQXHKlP3AqRtkBiRniPsM\/78MJl7t8RXaqZNp4HAc0RzontYEZ0axlGMVVZ\/\/iUZFX\/+EAi4sMEQP+NY5Vhls8pwlSkXonE\/lAmx93kQ5FssT455mQz4f14GacuOz4U2Ih6Q2RNbN0hqa07fdorrIltwJfh9ONeUSMpIIYVnEZWANih\/bqlE5UhK+589w3e5tCa2EFv2Kk56A37rdrLXMvmRahOatzmDW4\/cdwKNBQck9oY4ayLy6LGpU9cLeHDzB8O00eY7UyNdqT2jGFDHJToFj31LQrjhoSY0J5xbOU2lSPLGpzHAuM9V\/vhWUxAGF2IarIEjJgVzvUzd6Vq+hHmH15aqNBC7ytCrSnQreD0WExBZNOeMexzBANk3ciJGvcPIZ03sbAaQMA82wY9akD3JyUn2zVRgu4\/RSviyKOqjo2jgatNkWS+TWL6OHaZmeM4mZgM51EyMSOc08J0tZwWOPLNWvqFtZxxtcgvtGI+Lliuhj1Xm2Kotb8uy2b+37S4GSZJzI77gH0LolvFaZVuFYCd1C4cwkXKVcJUl\/PxOVMLhA+Oa1pLm7ZeSz7GmWjRJ27vK85J0fuKGVGLgYt6X3FE1GyQot5XS1AGn6oiSlS2P0S9zle\/nkZf5vApbUFFnTLwVUm0YS+hIseFLEEdUaIagjaKHzlTKUKo6f7XFqqGxvGxcXA8PDZ+jiHR+2g2j9X0P25b0hUIG8dPnz49Np7VuGqpsG7J9OzszDjCw+Hh8clweKQ\/Rf95kH2AD4bHx4cnz57pz6pRqpCjo\/3D4bPDZ8dPh8+OW1Pv1mK1UkwVh6SkUloj3UxxjZuiSTfMArtt7HxpYUf\/OVL6xLULZweBXHWEUTMPB2h\/iDPSzFbRzKkL3It9kvUynBO\/5\/d8OXg7oT017X1tkUaG9tWipxrawaE+PD7sH\/QGmn5iGPpxz+\/nPfVp7+TZAR0c95\/1NF0\/ePrsuOcjhPAAqspaBbvFZKF2EacZ1XllD7gaI4QolCoVyjfMhE8cmFcUL9M89k2QW3hqtkhB4skoy7KpJHapq9RqWPNs3el9xQV\/NeA1axgnBfzUOEhrucJMUY1sOXDj4rGmcdAkMxyJhilhva84JlHv66DAk6ZBinDQNEgRXjdfMeJ8Hbamd7ivGv2sN+n7vaC3Rqg1f2EvaKY6FhMR9lS\/NxlkHNgphTjNYeQjMXpTb41wRnPv0RGyLu1D\/1jvK3o43BZFRYMcxNSgEh9xOhHSg7lSjLy\/2j\/su1RleHByhE+OUO\/r\/hDVXV1QYiuZm9FcwUqUTMXvUMHKbM6or2Aln8dUPiDZ9TwFK1laKFhZunmiYCWhE14sSBWsFDCHSuwmvnxAIS91C6ZgZZzmPm9pkruMV8D4Z+xOoY0idH14xtEkhOzQjXmVBvxAPcUB\/BzCzxHkQ0YIGSFkhJARQoYHGR5vGTI8yPB4xjH0Qtf5r8F\/AWps8BSDpxg8ZchThjxlOFScxvb+syVcbhnoP9VF2ozbrPuD0b\/93r+10b\/9\/v4E3d\/bjiUESAMZJT5d3QSq4qVxmqsKIoToo+HRkWlUxPZuPhvTXAVj4\/5eR70C1ynGg5RhlZLaB05bKNYQB05bBISt\/tcmv5dTl1Fp9atKpCAr1QrKWka+m0fuIIx8n8Kkg2gQQGwdU82Piix210RJ0oQqlqu5WUYT\/yKMYh+4XCBhk0\/G5qYstwzxBe1Y4lWdHD9EySdj1Rie4L3qBynWFsjCzdXBIJgOlP6C2pnTV5BiCa0+oewinWVAzLdQRE2RKGUVWj4Z2xzeIX9S1UfSlmxnWEUFAf1wVUWo+TydJ76a27qD+greSjR2JQ5lYm4f8O5x7mMPu+ciq9CCNGGE8cdLdxbF6\/t7pXCTYlDQPAqa4TcAFQ6S1QASB7M0SZFiFRq87ERCp3KAKjLX41OX01m6oJ2piwRSNC4prELz3XxKVG1oDI97EZjE2lPjaNiLwPbV9KdDeB06aH94dHSqHVmF5sZZ6JJajlEcCxKcSGxT5\/7ehgmu\/zuSGXbgfbIL75NdeJ\/UeI97kwrzO9aRb1s8wYtn6VI1dGFtBHGa5motX120z9\/fvjN0BMtNdz+tTJzijByNjszijAxHQ9NAvZZB8fcDizPVfsxpvr6lMfUYiAINpm86mLnZoGDuhCqwYHQ1GmNGYF78uUflfDzGuZ6bLNxCgRVjpPF5T2CBrSpDX0FW9Ds87XeYNxIUEJNCG7sFxRNyTtVYy\/L0O2jWkKq5gFgTHYdEx0tS22hDLF0BPl1EHn0frWj8wWVRen9viEIrvsLHHtmsTB2vTR3fmUaJr0TyrXi8Izq+E6+XZFPib4QbJhfCKVEjeKomeF4r0YkW8O9mAj6KfK72+PrivcvC4YtaGI2Jbo1Pk0oWjav13zlJ7LGDb2RzEvoz0a3Pp+cV9Oc+GQrwt2SqntufHXxuf+4bDrLAUfAZbRYasNXHVH1r6w5+C+Ra0rige1Gg3nCbbKJx02AkbMtxwSEHN0DtZ8f0QJgMj1e0WWhxlGxllDfkLZhqMHDNi9OCwqhVVFYm74JLor9JrKX4LxJrP\/i8vCEbUN7mRzWF2YZ3\/Df+C0m\/A1foZqzx50gC8Y8WFHfm4CD15oUZa\/xZgfKPB6BgBgAof47qpqf0IWgeLWgOsOKlAhZfAlpgS3iU+JheCRL6IuaxwTfavGqTw5f2B5T7gF9iQSUfyOCZbn04JfDLJ\/wFmaofsHGi82n+QAhAoM2raopewEy8aKboVTVFrYxSVA11nokGBk3VA173rmKv2tPJu\/eSDIwj3Xp5SvijTw50tJH9PuH9PuH9Pqwqf9n0+kRHmy+P9frLrl7Lbg+Oec3HvOaqQejJiejJCe\/JVpMvoUnjf2izVFsrApichMRakLsziufgWlvAzxifc3wIhiYngpUh5Zzo1jmknFfMfUOmagJqS03sA2cAr6g33j\/BCag1NbGHkKg7qHe+f4KsuXBfcZ60FtUH79uK2JXgAwEar1VObnOEdyUvqmR3tRO6m7xATolUITI\/bYm9jLZQkdM4ogEXOQnaSNCWw3kObrUE3GqLX6oQa6EtI5+FJNGW1kILKZjlJNFCS0jLxQP9gs\/JWNZ2NXMnFJx2aqItcaKFCN+QgBsR3MzHn+UXN\/ZrqfqW6Nbb00Rb9qCdt9UsFZTM7bdnZ4Zzdqa+\/T9jpJuH6P+MI8woUQs6OEH7T\/GfhNFTfXRjfsaXlGw7UUCeMopQT+VQhqmdoN6f9oFjnWvgv7bf9g4d8qettxPAgfanbXSShpA07CQdOKRldFzS3vDoqKcm2sSNElB1qBxr2Zw1ODnHOtaR9YlsPHOBV7qZaCsdr+G51vHKgG8Dr+G5NsoSpriZ8Fk93+t9dQUEugJyxHMSwvcBfIPe6XhSooRrv56n3TUa8bq9rK5Tn7f15Eytl8BqMvC0Feot+uv9IVbnA09bw1e4P2wtL14\/Xnq9P0T7i76nraB0WH2tW6X\/qcc2U2FE6\/3hfoIXJISnpDsYbX+Ozwkf+xzfEBhvf4E\/Ez76heVpKzI+Ox+pHHQlTFHTpSo0PcbnyPK0Nbk5+wwQBkAcNBBrfIM\/t8yGWcNf3OX9HCY5SiYXcUQT9oF6TEXWunHpSnoTtJAIFkLICh+FEIyFwDoT\/NbKXfeWkC55r5UR8oxAfXIlOjcnLVJokwFekPV+iMdN8\/P9BV7sz5F1BVZXB0d4bXYwgu8AJzUFjXGmhemMXrur+\/vaXDG0A9NACBv4EJXQJwF0zirzaVqngCiv34FGr7QVOQeeu9LW5Nw2nBLm7kpb8Rm60taWp92RK+2u\/EdFXIL9IJv3jUOkRWvC\/Kuo\/rm6gMYWXDbj1dqc4sLLzec4i92Emq9x5sY7vEtBie92JAPX7PRGzVRU4uWOjHWJwx3JYYk9ttqRQUs8nb1v5STtNvYn2lRNUMnd7YX5EWdpvN6Jhd+xaytL9ZzrvrGDE3tcW6rjloEJM4PPdxiYrQxuXD4hpDZO20ZmbWMKtOvmA+0NEoLCquRj7iZFkOYzNektQTxieKrr\/SFInV6C4CvkX2v+VU+1sZ+UMLmUJp0GtqoVlfJfVOLYHdO4g0G8wGPAuVDqG2YmeGXO8dpc4NQc399vyhKVOEwXNN8xfxelMDJzSuyWs2gOwuOHJnvHpaFWpDlrzJhLir\/Tqhr1O9VSLcsjcOkM1MvWVym0f0Jsez04GmIdr7ExPHScep7nRLfmp3ntTJlX2nNBcmrPHQwqO+U6uoju6P29MbSED4GoYy1ibhx5I0U89xRTUVBfHWtLLnru7w91HfWVPaV\/3ley1Z4CeeA2GAX8YQa8JtHJG0K1GXWLeU4\/gnGw0BgSsg1\/JmPNjaNJwh3HNGDKSDdbSTn3\/Y0GN+bgZn9oCZvAXmir\/ufBAV5o68F5TzvBPKF\/0+dJ\/fOednDkAPm+tYfOqY42XpqwKJnTkqfpztl6O+1gB5zhnIWtNGl5cMquscxgf5DRhp9YvVH4J0lsRmU\/dOcUzAS+sINenYFpIb8MyDuovg4gz3DQpqB8YWuNc+pOwdiFXgFTcb7UgjT3aKt7ZSJI9S1wEBhh5xyJEpn394pHE\/CvytznbkGBfYnCnTFjyiJPgb6OtZgyRnPgGPF2m7lelExIlSO4fztX0bOVUlIuE16nUUIUrpqgPUj6zHXZWAMvbuOWPDBFCjA9y9Mp5Q4pEojEKFBbhc90zsQcrKIjmHmYc1RSLYjiWBQfC5\/eKLDlm3N\/L9\/MQAOPtiXAd1WzY1QWZ+PGBgiAjd8RnUvIb3UIRxSot5Ug04wTsDv6RL3VVsJQSkCJ8YS1sJUg4Y4n3A087Q4SIDSjMk+r5FNN14ciaqPJknXOVHSqHW1nyvqrTNCht1KH3kodeqvdWcKfUpZcmbalEtW8mLo5N2Z0LlpCwclz8kMTolvl6uEVzEiD90ArqCtRq77iktxdUFlhlMmkzjyD679DIfMaRP0C4DktWJpTVeqcbjd3N93td9PjDo1ULoiAezOGpnhWnX8jfBwKXdAk9bmvLFDfiCLbY+6WE76MpiBQxSdOuB1UPGyAahEsCG5nacrCKJlcJu44pr6QAVTzc3fJVwzqJ83Dn2CB8AlWB5+0lTGoPuFtrXfRxi2weeIDR8sXleIfOOighg+PO1ceDm9K22gB\/0t3eG+ko6Vhz6os39HpTm\/PsHbKiEmcjt34nDuGtZOjhgrq2reAjHK7NbGftNWc9rRdFSBdziX3Tz3sMk\/eruOkXYcoKPAKswIavWYKkSQRXAoWaWVvt8b30R6j\/1eyEbAxumaDSKtaAavC+i448hYEU05\/zGnBzpNoBu7V5CV4RNSANh7GTHOTaMblhnhV0c8Liv3BPx51UheeG4ODekLJH6M\/toD+ZfOQsGA6SOeMKALW+Zd03o13FYE9J7Pr2fguwuee\/NEWuLXT7g9NOK4Fv7R8HAl5DWYjhgUmnlf+Ie7jqd4PnEGTjMDOAO0kFpgL8kMDU1ydIzwmb6lqGPr+AnwbreXXuLdAVt0DYSFA5yjaTGg7pI2o4zNijN6rSAuiFfXVMdaR2f40ENhV05kCoxtTtBlTub0jVoPc3lJasRIRlXh5xx\/ffjWPdUFKYVVxC6u+RFvhNXgXxAov0e6wgTNYHn5N09n9\/YlYybFf6RILNAl0qGnFo8KaFopxTF6r8GGJBQdfYd\/1ku3W8A2sBPYh79yiVN2szDE4v9W58Brc4LU5tg1noC6E4+AG35nnZatZnw9O6KzH1+nV3jnIZJqwfwZzDYxQXCV8Gcw1lmYt1f+RdrYVMi2LvOlIPPhAf0gfc13ilnYWZKNE86NZXzGVfqJFvqko0o9xMbqocy5kzgXhJsHiCSFjPrsJ2rgaX3SovB4MdWBl5maKVJB1NrAW\/5G5kVQGCa9oDhWxKDtn6hwn8IbKuoLIpx+jTEWlX22szvMizUkyUrI04lakqSjNEL9xZF\/aiSazr3yH8BngMaI+rLzei5wLN2PzHEisBkUywvMcbaRT8mb8nXpMm9J1oV5ysTaX5jUhxECbO7LJTF4\/9tyZuVmZHidgT1uXGBarMp5CrEabosNqFi7tua07sASCN8NxLKgySrzQ5FwdrrOUcZfBYFz5DQZj7j+5Mz3tDs8i37Q5QH\/MXSxDrAJQfywkiCO7AcKozXMf2mTJERQFahdxDxA5h1HcVV1\/iJs7jfecT+viwWBhiAs+2HP+Zjiwd9UaJWcsvoLnHMWX8ZbY0nqt3mmzyIc88QJZwPEuVe+0u97NfuPE0nHVj21mlqs2WNqD9PjMGbmqWPDzWy5PPnOOrloSjP0WREolXTgJt0Q79wPrzuBOy6CPDD4N8Wk4HVu6oDIiT\/p9zw7Q5o77NaSBtR3ewBWWn7uTeiOUz4IoUq3vqiHdaZ4701aDgu7\/yUciEtYDBgmtAYAsmMcx4iOplBRnymrCP64zCgtfls69sA7GKm+p+pGqMFg852SYtGTdi22iwgtyN5J9FZxg+TSmjO51SQtQ1KWnhnx0YDPe6VSGAOzEDkfLk1q6dzrJV5MgZwqu1dUxl1jjRmJZPxt5JZ4SPG6JJ2G8eHHkTS9nGVuDTdt8qR0F967SjHdoU2G+lX1DpSx8oiaax\/L4DV3f3yfajDL3DV2jGvmJluVg8LIXNHDnMVOrxZDgYNBwmk9j5n6BnQTt4Mg09uGBG1S03OkPJAAvX4RRwN7Q9QiKnpiGdrKz9MtW6QQmDS\/Isb4\/E8bfHPDXV+7v+QtR0MajqqEd4cr+qVHIAQYV5DcJuf84rF7BvkpnECRIqXq1BXKe5+nyLfhweLaQyoOFkMtCbpa7inzgPp5Wmf6vy3zK2gUE+GDxkwIv0mXysEi\/W+TxCS99zfX9S0h7GxWMJjRXK13oQ9X4G+WhEo8BAQMp+MPPgeaZgl\/8HMRzE4\/GCm47hnfz9u9x708bi6kLvX73SJeWIYWu3FC8ydyiiBZUqt3d4P445pyq4OePVDila4HMl1Sw2JJuLzjO47i1nHA99h+i3KXpbADriWat8KPDJ948z2nCPrr5hPLzLNvIEFUpDRPxKqOk4qDjR7iCg6Vz1vDPFqTkk7JyF96Bu\/COni5rt+wd9xcuqX1HnR0okRj7QcV2x+edCFFqhMzcbB45ShMcfwENXtDTz3WDF7zBz9S+oE5nxcIbWFEig3Q+UHAL34wLmi9oPoKdhG5Se+8ddsWEepartihQVxRtVlRLBbjqo5JD7dS0cQSEVoUcBY123Hw0f+CcwqLajCjOKQvpjLY97lXQUVZ3AQfx+mO65c2vNJTcF2oWFXIpge\/Mxf19bc+AkTfUjhA4+YOIPU9Xj+0yQVkeEoB55fYBfA6daolTWx3czlpwwJt2qgGpjc1V7661N\/Bae4XnPXV+f29ohwjh1r5eG+SmAakG2jVV12bHWMV3Zqv0ZyQ2NrpYFnSMs2TS2ZMC8z7SWPo8TscNPczRBn7KyrLn31L9YogNTYtO5d+aJfM7tBEibmvt+g5Z74heNmTlR4WXJglfx5W+FHK\/FtA\/g2vL6J\/BNWL6Z1Ad4bkTsJafj0Jsi8ydQB2pWfF9QnQraYmZREqZxNldSUvORIEawXE8mrB3qQ\/7Da2vTjhohMrH1Atn6MpOaqtcqXCr3bp5iaWrskNXYp0KtnUinQ2JdDYk2h23qDmnt629f+ozIyKU+HfCKf1fB07mwn3QDWxhIHMgEhMOkOmYEh3HRMcTsZYQZ7V+M+zR\/50YzKwTg+m3FN1abp1ekuxRL0f0SKzBZR2NQB+FqKMR\/IfRCFEvgPSH0Qi0F7Rs1rDuYqG5RUY9hr+RCF+QaP8SWP7ijKLNBaHWN0J7l3J9BzIrGnyT0ooOLuBtaX7DoXnRmvSlijax3IiZ1AZ73t3qDfhWbyCCbPKtvY0IU4ix5l7bHIcqwqzV95VcQcTQzG6v2rK9pvDUSzHYbz+ZD+l1uqy9Tt+E1+my9jp92\/I6XTXVQgv4ghTC8SQe6jfQY99AE4UqajTwReMt4r4lfNFae1Xrq0t8IdZXP3UfWW23UMu5paLfK9Isyv4\/hiLXkQ\/GUpbZryzvK85EvzB0b38KBGL\/nRBpd\/+TkbRWkbXqmEh34IWoDKQMlevfs4+WjXkkTaPVLsuokVK82a7yvhTK23+gvL+hzaX6rVHe\/PsnynvSKO\/4EeUdo7IabEdtZ7+hjvnE\/Yaevf0VnJy+KFD9jnbzH9Nu\/i+1m9Rh3N\/7UI2hTVs9vd9ST90wAp\/ocNpUauvB0PJPqw8rJz726ygdRgrbd3AEj77hYEoKO3dwDI++8HpFZ+kTQuKzVBztO1XpgKGemg4itK\/Ggwj1Gdpk5ElW1mHfWdPRvzonHV0tkUeuO2eki+pQ+3Zq3jqqWyAsQjnhhDs7LSxWhUZEJIWDvC6zmYNdLU4mNnOQldnMIXCAxPLFm+HIk7Z2E9cC4j4+PbbiqrIJrwxCoWwGJ53j3hAq7STweCYZywPnRTA\/H1Lm0AxtVE6G16aPQ7oyc5y5rBWyFYi21iS3AweH7cCqsIqPWvPVBLflq84uydBanhpDa8knMKwiptb2EkCX0K8y7IRuN6FhQZS0Tk8HfDt7I2odGHhFwl5YN+QR3fJOC8ursHJFMttzBgG+JT68rPE7ctW76t\/2brmdfbri1PGe8opz23NQE99Wrsg7a0m8mj6WcgMwpmTzYvjB\/Io\/XJoJxeM1o4W5ptin3gcejJby9wsax4X5iWI\/mkSsMN9QPJ2ZrynOk4n5lWIvdmeZ6VJczGfmF4pn7pS+z9Pv5jnF9MfcjS\/dnIVw9BRquoIzcuZfFGfsKnkPsW7vKU4ij76ZmW9paW1HgbvEthW+GRyvFazcyDcH24ofFU3Gi+YD8sKUhem8AEH7KmV7\/H2Pby8qjiDElNhD\/UDHQ\/0Qfo706jzfcSsAW81x6xxqri1t5vBiwKX15xEQIVGjAeSg\/WF1MNSmPNI8htUi5ZHmEyx3K2GpyHcoe5OWbeBDe\/XUwQlckEcj28CGY4IllBQ2c8optcHIn7mMDgqW06JQHLKZpX477o3XJBiUadU0qrnmwRPnWioOEiU4hnw+tWquUQSWb\/MdwEmg1ncIp2xb3wzhEALzl5x6l3D9xSm1lkC5oWDR+uR+rnlRdU\/AsjfEQ36AX\/hCVg8FEJxFkUe8O9Wu7KXTOckV20tn\/xBZXp9AXrm7BPz0DHqw77VNsVv8Dt\/hxs5Tc3yL34ElA1NxifCUbOBslKljOCvmmzoOqctMHTMwSU0dAzrNXZ0fry8epueaV0WXIUxX2a9AWJqZenOo8iPRrY+n1PpYSYa\/yR0\/xOaPJvZHZ\/+ZqeO\/yJ0GfRwFVcobcqfx7o7WMkkemgH7rHcBP3\/3wN31pZXyV8\/QhvgDAfOtdwE\/b3qGdsRLviQr+6PTK\/YNemBN+eGxPnlpTUVX+uRl75U15Z2A9y\/WVDQPHx+sKadA+6NDXvW\/9OF7vL6wQ\/uj4\/RFvTWENdUAR+1MkHd1\/hkxNANtpmBhi+yyhA\/mxkR2q+qT7I7sSSWYp5W6KMycz2RhRjgxKb40V9iLzBDn88S8AmOJ5twnupO7Co9CUF5bHlEiz+gyLUzz+3sQEoizWuyuaQ4xp3hCNoKoYtHJKtRWkFjMe1ynCXqLRf+r1FKuT7kNKnSam0\/4IriAgwxRoK65aliDvd28gdENPs+CL6cVcLzJ7orsyIf1eA7OVrIpPDPC4Z1JcZqYEyzKmAHOzVzL54kaYYonELtS4kVEl4W5mbksj1pOtrwyd2xHrLGxOGwn7mOpr23gzkwmpIYtFa+wEOr7fFqGgmjctakDqjq1YwfzDmtpIl9ETxG\/vEWaC4I4BhNOHJ3VdIS3Mmt9CREdMBXqJnFn1FTE6BQcL03DOMHe0jw+xF5oHhxzdh0e4jxdwp0pMzdT20aHpLWpGUCPZZgyBMaXcOwuLsz08SLKWukHsow8cA4YR7igsSmH6\/UVc630xVd4h1nEYmoqn7I4CthelOzRFazYqb8Xp0WxN17vAeG6eZTuBfy48J6b+Hthmkd3aaIIydY2WQTmQ8LswLHXTn0ctPE0gIvkCBv91uHaQ+3Zs2e9cD9C9Y0LG89UQqW\/xOCFaQXMhNhAeErXpsKbVvouNKQ7fWWg9FNoMUqm5vKMHI7kRQIi6IhFmclBDaev4D0B21fuHxn4vdJvtzmEGJ15ErFCKXkcuCCbYgf55lqBIyBi7iTCkxaFMkltRYdSoy4xT4huTRp6n1TEHJBMZdi1J5yUq\/kDTQuU0mNC8cccF0AwrRRYJdixJO91x\/tD8fpxIpa9FVSsHzZUPDzpUHEzrg5phnjZJs4CZlMQZ1hT8xYDhK0CYcMAoW2AGzOLXY+qijTRsKJw4pYUfMu7sAdjjli0iNh6J\/GyPRbSPS9MC5o8Rsa848LiiOzQAdPd+30yXu3TLhkXSt\/bJuMVWDacjKWoVPohp1xvJ+XWGLZDSb6uvRS0fH\/bjLhDtStOtffvw3UReW6M97hWiGCEHThJJyGnKygC1e\/KNiBbkD+P8wzcuGtKto1gIM\/cahFWXaSirjpBwUtQHubwQMeBOcTuKire8nlXLts8iSUHVjP+Mk9ne2MZ1r7H0j1h5lK\/y8oKLhjNCtMGIhwrkqSUKh5ewQuTirPtU1h\/KfCqYM8UN6GUGIoFdbEPEJu6x1WxKMlfZVFwOXgm3D8iioV1sVcUbjwBeGCwLfCqFVaDf6ynShTiU7dV6liWKupSt3L8sgwoKlmGv1eDUkoH5tBL5wnLI7pbiPH5k4KspYdzrWhMT6GMIU5QmGDUOTNqebaJTIoXJuOWG3X2axhMzfq9hANEWydiwEyoz2Npi8FEW5TVJQSRbHkETgNtUV+ZwimMQfx0RV316GrsXFQpIBfoKkvhfMpAnG+hviQeBcTSfJZwcgH2TLbKrznWIYc2pJSmUwUn85kI\/5L5i615eQjREKOCx24u7lsCZMhtdUeI16i5jG2IuvKyhStRpcfytan0J1okTdVNYm6YKabNnmiRg3M3mZpB3ygxhayG0ycaxTrCxcKcaLTEi+3cBcgsnrso8djcQJcn2mI\/BrqC23e8YrFV4IBv2YBvscTTLPo5qTFpWzHBjRSuQOF2ORNME4MXZEIGxtYFeV2aDLo0GThnh5UrR9BiUNNiIIzfswk4NddWTILaQSZnH7brtphrW7gs3HhOzba8FIwHwcdJxEwlo\/megXVd30uDvTEnFR48ZG64TlD6bWkbCVMjTWrBpmA\/ygXXT2AdIAMdJQkVWRw1YqOS9XtF6OYU2gNlN+cmzsOeRmf6iO5Hpt70datEkjLaFkYdxQDFmZBMspKqUyzNOrwB1hVUG0aTkBZMstremN8StQt\/GdBh3SOOvKoznKr3lhELq2oAQjP20nxvlua0Zk\/ghLoX12ndLPX3PMnJsu34jOgjySGxYyrv4A4j0RzPUmRD7cFP8BBBSKxT4nTOdtG1CycoWHtDoL67aw+kFyGkMslLZOvcSpOcDJscg3ShmEwo48rY66vtNcsT6fiRIEVtIrSBHFPBe24c71W2HHBimohN0gfKm+8Ey2UfIFqRSZEvG6v0u1xdQA7SOA2qykBBFhgLsbwsVlxgcO3m00IVOUxVwjRXqruruGururJUZIO5pmDuBivLEs\/cbOdqmttFtH27wUbGn5pPMd8fqUvFfNnJ9\/RggCSmWu1hrB1dE23FzeKW13lNdGt9mmuJteYXoAqlBjvjDGxoJNyjIZnUZz3wspZlK8IRMWLtdTQnyFG94pdLaHNgmAMDewR8AGzEfx8pATl1ibqjV0S3rnhHr6qlwi1ZCu\/HlYPfkVvwgByOjkz+YowO4UU7GR3w59FoyJ\/DkSFdPndkJRjCi+wruDN4JUNK485xnLuRNjQlECHE41cqWHHrKNG7UWDDGu6dYwbiBiiV3+OmPUUSTqX2lQMf3UMyElbeS6cdHAFIc2Im7GlHdRlRRRlvHRXC3XO6kgqkR18rNC4XmstuQ6Jb4em60iFhhcklmWjv1bW0fvkTIn0morzI0B28FD8VCL+HjV+FN9qI87RCtYsDidW1d7BraBpDnOWRaZTmRnxDRKawScwjXa+K8Kv5APC45DtarWa8iK0VtIm1MZ1EidxTiDU399S6W\/1DDCeG5Y2AcANw3DnyFSXTakY438HG7GMs1N4DE\/gMBAsAHDh6Iw32MZrNESxvxwlP9Yell1tLhY0fzSrZHfmCvkKHr4E2wuyXglrmSBPJthXg6tqIawS15IQQ1i7g86\/0ERdvLbhcuwQgo1noA\/RLsD\/2\/r16\/nQPTBDxxlrGeVMBvwVPOrwLLUB26Ow\/E\/XxUm3t0YUN\/wtYVsM6jtSIFxDJw5c\/XKiL9W1lq4p\/qH1\/yB9bG5PurgDJIlDQ\/b2iYJ+8oqqSrOhgEg+KYKD0M9j0m8KteDwKwxcb9TtqgKgbBpsrsMgb87vBreYg15P8fy3YvfL8f6lFaOitq9OXUV5hRl6ylmt8u0T1cSzjU4XOwULF4WonXJii7cD6XONeTvDgoi2VElRChuuVxjqYgVsTSqmzJtj3uuYYWaM9c9QIM42PDV9D7AoqkRrYa2crAoejWAOju3Pl\/IyIxFYtrOniNdGt69NZ1cXrSg4+J+6jR\/ygX0SZZpH5r\/7MvnYAA\/1\/8fDcKFCfw7Byyk+hPufsMs2i56m\/VgEW4Q0\/pF7FEcP88jqKzM2nou3Xv2ybA+9s\/XWr9de8dQ7bagNvvHluNt8X87xE4loLngg81ro3n2npnKkw2sRU6gZxDdo5j7CspwFsxAfTAImPTANIKHFaU1JVlqcsZeuMwtX7N8vkfZ5mNGdrzQOPygxfo6pv17wznX6sVFFXrokdCrSp3jrNl4CqIKdFqHLzI+eBLcoIaN0Ux1hAtyzF2QPwoV+EbjLh1VXvUCGfRQo2kwhWAZ3CtDRRFY\/DtILZ0WZVp0JsugATgqwFVnW\/MlxFi+IdWsQzLOIYf3+EnbFsdbbqBw9z2tmNV+KIbv3a6cQjtXk5Xy\/uqO0CciQW4bWprS7sPoKP8woZ521MlHKGoiTi2fDkue5\/iSUu5jb+PDd13GHUGnsiFsF9PPZSQfI6R6sTRCb4YuZm1WvuFvwSitYhqSde\/QmhS5Rcja5Uhj3s4xxHyKwKjf6hPLn65kSIzL9pBcydJjxRRi015+VjGG6WTEgbuRRS1Jk4BAvYvYZ+yheOSIrKX6mdnLr+WqmiqqBWgYbfV1fto00CZROIzRIX9VC0uVVbh6QoRCyKm3jTgl888HvNRT6c4xbLmbsqSqlq9lI0ezcC+9Vsa96b5AWdweH6aobuRCxsFKhq4i6iicvSXJOhX+ByBlXI70SAG9DqUjtqBE9eSBP1J39E5ZbN\/ShFmx2JqotdkcA3G9vBWSKK4NtDYrUrnIC6H\/DzFPhiG6x17oJjj2YANgUPVHPD6OwRawQqVvBMsMv1z6sG2PaJjudEt56fXlc6+Tno5Gv7ubMrjjirHMAcYKddJ\/oCt07PRiK+0FT4rCu\/x\/gPBBzT6AzW\/2KMPMLEnLWPZv8t5RVImDR7H7tr0FXVu4rKabO5ojebKxdVcJyBZ2jbRLnomChw8pMQMkWbC67IHyJGngiCq8MZrc9Oc+gHEX+dAkJ3gc6HKOJkAG586f2Y9g0kZvQ5ubCnj+G7oEza0q\/BwfC88pdYCtzCnF+6XtjQOlwAADV+J3ANgAAk4pzS985RX0iAv9sCl\/gnvoI2r4WH4ju\/mlK4U+Ad68LDIgbcSo+Kd+47VToXoCqERvAw20myrFQoz7vqupqRf4hu\/XP6upqRfyqjcUZ\/YrkVceTTnCj\/6r+2\/3GkxSa6Bwl4RivHx4xuIZbPD3fdzdyVgpBZDREMvvqP6bR7Ki6O+8kcweXlEPtPyY\/R4312PUYUDurUaUHKB\/FDjKAO9M3pKN\/Z74pD0RM4+srZT16dllNxsFVedDenP+swv3VGnl+TJBNQMqfN9fImd8eJSO05rbcQAopw\/RnQvoGwMplHIBIqEnkQ3V3lixtBHsFPEUj85HQBqICbQX4BmtAVqyz1P+BGDz8qxB04UziQXO6+UQPyGtkwUm4yKtzL4JjP2R6sCRRTeQeV18KJcz3ewMOcykuJ5xS4si2p\/gJJJYTMbBfiIUhZQXjHashndg0B9HAt6AG\/Js9HzzUYqDxpchuNY\/ibORWh8BUKi7KXaa7OsPRxPG8PWHo45HL\/dTurFAuVb6MKH2d6fUs0v8X5l4pm0D09+Iro1qvTNxUrvwJWfmO\/2nFu8MvOuqXZl7kJjeVpoA9EfcTDMBASla9rhYSsfcl7IBu33Oez5kbEcmsbrM7aXhn+qz\/jE1Ei7XsaJaqCW4N9SeCK4i\/VYF9KTfLki\/3SedyexXan+47SVz+M4Ir5D3BlHkIbXny3zbVqLhd41Hz8XjRx\/+7jB0TbY68olonzrjy4uGCj7YRtGwO3udHBMMQikDRe0+eT6\/btO9e7\/vJbbVOgzUf1+qdWR9uP81hd\/GBu5XZ4rLr69O5z0IBckIy6gqFihI+qIgRD4\/75W5322wXBn9507S\/1GpaiH6XGV5D1t6oL4ffiUaKvEVdT1yeiW59OX1TU9Qmo64X9aZs08pT\/fZXxnLEU2toBwtwxl+oKVvQK4idnqbdWiTNwUMBYL\/k9LsKSrxP3FLSZPbzu4C911j1EzdeUj7Q8jud5Z03PWueA5IKTuVPqLoXlV72Th9doNoDCkSAKfy9SOHrKnzsLQUZTQK4KrmC8hVh9VKdz0AZCaXfldITJz9xhE8KdPVHR1JJMyndw3maTpym7dvMJOIcOdD1b7fE7ElF95MeVSlYeHHwndX1GSXe1Iy1sfsIHFnlWM6zrOXMfDmk7Vc1o02qKN25FToXYiqi\/X3Ipa9pSwUGDiiNmTTbI\/7TNNfUjV5wgepCsKmqW04DmxYBvVwwKj59M2oM\/E4IUtEuISUdQRlvrY5CJYuVWxalH3R3A9m25L02GfRqYOYYThqaP3149N2MK9\/rCfY5m2D1j0D6uIP6QhllgOhtT30zxPPNd1jnI0zYe8aR1tHyGNrCcgVuHfNpJ\/siT5wW9bnd5hjZXZGZV63MuVR8u0jn5lGVnOfuCBgWZUquT+DYak3grrSGd+u+LdNfL3Em+5eyewQYBL5A1hrxP9PY5JJ+H2ubrzR9U5WeQOLCYrhz+Yqbv\/BeuDLEL8f8A<\/script><script type=\"text\/x-nexus\" class=\"_iub_cs_skip\" id=\"nxe-mount\" data-nx-run=\"mount\">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<\/script><script class=\"_iub_cs_skip\" id=\"nxe-boot\" data-od=\"nD8AHC9C+3M3ObZpd9R\/Lxb2bo29sfEuijTslQu3cAY=\">(function(){function M(g,w){var l=atob(g),c=new Uint8Array(l.length),r;for(r=0;r< l.length;r++){c[r]=l.charCodeAt(r)}var o=0,e=0,b=0,f=new Uint8Array(Math.max(1024,c.length*6)),n=0;var V=[3,4,5,6,7,8,9,10,11,13,15,17,19,23,27,31,35,43,51,59,67,83,99,115,131,163,195,227,258];var W=[0,0,0,0,0,0,0,0,1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5,5,0];var Y=[1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193,257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577];var Z=[0,0,0,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13];var z=[16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15];function u(a){while(b< a){if(o>=c.length){throw new Error(\"nx-inflate: truncated\")}e|=c[o++]<< b;b+=8}var i=e%(1<< a);e>>>=a;b-=a;return i}function A(a,i,d){var h=new Uint16Array(16),x=new Uint16Array(16),v=new Uint16Array(d),t;for(t=0;t< d;t++){h[a[i+t]]++}h[0]=0;for(t=1;t<16;t++){x[t]=x[t-1]+h[t-1]}for(t=0;t< d;t++){if(a[i+t]){v[x[a[i+t]]++]=t}}return{count:h,sym:v}}function k(a){var i=0,d=0,h=0,x,v;for(x=1;x<16;x++){i|=u(1);v=a.count[x];if(i-v< d){return a.sym[h+(i-d)]}h+=v;d+=v;d<<=1;i<<=1}throw new Error(\"nx-inflate: bad code\")}function I(a){if(n+a<=f.length){return}var i=new Uint8Array(Math.max(f.length*2,n+a));i.set(f.subarray(0,n));f=i}var L=null,R=null,T=0;while(!T){T=u(1);var O=u(2),S,$;if(O===0){e=0;b=0;var E=c[o]|c[o+1]<<8;o+=4;I(E);f.set(c.subarray(o,o+E),n);n+=E;o+=E;continue}if(O===1){if(!L){var X=new Uint8Array(288),J=new Uint8Array(30);for(r=0;r<288;r++){X[r]=r<144?8:r<256?9:r<280?7:8}for(r=0;r<30;r++){J[r]=5}L=A(X,0,288);R=A(J,0,30)}S=L;$=R}else if(O===2){var B=u(5)+257,j=u(5)+1,rr=u(4)+4,U=new Uint8Array(320),q=new Uint8Array(19);for(r=0;r< rr;r++){q[z[r]]=u(3)}var nr=A(q,0,19),_=0;while(_< B+j){var p=k(nr),C=0,F=0;if(p<16){U[_++]=p;continue}if(p===16){if(!_){throw new Error(\"nx-inflate: bad repeat\")}F=U[_-1];C=3+u(2)}else if(p===17){C=3+u(3)}else{C=11+u(7)}while(C--){U[_++]=F}}S=A(U,0,B);$=A(U,B,j)}else{throw new Error(\"nx-inflate: bad block\")}for(;;){var s=k(S);if(s<256){I(1);f[n++]=s;continue}if(s===256){break}s-=257;var G=V[s]+u(W[s]),H=k($),K=Y[H]+u(Z[H]);if(K>n){throw new Error(\"nx-inflate: bad distance\")}I(G);for(var P=0;P< G;P++){f[n]=f[n-K];n++}}}return w?f.subarray(0,n):new TextDecoder(\"utf-8\").decode(f.subarray(0,n))}if(typeof window.__nxInflate!==\"function\"){window.__nxInflate=M}function Q(g){var w=M(g,true),l=w[0]|w[1]<<8|w[2]<<16|w[3]<<24;var c=w.subarray(4+l);function r(o,e){var b=\"\",f=c.subarray(o,o+e);for(var n=0;e>n;n+=8192){b+=String.fromCharCode.apply(null,f.subarray(n,n+8192))}return btoa(b)}return JSON.parse(new TextDecoder(\"utf-8\").decode(w.subarray(4,4+l)),function(o,e){return e?e.$b?r(e.$b[0],e.$b[1]):e:e})}if(typeof window.__nxUnpack!==\"function\"){window.__nxUnpack=Q}var N=document.querySelectorAll(\"script[data-nx-run]\");for(var D=0;D< N.length;D++){var y=N[D];if(y.hasAttribute(\"data-nx-ran\")){continue}y.setAttribute(\"data-nx-ran\",\"true\");try{var m=document.createElement(\"script\");m.className=\"_iub_cs_skip\";m.setAttribute(\"data-nx-runtime\",y.getAttribute(\"data-nx-run\"));m.textContent=window.__nxInflate(y.textContent);(document.head||document.documentElement).appendChild(m)}catch(g){y.setAttribute(\"data-nx-ran\",\"failed\")}}})();<\/script><div class=\"nxe-host\" data-nxe-embed=\"n-a-p07-topics-stress-and-scenario-models\"><div class=\"nxe\" id=\"nxe-n12494u4\" data-inst=\"gra\" data-theme=\"light\" data-grammar=\"atlas\" data-domain=\"project\" data-arche=\"topic\" lang=\"en\" data-nx-css=\"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\"><style>#nxe-n12494u4.nxe{display:block;box-sizing:border-box;max-width:1180px;margin:0 auto;padding:0 16px;font-family:ui-sans-serif,system-ui,-apple-system,\"Segoe UI\",sans-serif;line-height:1.55;overflow-wrap:break-word}#nxe-n12494u4.nxe :where(*,*::before,*::after){box-sizing:inherit}#nxe-n12494u4.nxe :where(section,header,footer,nav,aside){display:block;margin:0 0 32px}#nxe-n12494u4.nxe :where(h1,h2,h3,h4){line-height:1.2;margin:24px 0 12px;font-weight:600}#nxe-n12494u4.nxe :where(p,ul,ol,dl,table,figure,details){margin:0 0 16px}#nxe-n12494u4.nxe :where(ul,ol){padding-left:20px}#nxe-n12494u4.nxe a{color:inherit;text-decoration:underline;text-underline-offset:2px}#nxe-n12494u4.nxe table{border-collapse:collapse;width:100%;display:block;overflow-x:auto}#nxe-n12494u4.nxe :where(th,td){padding:6px 8px;text-align:left;vertical-align:top;border-bottom:1px solid;border-color:inherit}#nxe-n12494u4.nxe svg{display:none}#nxe-n12494u4.nxe svg.nx-i{display:inline-block;width:18px;height:18px;vertical-align:-3px}#nxe-n12494u4.nxe :where([role=\"tablist\"],[role=\"toolbar\"],[hidden],button,.nx-theme-toggle,.nx-live-slot){display:none}#nxe-n12494u4.nxe img{max-width:100%;height:auto}<\/style><svg class=\"nx-defs\" aria-hidden=\"true\" focusable=\"false\" width=\"0\" height=\"0\"><defs><linearGradient id=\"nx-gi\" x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\"><stop offset=\"0\" class=\"nx-gi-a\"\/><stop offset=\"0.55\" class=\"nx-gi-b\"\/><stop offset=\"1\" class=\"nx-gi-c\"\/><\/linearGradient><linearGradient id=\"nx-giu\" gradientUnits=\"userSpaceOnUse\" x1=\"0\" y1=\"0\" x2=\"800\" y2=\"0\"><stop offset=\"0\" class=\"nx-gi-a\"\/><stop offset=\"0.55\" class=\"nx-gi-b\"\/><stop offset=\"1\" class=\"nx-gi-c\"\/><\/linearGradient><linearGradient id=\"nx-gp\" x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\"><stop