GRA · Actuarial and Quantitative Risk
Spatial financeWhere capital meets hazard, exposure and transition
Research and evidence
Actuarial and Quantitative Risk research asks how to estimate tails honestly with short and changing data, how to represent dependence between perils and regions, how climate should condition pricing, and how models should be validated when the past is a biased sample.
The workspace for research and evidence.
Evidence grading
Research and evidence · Review wave 2 · Example interface, not a record
Certainty by criterion
0 low to 100 high
Higher is more certain. Values are examples.
Review queue
- R-1Best estimate reservessecond reviewer4 d
- R-2Risk marginsecond reviewer1 d
- R-3Actual vs expected claimssecond reviewer3 d
Actuarial and Quantitative Risk · Evidence grading. Evidence grading: example names and illustrative values, not a record.
What the research asks.
- 01
Loss distributions
Frequency, severity and aggregate loss, and how tails are estimated.
- 02
Extreme value methods
Tail estimation for hazards, markets and operational losses.
- 03
Climate-adjusted pricing questions
How non-stationary hazard changes pricing assumptions.
- 04
Credibility and sparse data
Working where loss history is short or missing.
- 05
Dependence and correlation
Copulas, contagion and why correlations break in stress.
- 06
Capital and solvency models
Internal models, standard formulas and their assumptions.
Questions, methods and limits.
| Question | Method | Limit |
|---|---|---|
| How uncertain is a 1-in-200 estimate? | Extreme value theory with intervals | Short records |
| How should climate adjust pricing? | Trend-adjusted hazard views | Attribution contested |
| How should dependence be modelled? | Copulas and scenario dependence | Tail dependence poorly identified |
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Results are labelled as observed, published statistics, estimated, modelled, forecast, scenario or expert judgement. Missing data is shown as missing, never filled silently.
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