Market Based Climate Stress Tests

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A changing climate will also create changes in the risks and opportunities faced by investors. If banks are exposed to the risks of climate change, then these risks could potentially affect how financial markets function. This project will evaluate the exposure of banks and other financial institutions to climate change risks. After these methods are carefully tested for accuracy and reliability, the results will be updated weekly and posted freely on the internet for regulators, investors and policy makers to see. These estimates will help regulators assess financial system resilience in the event of climate related shocks. This project takes a completely new approach to stress testing for climate change. It focusses on prices of assets such as bank loans, rather than actual damages. Such bank stress tests can show exposure to climate change from damages that will not occur for more than twenty years but which could bankrupt a bank over a short period of time since asset prices can fall today in response to bad news about the distant future. These exposure measures are essentially a way to see how resilient banks are to climate related risks by examining the correlation between its stock return and climate risk factors. In fact, this observation leads to the measurement of the “greenness” of any investment by looking at the correlation between its return and a suite of climate risk factors. This project will apply state of the art statistical procedures to measure the exposure of financial institutions to climate risks. The procedure first formulates a portfolio of investment assets that reflects the risks of climate change. If climate change becomes more severe, this portfolio will fall in value. Several candidates for this portfolio are based on transition risks or physical risks or other versions of climate risk. To assess the exposure of banks, the change in a bank’s stock market valuation is regressed on the climate risk portfolios to see how important they are in determining the change in value of the bank. This regression allows the coefficients to change following the Dynamic Conditional Beta (DCB) framework as in Engle (2016). To be certain that this accurately measures the exposure, the project will a) develop the econometrics to estimate the confidence intervals in the dynamic conditional beta model, b) develop additional climate risk measures using climate efficient factor mimicking portfolios of publicly available funds marketed as sustainable, c) determine which portfolio is the best measure of transition risk, d) consider other risk factors for financial institutions, e) incorporate granular supervisory data to determine the extent to which market measures reflect actual bank holdings, f) compare the exposure rankings from the VRI model and from supervisory measures which mostly reflect NGFS methodology. A key feature of this research involves the use of confidential supervisory data from central banks in different parts of the world. Together these unique data sources will help to corroborate and improve the estimates from the econometrics. Comparison of the findings with those from supervisors can be carried out simply by rank correlations. However more interesting comparisons may involve understanding where there are differences. For example, using one set of exposures to predict another and including characteristics data will show where the differences are most important.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.