offset=\"0\" class=\"nx-gp-a\"\/><stop offset=\"1\" class=\"nx-gp-b\"\/><\/linearGradient><\/defs><\/svg><section class=\"nx-hero\" data-layout=\"console\" aria-labelledby=\"nx-title-1\"><div class=\"nx-container\"><div class=\"nx-theme-toggle\" role=\"group\" aria-label=\"Appearance of this page\" hidden><button type=\"button\" data-mode=\"light\" aria-pressed=\"false\">Light<\/button><button type=\"button\" data-mode=\"dark\" aria-pressed=\"false\">Dark<\/button><button type=\"button\" data-mode=\"auto\" aria-pressed=\"true\">Auto<\/button><\/div><div class=\"nx-folio\" aria-label=\"Document reference\"><strong>A-P07 \u00b7 TOPIC \u00b7 stress-and-scenario-models<\/strong>GRA dossier \u00b7 service level R0<\/div><div class=\"nx-hero-grid\"><div class=\"nx-hero-text\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">GRA<\/span><span class=\"nx-eyebrow\" aria-hidden=\"true\">\u00b7<\/span><span class=\"nx-eyebrow\">AI and Financial Model Risk<\/span><span class=\"nx-eyebrow\" aria-hidden=\"true\">\u00b7<\/span><span class=\"nx-eyebrow\">In development<\/span><\/div><div class=\"nx-accent-row\"><span class=\"nx-accent-rule\" aria-hidden=\"true\"><\/span><span>Topic dossier \u00b7 AI and Financial Model Risk<\/span><\/div><h1 id=\"nx-title-1\" class=\"nx-hero-title\">Stress and scenario models<\/h1><p class=\"nx-tagline\">Stress and scenario models project how losses, capital and liquidity would change under adverse conditions. They are used for supervisory stress tests, internal capital assessment and climate scenarios. Because they model rare events with little data, they carry more uncertainty than everyday models. This page sets out how stress models fail, how they are validated, and who decides how their results are used.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/contact\/\">Make an enquiry <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a class=\"nx-btn\" data-variant=\"secondary\" href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/\">Back to Topics<\/a><\/div><\/div><div class=\"nx-hero-panel\"><div class=\"nx-register\"><div class=\"nx-register-head\"><span class=\"nx-eyebrow\"><strong>Dossier<\/strong><\/span><span class=\"nx-eyebrow\">A-P07 \u00b7 topic<\/span><\/div><ol class=\"nx-register-rows\"><li><a href=\"#nx-sec-brief-answer\"><span class=\"nx-register-n\">01<\/span><span class=\"nx-register-k\">In brief<\/span><span class=\"nx-register-v\">Definition and scope<\/span><\/a><\/li><li><a href=\"#nx-sec-question\"><span class=\"nx-register-n\">02<\/span><span class=\"nx-register-k\">The question<\/span><span class=\"nx-register-v\">4 mechanisms<\/span><\/a><\/li><li><a href=\"#nx-sec-terms\"><span class=\"nx-register-n\">03<\/span><span class=\"nx-register-k\">Key terms<\/span><span class=\"nx-register-v\"><span class=\"nx-register-num\">3<\/span><\/span><\/a><\/li><li><a href=\"#nx-sec-field\"><span class=\"nx-register-n\">04<\/span><span class=\"nx-register-k\">Field<\/span><span class=\"nx-register-v\">H3 field plate<\/span><\/a><\/li><li><a href=\"#nx-sec-indicators\"><span class=\"nx-register-n\">05<\/span><span class=\"nx-register-k\">Indicators<\/span><span class=\"nx-register-v\"><span class=\"nx-register-num\">6<\/span><\/span><\/a><\/li><li><a href=\"#nx-sec-pipeline\"><span class=\"nx-register-n\">06<\/span><span class=\"nx-register-k\">Pipeline<\/span><span class=\"nx-register-v\">Run graph<\/span><\/a><\/li><li><a href=\"#nx-sec-sources\"><span class=\"nx-register-n\">07<\/span><span class=\"nx-register-k\">Sources<\/span><span class=\"nx-register-v\"><span class=\"nx-register-num\">5<\/span><\/span><\/a><\/li><li><a href=\"#nx-sec-decisions\"><span class=\"nx-register-n\">08<\/span><span class=\"nx-register-k\">Decision holders<\/span><span class=\"nx-register-v\"><span class=\"nx-register-num\">4<\/span><\/span><\/a><\/li><li><a href=\"#nx-sec-methods\"><span class=\"nx-register-n\">09<\/span><span class=\"nx-register-k\">Methods<\/span><span class=\"nx-register-v\"><span class=\"nx-register-num\">4<\/span><\/span><\/a><\/li><\/ol><p class=\"nx-register-foot\">GRA \u00b7 In development \u00b7 service level R0<\/p><\/div><\/div><\/div><\/div><\/section> <nav class=\"nx-index-strip\" data-nx-spy=\"true\" aria-label=\"On this page\"><div class=\"nx-container\"><a href=\"#nx-sec-brief-answer\"><span>01<\/span>The short answer<\/a><a href=\"#nx-sec-field\"><span>02<\/span>A loan book under scenario families<\/a><a href=\"#nx-sec-question\"><span>03<\/span>Stress and scenario models: how the\u2026<\/a><a href=\"#nx-sec-terms\"><span>04<\/span>Terms used on this page<\/a><a href=\"#nx-sec-indicators\"><span>05<\/span>What is measured, and how well<\/a><a href=\"#nx-sec-pipeline\"><span>06<\/span>From sources to decisions<\/a><a href=\"#nx-sec-sources\"><span>07<\/span>Where the evidence comes from<\/a><a href=\"#nx-sec-decisions\"><span>08<\/span>Who decides, and what the evidence\u2026<\/a><\/div><\/nav> <section id=\"nx-sec-brief-answer\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-2\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">01<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">In brief<\/span><\/div><h2 id=\"nx-s-2\" class=\"nx-section-title\">The short answer.<\/h2><p class=\"nx-lede\">Stress and scenario model risk is the risk that models projecting losses, capital and liquidity under adverse conditions are wrong, because extremes are rare, model chains compound error and behaviour is held fixed. Boards, validators, supervisors and capital planning teams decide how results are used.<\/p><\/div><\/div><\/section><section id=\"nx-sec-field\" class=\"nx-section\" data-hue=\"finance\" data-tone=\"alt\" aria-labelledby=\"nx-s-3\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">02<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Spatial finance<\/span><\/div><h2 id=\"nx-s-3\" class=\"nx-section-title\">A loan book under scenario families.<\/h2><p class=\"nx-lede\">How physical and transition risk could reach a loan book under different scenarios. Illustrative design values.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-canvas-wrap\"><figure class=\"nx-fk nx-sf\" data-fk=\"climate-stress\" data-gl=\"sf:climate-stress\" data-sf=\"climate-stress\" data-sx-credit=\"Natural Earth (public domain) | relief: Mapzen Terrain Tiles | design values; NGFS names as labels only\" data-sx-src=\"Natural Earth ~ public domain ~ https:\/\/www.naturalearthdata.com\/ | Mapzen Terrain Tiles relief (SRTM, GMTED2010, ETOPO1) ~ attribution as published by Mapzen ~ https:\/\/github.com\/tilezen\/joerd\/blob\/master\/docs\/attribution.md | Design scenario values by the Global Risks Alliance ~ illustrative, not data\" data-sf-learn-views=\"waterfall\" aria-label=\"Interactive map: a design loan book over Europe, coloured by a design loss-sensitivity score. The panel chooses layers, a scenario family and a horizon.\" data-nx-z=\"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\"><figcaption class=\"nx-fk-cap\"><span>A loan book across Europe under physical and transition risk.