Journal Article10.2139/SSRN.887730
What We Know, Don't Know and Can't Know About Bank Risks: A View from the Trenches
Andrew Kuritzkes,Til Schuermann +1 more
TL;DR: In this paper, the authors define total bank risk in terms of earnings volatility, which can be broken down into five major classes of risk: market, credit, asset/liability, operational, and business risks.
read more
Abstract: This paper seeks to put forward a framework, from the perspective of practitioners and policymakers, for how the known, unknown, and unknowable vary by risk type within banking. We define total bank risk in terms of earnings volatility, which can be broken down into five major classes of risk: market, credit, asset/liability, operational, and business risks. For our purposes, risk is known (K) if it can be enumerated, in the sense of being identified, and quantified; it is unknown (U1) if the set of risks can be identified and enumerated but not meaningfully quantified; and it is unknowable (U2) if the existence of the risk or set of risks is not predictable ex ante, let alone quantifiable. Based on these definitions, we position the five sources of bank risk within the K, U1, U2 space based on evidence from industry practice and suggest that K decreases, and U1 and U2 increase, along a spectrum from market risk to credit risk, asset/liability risk, operational risk, and business risk. Using bank-level data we attempt to quantify or size both total bank risk and the contribution from each risk type based on a large sample of earnings volatility data for US bank holding companies over the 1986-2005 period. We find that a) total earnings volatility is protected by minimum regulatory capital requirements at implied credit rating levels ranging from about A- to BBB, depending on the sample; b) when allocating among the different risk types, market risk accounts for only about 5%, credit for almost half, structural interest rate risk for about 18%, and non-financial risks, including both operational and business risk, for about 30% of total risk; c) the diversification benefit, i.e., the difference between the whole and the sum of the parts, is about one-third. Not surprisingly, large banks also seem to experience fewer extreme adverse outcomes.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Hazardous times for monetary policy : What do twenty-three million bank loans say about the effects of monetary policy on credit risk-taking?
TL;DR: In this paper, the authors identify the effects of monetary policy on credit risk-taking with an exhaustive credit register of loan applications and contracts, and find that a lower overnight interest rate induces lowly capitalized banks to grant more loan applications to ex ante risky firms and to commit larger loan volumes with fewer collateral requirements to these firms, yet with a higher ex post likelihood of default.
Asymmetric Information Effects on Loan Spreads
TL;DR: In this article, the authors estimate the economic cost arising from information asymmetry between the lead bank and members of the lending syndicate and find that it has a large economic cost, accounting for approximately 4 percent of the total cost of credit.
612
Hazardous Times for Monetary Policy: What Do Twenty-Three Million Bank Loans Say about the Effects of Monetary Policy on Credit Risk-Taking?
Gabriel Jiménez,Steven Ongena,José-Luis Peydró,José-Luis Peydró,José-Luis Peydró,Jesus Saurina Salas +5 more
TL;DR: In this article, the authors investigate the impact of monetary policy on the level of credit risk of individual bank loans and on lending standards and find that low short-term interest rates prior to loan origination result in banks granting more risky new loans.
Stress-testing US bank holding companies: A dynamic panel quantile regression approach
TL;DR: In this article, an econometric framework for estimating capital shortfalls of bank holding companies (BHCs) under pre-specified macroeconomic scenarios is proposed, based on a fixed effects quantile autoregressive (FE-QAR) model with exogenous macroeconomic covariates, which delivers a superior out-of-sample forecasting performance relative to the standard linear framework.
108
Predicting the unpredictable: Value-at-risk, performativity, and the politics of financial uncertainty
TL;DR: In this paper, the authors identify Value-at-Risk's (counter)performative effects and the way in which it produces banks as authoritative, responsible managers of an uncertain financial future.
101
References
Modeling and forecasting realized volatility
TL;DR: In this article, the authors provide a general framework for integration of high-frequency intraday data into the measurement, modeling, and forecasting of daily and lower frequency volatility and return distributions.
Extreme Correlation of International Equity Markets
François Longin,Bruno Solnik +1 more
TL;DR: This article showed that correlation is not related to market volatility per se but to the market trend and that correlation increases in bear markets, but not in bull markets, and they also showed that the distribution of extreme correlation for a wide class of return distributions can be derived using extreme value theory.
Has Financial Development Made the World Riskier? 1
TL;DR: In this paper, the authors discuss the implications of monetary policy and prudential supervision on financial intermediaries and suggest market-friendly policies that would reduce the incentive of intermediary managers to take excessive risk.
Evaluating density forecasts with applications to financial risk management
TL;DR: In this article, a simple and operational framework for density forecast evaluation is developed, with a detailed application to density forecasting of asset returns in environments with time-varying volatility.
1.3K
Diversification in Banking: Is Noninterest Income the Answer?
Abstract: This paper assesses potential diversification benefits in the U.S. banking industry from the steady shift toward activities that generate fee income, trading revenue, and other types of noninterest income. In the aggregate, declining volatility of net operating revenue reflects reduced volatility of net interest income, not diversification benefits from noninterest income, which is quite volatile and increasingly correlated with net interest income. At the bank level, greater reliance on noninterest income, particularly trading revenue, is associated with lower risk-adjusted profits and higher risk. This suggests few obvious diversification benefits from the ongoing shift toward noninterest income.