Physical Climate Risk Factors and an Application to Measuring Insurers’ Climate Risk Exposure
Co-authors: Robert Engle, Shan Ge, and Xuran Zeng
Revise and Resubmit at Review of Financial Studies
Presentations
FHFA Econ Summit, System Banking Conference by the Federal Reserve Board, Financial Stability Conference by the Cleveland Fed and the OFR, NY Fed, Central Bank of Chile Annual Conference (
Video), Greater New York Finance Women Symposium*, Midwest Finance Association Meeting, Federal Reserve Board*, Baruch Conference* (Poster), CESifo Conference, Columbia, ZEW Conference, WEAI-IBEFA Conference, Banca d’Italia and the IMF Conference, Volatility and Risk Institute Conference*, Yale Symposium, Stanford Institute for Theoretical Economics Conference, WE ARE IN Macroeconomics and Finance Conference, OU-RFS Conference, KU-KAIST Virtual Finance Seminar, SFS Cavalcade, FIRS Conference, SoFiE Summer School, EFA, Conference in Sustainable Finance at the University of Luxembourg
View Abstract Blog
We construct a novel physical risk factor by forming a portfolio of REITs, long on those with properties more exposed to climate risk and short on those less exposed. Combined with a transition risk factor, we assess the climate risk exposure of P&C and life insurance companies in the U.S. Insurers can be exposed to climate-related physical risk through their operations and transition risk through their $12 trillion of financial asset holdings. We estimate insurers’ dynamic physical and transition climate beta, i.e. their stock return sensitivity to the physical and transition risk factors. Validating our approach, we find that insurers with larger exposures to risky states have a higher sensitivity to physical risk, while insurers holding more brown assets have a higher sensitivity to transition risk. Using the estimated betas, we calculate the expected capital shortfall of insurers under various climate stress scenarios.U.S. Banks’ Exposures to Climate Transition Risks
Co-authors: Joao Santos and Lee Seltzer
Presentations
Federal Reserve Bank of Cleveland*, System Climate Meeting by the Federal Reserve Bank of San Francisco, OCC Symposium*, IFABS Oxford Conference*, ASSA IBEFA, Stanford Institute for Theoretical Economics Conference, NY Fed-Columbia University Conference*, American Bankers Association, Federal Reserve Board, E-axes Young Scholar’s Webinar, CBRT-IFC Workshop*, the Banque de France Conference*, Banca d’Italia and the IMF Conference, EFA*
View Abstract Blog
We propose a novel approach to estimate banks' credit exposures to transition risks using sectoral effects of climate policies from general equilibrium (GE) models. At worst, estimated exposures reach 14% of bank loan portfolio values. Average exposures drop below 2% after incorporating information on loan payoff structures. Emissions only explain 60% of the variation in our estimated exposure measure, suggesting that our measure captures aspects of transition risk unique to GE models. Consistent with managing their exposures to transition risk, banks joining the Net-Zero Alliance reduced their exposures compared to other banks, mainly by decreasing lending to the riskiest industries.Credit Card Banking
Co-authors: Itamar Drechsler, Weiyu Peng, Dominik Supera, and Guanyu Zhou
Presentations
Federal Reserve Bank of New York, Wharton*, Federal Reserve Bank of Philadelphia*, NBER Financial Market Frictions and Systemic Risks*, SFS Cavalcade, FIRS Conference, EFA*, NBER Summer Institute*
View Abstract Blog
Credit card interest rates, the marginal cost of consumption for nearly half of households, currently average 23 percent, far exceeding the rates on any other major type of loan or bond. Why are these rates so high? To understand this, and the economics of credit card banking more generally, we analyze regulatory account-level data on 330 million monthly accounts, representing 90 percent of the US credit card market. Default rates are relatively high at around 5 percent, but explain only a fraction of cards’ rates. Non-interest expenses and rewards payments are more than offset by interchange and non-interest income. Operating expenses, such as marketing, are very large, and are used to generate pricing power. Deducting them, we find that credit card lending still earns a 6.8 percent return on assets (ROA), more than four times the banking sector’s ROA. Using the cross section of accounts by FICO score, we estimate that credit card rates price in a 5.3 percent default risk premium, which we show is comparable to the one in high-yield bonds. Adjusting for this, we estimate that card lending still earns a 1.17 percent to 1.44 percent “alpha” relative to the overall banking sector.Deviations from the Law of One Price across Economies
Co-author: Jaehoon (Kyle) Jung
Presentations
WAPFIN @ Stern Conference, Australasian Finance and Banking Conference*, Columbia GSB Finance Ph.D. Seminar, NYU Stern
View Abstract AFA Poster
In a model with agents facing constraints heterogeneous across economies, we provide a novel explanation for an understudied yet economically significant deviation from the Law of One Price across FX forward markets. Specifically, we document a substantial divergence between the exchange rate for locally traded forward contracts and contracts with the same maturity traded outside the jurisdiction of countries during the global financial crisis, and that the magnitudes varied across currencies. The model predicts that (1) the basis increases with the shadow costs of constraints across time and increases with the country-specific FX position limits across countries; (2) the shadow cost of each constraint non-linearly increases as the intermediary sector's relative performance declines below a threshold; and (3) higher shadow cost of the position limit predicts lower future excess return on local-currency denominated assets, as buying local assets relaxes the FX position limit constraint imposed on the intermediaries. We test the model predictions and find consistent evidence in countries with tight position limits.