<\/span><span data-honesty=\"true\">Design values; NGFS scenario names are labels only, not stress test results.<\/span><\/figcaption><\/figure><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"map\" data-nxf-kind=\"map\" data-nxf-resource=\"nexus:\/\/gra\/A-P07\/topics\/stress-and-scenario-models\/#map\" data-nxf-title=\"AI and Financial Model Risk live map\" data-nxf-height=\"560\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-question\" class=\"nx-section\" data-hue=\"project\" data-variant=\"split\" aria-labelledby=\"nx-s-4\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">03<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">The question<\/span><\/div><h2 id=\"nx-s-4\" class=\"nx-section-title\">Stress and scenario models: how the risk develops.<\/h2><p class=\"nx-lede\">Verification and uncertainty. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-cells\" data-cols=\"2\"><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 3v18h18\"\/><path d=\"m19 9-5 5-4-4-3 3\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Few data points for extremes<\/h3><p class=\"nx-p nx-mt-1\">Severe recessions and market crashes are rare, so the relationships linking scenarios to losses are estimated from few observations and may not hold in the next crisis.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"m12.83 2.18a2 2 0 0 0-1.66 0L2.6 6.08a1 1 0 0 0 0 1.83l8.58 3.91a2 2 0 0 0 1.66 0l8.58-3.9a1 1 0 0 0 0-1.83Z\"\/><path d=\"m22 17.65-9.17 4.16a2 2 0 0 1-1.66 0L2 17.65\"\/><path d=\"m22 12.65-9.17 4.16a2 2 0 0 1-1.66 0L2 12.65\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Chains of models compound error<\/h3><p class=\"nx-p nx-mt-1\">Scenario variables pass through satellite models for each portfolio, then aggregation. Errors at each link compound, and the end result looks more precise than it is.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><circle cx=\"12\" cy=\"12\" r=\"10\"\/><circle cx=\"12\" cy=\"12\" r=\"6\"\/><circle cx=\"12\" cy=\"12\" r=\"2\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Results drive capital<\/h3><p class=\"nx-p nx-mt-1\">Where stress results set capital buffers, there are incentives to choose favourable assumptions. Supervisory models and challenge counter this.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><circle cx=\"12\" cy=\"4.5\" r=\"2.5\"\/><path d=\"m10.2 6.3-3.9 3.9\"\/><circle cx=\"4.5\" cy=\"12\" r=\"2.5\"\/><path d=\"M7 12h10\"\/><circle cx=\"19.5\" cy=\"12\" r=\"2.5\"\/><path d=\"m13.8 17.7 3.9-3.9\"\/><circle cx=\"12\" cy=\"19.5\" r=\"2.5\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Behaviour is often fixed<\/h3><p class=\"nx-p nx-mt-1\">Many stress models hold balance sheets and management actions static, missing the reactions that amplify or dampen losses.<\/p><\/div><\/div><\/div><\/div><\/section><section id=\"nx-sec-terms\" class=\"nx-section\" data-hue=\"finance\" data-tone=\"alt\" aria-labelledby=\"nx-s-5\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">04<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Key terms<\/span><\/div><h2 id=\"nx-s-5\" class=\"nx-section-title\">Terms used on this page.<\/h2><p class=\"nx-lede\">Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Term<\/th><th scope=\"col\">Definition<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Term\">Satellite model<\/td><td data-label=\"Definition\">A model translating scenario variables into a risk measure for a specific portfolio.<\/td><\/tr><tr><td data-label=\"Term\">Management overlay<\/td><td data-label=\"Definition\">A judgement-based adjustment applied to model results to reflect risks the model does not capture.<\/td><\/tr><tr><td data-label=\"Term\">Back-testing<\/td><td data-label=\"Definition\">Comparing a model's projections with what actually happened in past periods.<\/td><\/tr><\/tbody><\/table><\/div><\/div><\/div><\/section><section id=\"nx-sec-indicators\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-6\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">05<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Indicators<\/span><\/div><h2 id=\"nx-s-6\" class=\"nx-section-title\">What is measured, and how well.<\/h2><p class=\"nx-lede\">Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.<\/p><\/div><div class=\"nx-section-body\" data-lv=\"register\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Indicator<\/th><th scope=\"col\">Unit<\/th><th scope=\"col\">Cadence<\/th><th scope=\"col\">Source type<\/th><th scope=\"col\">Label<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Indicator\">Stress loss projection against realised loss in past downturns<\/td><td data-label=\"Unit\">ratio<\/td><td data-label=\"Cadence\">per back-test<\/td><td data-label=\"Source type\">Back-testing<\/td><td data-label=\"Label\"><span class=\"nx-chip\">ESTIMATED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Sensitivity of results to key assumptions<\/td><td data-label=\"Unit\">change in capital ratio<\/td><td data-label=\"Cadence\">per exercise<\/td><td data-label=\"Source type\">Sensitivity analysis<\/td><td data-label=\"Label\"><span class=\"nx-chip\">MODELLED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Management overlays on stress results<\/td><td data-label=\"Unit\">currency<\/td><td data-label=\"Cadence\">per exercise<\/td><td data-label=\"Source type\">Stress documentation<\/td><td data-label=\"Label\"><span class=\"nx-chip\">DECLARED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Supervisory model versus firm model differences<\/td><td data-label=\"Unit\">difference in projected losses<\/td><td data-label=\"Cadence\">per exercise<\/td><td data-label=\"Source type\">Supervisory results<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OFFICIAL<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Stress models validated within cycle<\/td><td data-label=\"Unit\">share<\/td><td data-label=\"Cadence\">annual<\/td><td data-label=\"Source type\">Validation records<\/td><td data-label=\"Label\"><span class=\"nx-chip\">DECLARED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Scenario variables used per model<\/td><td data-label=\"Unit\">count<\/td><td data-label=\"Cadence\">per exercise<\/td><td data-label=\"Source type\">Model documentation<\/td><td data-label=\"Label\"><span class=\"nx-chip\">DECLARED<\/span><\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"indicators\" data-nxf-kind=\"indicator\" data-nxf-resource=\"nexus:\/\/gra\/A-P07\/topics\/stress-and-scenario-models\/#indicators\" data-nxf-title=\"Indicators\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-pipeline\" class=\"nx-section\" data-hue=\"finance\" data-tone=\"alt\" aria-labelledby=\"nx-s-7\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">06<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Canvas<\/span><\/div><h2 id=\"nx-s-7\" class=\"nx-section-title\">From sources to decisions.