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Topic dossier · Asset Management Nexus

Long-horizon risk

Pension funds, insurers, endowments and sovereign funds hold liabilities that run for decades, far beyond the window of most risk models. Over those horizons, climate change, nature loss, demographic ageing and technology shifts can reprice whole sectors and change the returns assumed in funding plans. This page sets out how long-horizon risks reach portfolios, how scenario and macro-financial methods treat them, and why their outputs are explorations of possible futures rather than forecasts.

01In brief

The short answer.

Long-horizon investment risk is the risk to investors whose liabilities run for decades, such as pension funds, insurers, endowments and sovereign funds, from slow drivers that can reprice whole sectors suddenly: climate change, nature loss, demographic ageing and technology shifts. Much of it is systemic and cannot be diversified away.

02Spatial finance

Assets on the grid, and the portfolio they make.

Where a portfolio's assets sit, and how much of its value is most exposed. Hypothetical assets, not an assessment.

Sixteen assets at real towns, and the share of portfolio value in the most exposed places.Hypothetical assets and design scores, not advice.
03The question

Long-horizon risk: how the risk develops.

Climate, demographic and technology shifts. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.

Mechanism

Slow drivers, sudden repricing

Physical climate change, ageing and technological diffusion move slowly, but markets can reprice them abruptly when policy, a disclosure or an event changes expectations. The risk lies in the timing of recognition as well as in the drift.

Mechanism

Capital market assumptions shift

Funding plans rest on assumed long-run returns, inflation and discount rates. A structural change in growth, productivity or interest rates changes contributions, funding levels and the asset allocation that follows from them.

Mechanism

Liabilities move too

Longevity, wage growth, inflation indexation and discount rates change the value of pension and insurance liabilities. A shock that moves assets and liabilities together differs from one that pulls them apart.

Mechanism

Systemic, not diversifiable

Risks that touch every sector and region, such as unmitigated warming or widespread loss of ecosystem services, cannot be diversified away. Universal owners carry them as exposure to the whole economy, which makes them a question of stewardship and policy as well as allocation.

04Key terms

Terms used on this page.

Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.

TermDefinition
Climate value at riskA scenario-based estimate of the change in a portfolio's value from climate policy and physical risks.
Capital market assumptionsLong-run expected returns, volatilities and correlations used for strategic asset allocation.
Liability durationThe sensitivity of the present value of liabilities to interest rates, roughly their weighted average timing.
05Indicators

What is measured, and how well.

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.

IndicatorUnitCadenceSource typeLabel
Climate value-at-risk under a named scenariopercent of portfolio valueannualScenario models applied to holdingsMODELLED
Exposure to transition-sensitive sectorspercent of assets under managementquarterlyHoldings mapped to sector classificationESTIMATED
Physical hazard exposure of investee assetsshare of portfolio value by hazard classannualAsset locations on hazard layersMODELLED
Dependency on ecosystem servicesshare of portfolio value by dependency ratingannualSector mapping of holdings with ENCOREESTIMATED
Liability durationyearsannualActuarial valuationOFFICIAL
Funding level sensitivity to the discount ratepercentage points per basis pointannualAsset-liability modelsMODELLED
Long-run expected return assumptionpercent per year, realannualPublished capital market assumptionsDECLARED
Scenarios tested against the strategic allocationcount of scenarios by typeper reviewInvestment committee recordsREPORTED
06Canvas

From sources to decisions.

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.

Pipeline canvas · Long-horizon risk as a run graphDesign, not a record
Long-horizon risk: from horizon scanning to wind-tunnelled policySignals from horizon scanning are clustered into drivers; the most uncertain and impactful define scenario axes; narratives are written; policies are wind-tunnelled against each scenario and signposts monitored. Design of the process, not a record of a run.

Fig. 02Nodes 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.

07Sources

Where the evidence comes from.

Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.

SourceWhat it givesLicence class
NGFS climate scenariosTransition and physical risk pathways for macro-financial variables, by scenario and regionFree, attribution under the scenario explorer terms
IPCC Sixth Assessment ReportAssessed physical climate projections and mitigation pathwaysFree to read, IPCC terms
ENCORE (UNEP-WCMC, UNEP FI, Global Canopy)Sector dependencies and impacts on nature for screening portfoliosFree, attribution, terms of use
UN World Population ProspectsPopulation and age structure projections by countryFree and open, attribution
OECD Pension Markets in Focus and Global Pension StatisticsPension fund assets, allocations and funding indicators by countryFree to read, OECD terms
Federal Reserve Financial Accounts of the United StatesSector balance sheets of pension funds, insurers and householdsPublic domain
Bank of England climate biennial exploratory scenario publicationsSupervisory climate scenario design and aggregate results for banks and insurersFree reuse, attribution
Copernicus Climate Change ServiceClimate reanalysis and projections for physical hazard analysisFree and open, attribution
08Decisions

Who decides, and what the evidence serves.

