9 June 2025; 13:00-14:00 GMT+1
Presenter: William Shields
Will’s research is focused on investigating how Climate Risk Assessments (CRA) can be enhanced for vulnerable rural communities in the Global South, to quantitatively capture inter-linked dynamics across climate, crop suitability, agricultural production, impact of pests, markets and their capacity for disruption, demographics and welfare / political interventions.
Paper to be presented
Title: CreditMetrics™ TECHNICAL DOCUMENT. The focus of Will’s discussion: three related financial formulas that changed the world… and how we could update them for Climate Change
Author: Risk Metrics Group. Internationally designed by Academics and Corporations (including JPMorgan, Bank of America, BZW, Deutsche Morgan Grenfell, KMV Corporation, Swiss Bank Corporation, and UBS), CreditMetrics™ TECHNICAL DOCUMENT was made freely available to the world and underscored a collective commitment to developing an open and evolving standard for good risk management. Could such a thing happen today with Climate Insight? Or is the collaborative spirit that led to the publishing of CreditMetrics™ TECHNICAL DOCUMENT missing in today’s Corporate world where firms see Climate Research / Insight as a key commercial edge, not to be given away for free?
Link to paper: https://ecologyandsociety.org/vol27/iss2/art7/
Three related financial formulas that changed the world… and how we could update them for Climate Change
- Merton 1974:
- PD_i = 1 – N(Z_i) … a firm defaults on its obligations when its assets deteriorate by Z standard deviations of its asset value which also defines its Probability of Default (PD)
- Vasicek 1987:
- Z_i = ρ⋅X + sqrt(1− ρ )⋅ϵ_i …. Z standard deviations can be decomposed into a systematic part X (shared with other firms e.g. deteriorating GDP) and an idiosyncratic part ϵ (e.g. lawsuits)
- cPD_i = N((N-1(PD_i) + Sqrt( ρ ) * N-1(X))/sqrt(1 – ρ )) … rearrange the formula above and you can assess how a firm’s Probability of Default responds to systematic factors X
- When we have enough customers: Portfolio cPD = Sum ( N((N-1(PD_i) + Sqrt( ρ ) * X) / sqrt(1 – ρ )) ) / n … if we have lent to a lot of customers in a portfolio, then we can assume the idiosyncratic risk of each diversifies away and only systematic inputs affect the Portfolio Credit Risk.
- CreditMetrics™ TECHNICAL DOCUMENT 1997: Formalised, expanded and calibrated these equations to standardise Credit Risk assessments for banks.
- Basel Committee on Banking Reform 2004: Following Credit Metrics wide industry acceptance, the above formula was used by international regulators to set the amount of capital many banks must hold against their lending risk: Sum ( N((N-1(PD) + Sqrt( ρ ) * N-1(0.999) ) / sqrt(1 – ρ )) ) * Exposure) … our banks must be able to survive 1in1000 year systemic loss events within their lending portfolios!
Why does this matter? Like E=MC^2 the above formulae changed the world.
The system of formulas above describes a network of related entities across the planet, responding to shared drivers of risk. Climate Change could be considered one of these drivers. So far this framework has not been used as a lever to adjust Banking Lending habits (perhaps partially to blame for the large amount of UK banking lending to fossil fuel projects?), but it has the potential to via the all important rho parameter which describes how correlated a bank’s customers are to each other, as well as how likely firms are to fail in general (the PD_i parameter). Higher PD & Rho means more capital a bank must set aside, which means the less lending of that type they will do. Should international regulators be using this stick more to limit Fossil Fuel lending, particularly given increasing PD & Rho parameters would not stop lending, but ensure that only the most necessary fossil fuel lending was done?
Session Highlights:
To be added after the session.