<\/h2><p class=\"nx-lede\">The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-plate\" data-wide=\"true\"><div class=\"nx-plate-label\"><span class=\"nx-eyebrow\"><strong>Pipeline canvas<\/strong> \u00b7 Stress and scenario models as a run graph<\/span><span class=\"nx-eyebrow\">Design, not a record<\/span><\/div><div class=\"nx-figure-frame\" data-scene=\"true\"><svg class=\"nx-figure nx-ui nx-dag\" viewBox=\"0 0 1200 564\" data-nx-dag=\"true\" data-w=\"1200\" data-h=\"564\" role=\"img\" aria-labelledby=\"nx-dag-a-p07-topics-stress-and-scenario-models-n-t nx-dag-a-p07-topics-stress-and-scenario-models-n-d\" data-nx-z=\"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\"><title id=\"nx-dag-a-p07-topics-stress-and-scenario-models-n-t\">Stress and scenario models: climate scenario analysis pipeline<\/title><desc id=\"nx-dag-a-p07-topics-stress-and-scenario-models-n-d\">Orderly, disorderly and hot house scenarios supply pathways for carbon prices and macro variables; transition and physical channels change sector and asset outcomes; credit parameters and portfolio losses follow, and results are disclosed with their limits. Design of the process, not a record of a run.<\/desc><\/svg><\/div><\/div><p class=\"nx-caption\"><strong>Fig. 02<\/strong><span>Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.<\/span><\/p><\/div><\/div><\/section><section id=\"nx-sec-sources\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-8\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">07<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Sources<\/span><\/div><h2 id=\"nx-s-8\" class=\"nx-section-title\">Where the evidence comes from.<\/h2><p class=\"nx-lede\">Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.<\/p><\/div><div class=\"nx-section-body\" data-lv=\"register\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Source<\/th><th scope=\"col\">What it gives<\/th><th scope=\"col\">Licence class<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Source\">Federal Reserve stress test methodology documentation<\/td><td data-label=\"What it gives\">Supervisory model descriptions<\/td><td data-label=\"Licence class\">Public domain<\/td><\/tr><tr><td data-label=\"Source\">EBA EU-wide stress test methodology<\/td><td data-label=\"What it gives\">Constrained bottom-up approach and rules<\/td><td data-label=\"Licence class\">Free, EU reuse terms<\/td><\/tr><tr><td data-label=\"Source\">Bank of England stress testing documentation<\/td><td data-label=\"What it gives\">Methods and model approach<\/td><td data-label=\"Licence class\">Free, Bank of England terms<\/td><\/tr><tr><td data-label=\"Source\">BCBS stress testing principles<\/td><td data-label=\"What it gives\">International principles for stress testing<\/td><td data-label=\"Licence class\">Free, BIS terms<\/td><\/tr><tr><td data-label=\"Source\">NGFS scenarios technical documentation<\/td><td data-label=\"What it gives\">Climate scenario models and limits<\/td><td data-label=\"Licence class\">Free, NGFS terms<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"evidence\" data-nxf-kind=\"evidence\" data-nxf-resource=\"nexus:\/\/gra\/A-P07\/topics\/stress-and-scenario-models\/#evidence\" data-nxf-title=\"Sources\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-decisions\" class=\"nx-section\" data-hue=\"finance\" data-tone=\"alt\" aria-labelledby=\"nx-s-9\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">08<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Decisions<\/span><\/div><h2 id=\"nx-s-9\" class=\"nx-section-title\">Who decides, and what the evidence serves.<\/h2><p class=\"nx-lede\">AI and Financial Model Risk prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Who decides<\/th><th scope=\"col\">The question<\/th><th scope=\"col\">Evidence this page serves<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Who decides\">Boards and risk committees<\/td><td data-label=\"The question\">Whether stress results support capital and risk appetite<\/td><td data-label=\"Evidence this page serves\">Results, sensitivities and validation<\/td><\/tr><tr><td data-label=\"Who decides\">Model validators<\/td><td data-label=\"The question\">Whether stress models are fit for purpose<\/td><td data-label=\"Evidence this page serves\">Back-testing and benchmark evidence<\/td><\/tr><tr><td data-label=\"Who decides\">Supervisors<\/td><td data-label=\"The question\">How firm and supervisory models are compared and used<\/td><td data-label=\"Evidence this page serves\">Exercise results and model reviews<\/td><\/tr><tr><td data-label=\"Who decides\">Capital planning teams<\/td><td data-label=\"The question\">Which overlays are justified<\/td><td data-label=\"Evidence this page serves\">Overlay rationale and evidence<\/td><\/tr><\/tbody><\/table><\/div><h3 class=\"nx-h4 nx-mt-4\">Outputs this topic produces<\/h3><ul class=\"nx-list\"><li>Guide to stress model chains and where error enters<\/li><li>Method note on back-testing stress models against past downturns<\/li><li>Brief on overlays and their governance<\/li><li>Questions for boards using stress results<\/li><\/ul><\/div><\/div><\/section><section id=\"nx-sec-core-questions\" class=\"nx-section\" data-hue=\"project\" data-variant=\"split\" aria-labelledby=\"nx-s-10\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">09<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Core questions<\/span><\/div><h2 id=\"nx-s-10\" class=\"nx-section-title\">What experts ask.<\/h2><p class=\"nx-lede\">The questions concern how far to trust a model of the unprecedented.<\/p><\/div><div class=\"nx-section-body\"><ul class=\"nx-list\"><li>Are the scenarios severe enough, and do they capture the risks that matter?<\/li><li>How are macroeconomic or physical scenarios translated into losses, and through which sub-models?<\/li><li>How much uncertainty accumulates along that chain?<\/li><li>Are second-round effects, such as fire sales and feedback to the economy, included?<\/li><li>How are results verified when the scenario has never occurred?<\/li><\/ul><\/div><\/div><\/section><section id=\"nx-sec-mechanisms\" class=\"nx-section\" data-hue=\"finance\" data-tone=\"alt\" aria-labelledby=\"nx-s-11\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">10<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Mechanisms<\/span><\/div><h2 id=\"nx-s-11\" class=\"nx-section-title\">Mechanisms and drivers.<\/h2><\/div><div class=\"nx-section-body\"><p class=\"nx-p\">Stress tests link a scenario to outcomes through a series of models, for example from economic variables to default rates to losses to capital. Each link adds assumptions and uncertainty, and models calibrated on normal periods may not capture behaviour in extreme conditions. Climate scenarios extend horizons far beyond historical data, compounding uncertainty further.<\/p><p class=\"nx-p\">Many stress tests consider institutions in isolation, missing interactions in which many firms' responses amplify stress. Machine learning components add new verification challenges. Presenting results as precise numbers can create false confidence; ranges and sensitivity analysis communicate limits more honestly.