Asset Management Nexus prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.

Who decidesThe questionEvidence this page serves
Pension trustees and boards of asset ownersWhether the strategic asset allocation and funding plan hold up across a range of long-horizon futuresAsset and liability sensitivities under named scenarios, and the assumptions each scenario fixes
Insurance boards and actuarial functionsHow climate and longevity scenarios enter the own risk and solvency assessmentScenario design notes; the variables each scenario does and does not move
Central banks and supervisors running climate exercisesWhich scenarios, horizons and balance-sheet assumptions an exploratory exercise should useReviews of scenario construction and of known gaps in damage functions
Sovereign fund boards and ministries of financeHow long-horizon exposures inform the fund's mandate and reference portfolioSector and region exposures under transition and physical scenarios

Outputs this topic produces

  • Scenario narratives linking climate, nature, demographic and technology drivers to asset classes
  • Asset and liability sensitivity tables under named scenarios, with assumptions listed
  • Reviews of how published scenarios are built and what they leave out
  • Research notes on capital market assumptions over long horizons
09Core questions

What experts ask about long horizons.

  • How might climate change, ageing and technology affect growth, interest rates and asset returns?
  • How can scenarios represent change that has no historical precedent?
  • How should uncertainty about the distant future be reflected in risk measures?
  • How do short-term incentives in the industry interact with long-horizon risks?
  • When might slow-moving risks crystallise suddenly through repricing?
10Mechanisms and drivers

Forces that act over long horizons.

Physical climate change affects productivity, asset damage and the habitability of regions, with impacts growing over time. Transition policies and technologies change the value of carbon-intensive assets and create new sectors. Population ageing affects labour supply, savings, interest rates and demand for different goods. Technological change shifts which firms and sectors earn returns.

These forces are uncertain in timing and magnitude, but their direction is often well established. Markets may not price them fully, because standard risk models rely on historical data and short horizons. Repricing can then occur abruptly when new information, policy or events change expectations.

Industry incentives can shorten horizons. Performance is often assessed over short periods, and managers may be reluctant to deviate from benchmarks.

11Methods and data

Methods and their limits.

  1. 01

    Long-horizon scenarios

    Coherent narratives and pathways for climate, demographics and technology, using scenario design and stress testing. Scenarios are not forecasts.

  2. 02

    Integrated models

    Linking physical and economic models to estimate impacts on growth and sectors. Damage functions are highly uncertain.

  3. 03

    Capital market assumptions

    Long-run return and risk expectations. They rely on judgement and are sensitive to assumptions.

  4. 04

    Historical analysis

    Studying past structural shifts. History may not repeat.

Long-horizon modelling carries deep uncertainty. Analyses show ranges and the assumptions that drive them.

12Methods

How it is studied, and what the methods cannot say.

  1. Scenario design and stress testingClimate scenario analysis of portfolios · System-wide redemption and margin scenariosApplied here
  2. Financial economics and macro-financeCommon ownership and fire-sale channels · Pricing of transition riskApplied here
  3. EconometricsFlow and price pressure estimation · Event studies of stewardship and disclosureApplied here
  4. Financial and risk engineeringLook-through risk decomposition · Liquidity bucketing and redemption testsApplied here
13Questions

Questions and answers.

Are long-horizon scenarios forecasts?
No. A scenario states what would follow if a set of assumptions held. Several are read together to see which decisions are robust across them; none carries a probability unless it states one.
Why can climate value-at-risk look small?
Because many models discount distant damages, omit tipping points and compound effects, and assume smooth adjustment. The figure reflects the model's coverage, so the page lists what each model includes and excludes.
What is a universal owner?
A large, diversified, long-term investor whose portfolio is in effect a slice of the whole market. It cannot diversify away economy-wide risks, so it has an interest in how those risks develop across the economy.
Where does nature fit in long-horizon risk?
Many sectors depend on water, soils, pollination and stable ecosystems. ENCORE and the TNFD LEAP approach help map those dependencies, but translating them into financial terms is at an early stage and heavily assumption-led.
Does the platform forecast long-term returns?
No. It studies scenario methods and their uncertainty. It does not publish return forecasts or recommendations.
Why are historical data not enough?
Because long-horizon forces such as climate change and demographic shifts may have no close historical precedent.
Can scenarios be used for decisions?
Yes, to test resilience across plausible futures. They do not tell an institution what will happen.
Are demographic risks included?
Yes, alongside climate and technology, since ageing affects savings, interest rates and demand.
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