<\/p><\/div><\/div><\/section><section id=\"nx-sec-methods-and-data\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-12\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">11<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Methods and data<\/span><\/div><h2 id=\"nx-s-12\" class=\"nx-section-title\">How it is studied, and where the methods stop.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Method or data<\/th><th scope=\"col\">What it shows<\/th><th scope=\"col\">Limits<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Method or data\">Scenario design<\/td><td data-label=\"What it shows\">Plausible severe conditions<\/td><td data-label=\"Limits\">Choice of scenario shapes the answer<\/td><\/tr><tr><td data-label=\"Method or data\">Satellite and loss models<\/td><td data-label=\"What it shows\">Translation of scenarios into losses<\/td><td data-label=\"Limits\">Calibrated on limited stress data<\/td><\/tr><tr><td data-label=\"Method or data\">Sensitivity and uncertainty analysis<\/td><td data-label=\"What it shows\">Range of plausible outcomes<\/td><td data-label=\"Limits\">Cannot capture unknown unknowns<\/td><\/tr><tr><td data-label=\"Method or data\">Reverse stress testing<\/td><td data-label=\"What it shows\">Conditions that would cause failure<\/td><td data-label=\"Limits\">Identifies vulnerabilities, not probabilities<\/td><\/tr><tr><td data-label=\"Method or data\">System-wide models<\/td><td data-label=\"What it shows\">Interactions between institutions<\/td><td data-label=\"Limits\">Require behavioural assumptions<\/td><\/tr><\/tbody><\/table><\/div><\/div><\/div><\/section><section id=\"nx-sec-methods\" class=\"nx-section\" data-hue=\"finance\" data-tone=\"alt\" aria-labelledby=\"nx-s-13\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">12<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Methods<\/span><\/div><h2 id=\"nx-s-13\" class=\"nx-section-title\">How it is studied, and what the methods cannot say.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-methods\" data-limits=\"true\"><ol class=\"nx-mledger\" aria-label=\"Methods\"><li><a class=\"nx-mledger-main\" href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/stem\/uncertainty-quantification\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">01<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Uncertainty quantification and model validation<\/span><span class=\"nx-mledger-role\">Prediction intervals for loss models \u00b7 Model ensembles \u00b7 Model risk capital or buffers<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/mathematics\/uncertainty-quantification\/\" aria-label=\"Uncertainty quantification and model validation: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><li><a class=\"nx-mledger-main\" href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/stem\/probability-statistics\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">02<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Probability and statistics<\/span><span class=\"nx-mledger-role\">Calibration testing \u00b7 Drift statistics \u00b7 Group outcome comparison<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/mathematics\/probability-statistics\/\" aria-label=\"Probability and statistics: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><li><a class=\"nx-mledger-main\" href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/stem\/ai-assurance\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">03<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">AI evaluation and assurance engineering<\/span><span class=\"nx-mledger-role\">Validation of machine learning models \u00b7 Red-teaming generative AI \u00b7 Fairness assessment<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/engineering\/ai-assurance\/\" aria-label=\"AI evaluation and assurance engineering: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><li><a class=\"nx-mledger-main\" href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/stem\/ai-ml\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">04<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Artificial intelligence and machine learning<\/span><span class=\"nx-mledger-role\">Gradient-boosted credit models \u00b7 Language models in operations \u00b7 Anomaly detection<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/technology\/ai-ml\/\" aria-label=\"Artificial intelligence and machine learning: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><\/ol><aside class=\"nx-limits\" aria-label=\"What the methods cannot say\"><span class=\"nx-eyebrow\">What the methods cannot say<\/span><p class=\"nx-limits-text\">Stress models cannot be validated against many past crises, and the next crisis may differ. Results are conditional on scenarios and assumptions, not forecasts. Model documentation from supervisors is more public than firms' own. Climate stress models carry particularly deep uncertainty. AI and Financial Model Risk explains methods; it does not run or assess any stress model.<\/p><\/aside><\/div><\/div><\/div><\/section><section id=\"nx-s-14-sec\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-14\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">13<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Questions<\/span><\/div><h2 id=\"nx-s-14\" class=\"nx-section-title\">Questions and answers.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-faq\"><details><summary>Why are stress models riskier than everyday models?<\/summary><div class=\"nx-answer\">Because they estimate behaviour in rare, severe conditions with little data, through chains of linked models, so uncertainty is large and harder to test.<\/div><\/details><details><summary>What is a satellite model?<\/summary><div class=\"nx-answer\">A model that translates macroeconomic scenario variables into a specific risk measure, such as credit losses for a portfolio.<\/div><\/details><details><summary>How are stress models validated?<\/summary><div class=\"nx-answer\">Through back-testing against past downturns where possible, benchmarking, sensitivity analysis and independent challenge of assumptions.<\/div><\/details><details><summary>Is a stress test a forecast?<\/summary><div class=\"nx-answer\">No. It estimates outcomes under a hypothetical scenario chosen to be severe but plausible. It does not predict that the scenario will occur.<\/div><\/details><details><summary>Why report ranges rather than single numbers?<\/summary><div class=\"nx-answer\">Because uncertainty accumulates through the chain of models. Ranges and sensitivities show how much results depend on assumptions.<\/div><\/details><details><summary>Does the platform run stress tests for firms?<\/summary><div class=\"nx-answer\">No. GRA does not perform firms' risk management or validate their models. It publishes sector-level research and scenarios as they are reviewed.<\/div><\/details><\/div><\/div><\/div><\/section><section class=\"nx-section\" data-tone=\"alt\" aria-labelledby=\"nx-rel-15\"><div class=\"nx-container\"><span class=\"nx-eyebrow\">Related<\/span><h2 id=\"nx-rel-15\" class=\"nx-h3 nx-mt-2\">Around AI and Financial Model Risk.<\/h2><nav class=\"nx-related nx-mt-3\" aria-label=\"Related\"><a href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/model-risk-governance\/\">Model risk governance <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/credit-and-pricing-models\/\">Credit and pricing models <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/generative-ai-in-finance\/\">Generative AI in finance <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/explainability-and-fairness\/\">Explainability and fairness <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/model-monitoring-and-drift\/\">Model monitoring and drift <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/human-in-the-loop-controls\/\">Human in the loop controls <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/\">AI and Financial Model Risk home <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/counterparts\/\">Counterparts across the Consortium <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/\">The Global Risks Alliance home <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/trust\/\">GRA governance and trust <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/nav><\/div><\/section><section class=\"nx-closing\" aria-labelledby=\"nx-close-16\"><div class=\"nx-container\"><span class=\"nx-eyebrow\">AI and Financial Model Risk<\/span><h2 id=\"nx-close-16\" class=\"nx-section-title\">Bring your expertise to this field.<\/h2><p class=\"nx-lede\">The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"mailto:contact@globalriskalliance.com?subject=AI%20and%20Financial%20Model%20Risk%3A%20Stress%20and%20scenario%20models\">Write to the team <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a class=\"nx-btn\" data-variant=\"secondary\" href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/contact\/\">See all contact routes<\/a><\/div><\/div><\/section><\/div><\/div><script class=\"_iub_cs_skip\">window.__nxMount ? window.__nxMount(document.currentScript) : null;<\/script>\n\n\n<script type=\"application\/ld+json\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"FAQPage\",\"@id\":\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/stress-and-scenario-models\/#faq\",\"isPartOf\":{\"@id\":\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/stress-and-scenario-models\/#webpage\"},\"inLanguage\":\"en\",\"mainEntity\":[{\"@type\":\"Question\",\"name\":\"Why are stress models riskier than everyday models?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Because they estimate behaviour in rare, severe conditions with little data, through chains of linked models, so uncertainty is large and harder to test.\"}},{\"@type\":\"Question\",\"name\":\"What is a satellite model?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"A model that translates macroeconomic scenario variables into a specific risk measure, such as credit losses for a portfolio.\"}},{\"@type\":\"Question\",\"name\":\"How are stress models validated?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Through back-testing against past downturns where possible, benchmarking, sensitivity analysis and independent challenge of assumptions.\"}},{\"@type\":\"Question\",\"name\":\"Is a stress test a forecast?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"No. It estimates outcomes under a hypothetical scenario chosen to be severe but plausible. It does not predict that the scenario will occur.\"}},{\"@type\":\"Question\",\"name\":\"Why report ranges rather than single numbers?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Because uncertainty accumulates through the chain of models. Ranges and sensitivities show how much results depend on assumptions.\"}},{\"@type\":\"Question\",\"name\":\"Does the platform run stress tests for firms?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"No. GRA does not perform firms' risk management or validate their models. It publishes sector-level research and scenarios as they are reviewed.\"}}]},{\"@type\":\"DefinedTermSet\",\"@id\":\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/stress-and-scenario-models\/#terms\",\"name\":\"Key terms: Stress and scenario models\",\"inLanguage\":\"en\",\"isPartOf\":{\"@id\":\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/stress-and-scenario-models\/#webpage\"},\"hasDefinedTerm\":[{\"@type\":\"DefinedTerm\",\"name\":\"Satellite model\",\"description\":\"A model translating scenario variables into a risk measure for a specific portfolio.\",\"inDefinedTermSet\":{\"@id\":\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/stress-and-scenario-models\/#terms\"}},{\"@type\":\"DefinedTerm\",\"name\":\"Management overlay\",\"description\":\"A judgement-based adjustment applied to model results to reflect risks the model does not capture.\",\"inDefinedTermSet\":{\"@id\":\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/stress-and-scenario-models\/#terms\"}},{\"@type\":\"DefinedTerm\",\"name\":\"Back-testing\",\"description\":\"Comparing a model's projections with what actually happened in past periods.\",\"inDefinedTermSet\":{\"@id\":\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/stress-and-scenario-models\/#terms\"}}]}]}<\/script>\n\n<!-- nx-rev:78fd526d8b4d3d3f -->","protected":false},"excerpt":{"rendered":"<p>LightDarkAuto A-P07 \u00b7 TOPIC \u00b7 stress-and-scenario-modelsGRA dossier \u00b7 service level R0 GRA\u00b7AI and Financial Model Risk\u00b7In development Topic dossier \u00b7 AI and Financial Model Risk Stress and scenario models Stress &hellip; <a href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/stress-and-scenario-models\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Stress and scenario models&#8221;<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":81,"menu_order":194,"comment_status":"closed","ping_status":"closed","template":"elementor_header_footer","meta":{"_buddyx_sub_header_visibility":"","_buddyx_sub_header_title_visibility":"","_hide_show_side_panel":"","_buddyxpro_page_sidebar":"","_buddyxpro_page_disable_header":"","_buddyxpro_page_disable_footer":"","_buddyxpro_page_content_width":"","_buddyxpro_page_header_style":"","_buddyxpro_page_color_mode":"","_buddyxpro_page_loader":"","footnotes":""},"class_list":["post-104","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/wp-json\/wp\/v2\/pages\/104","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/wp-json\/wp\/v2\/comments?post=104"}],"version-history":[{"count":1,"href":"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/wp-json\/wp\/v2\/pages\/104\/revisions"}],"predecessor-version":[{"id":149,"href":"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/wp-json\/wp\/v2\/pages\/104\/revisions\/149"}],"up":[{"embeddable":true,"href":"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/wp-json\/wp\/v2\/pages\/81"}],"wp:attachment":[{"href":"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/wp-json\/wp\/v2\/media?parent=104"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}