The financial crisis forced the development of new approaches for determining capital adequacy in banks since extant methods clearly did not prepare banks or their supervisors sufficiently. The ...success of stress testing as a crisis response tool, particularly in the U.S. in 2009, has led to its adoption postcrisis as the tool of choice for assessing capital adequacy in banks and testing resiliency to economic and financial shocks. But the increased reliance on stress testing in financial peacetime has given rise to a new risk concentration, namely, in the rather narrow set of scenarios and their translation to outcomes and impact on bank financials.
Stress testing banks Schuermann, Til
International journal of forecasting,
07/2014, Letnik:
30, Številka:
3
Journal Article
Recenzirano
How much capital and liquidity does a bank need to support its risk taking activities? During the recent (and still ongoing) financial crisis, answers to this question using standard approaches, ...e.g., regulatory capital ratios, were no longer credible, and thus broad-based supervisory stress testing became the new tool. Bank balance sheets are notoriously opaque and susceptible to asset substitution (easy swapping of high risk for low risk assets), so stress tests, tailored to the situation at hand, can provide clarity by openly disclosing details of the results and approaches taken, allowing trust to be regained. With that trust re-established, the cost-benefit of stress testing disclosures may tip away from bank-specific towards more aggregated information. This paper lays out a framework for the stress testing of banks: why it is useful and why it has become such a popular tool for the regulatory community in the course of the recent financial crisis; how stress testing is done (design and execution); and finally, with stress testing results in hand, how one should handle their disclosure, and whether it should be different in crisis vs. “normal” times.
Despite mounting evidence to the contrary, credit migration matrices, used in many credit risk and pricing applications, are typically assumed to be generated by a simple Markov process. Based on ...empirical evidence, we propose a parsimonious model that is a mixture of (two) Markov chains, where the mixing is on the speed of movement among credit ratings. We estimate this model using credit rating histories and show that the mixture model statistically dominates the simple Markov model and that the differences between two models can be economically meaningful. The non-Markov property of our model implies that the future distribution of a firm’s ratings depends not only on its current rating but also on its past rating history. Indeed we find that two firms with identical current credit ratings can have substantially different transition probability vectors. We also find that conditioning on the state of the business cycle or industry group does not remove the heterogeneity with respect to the rate of movement. We go on to compare the performance of mixture and Markov chain using out-of-sample predictions.
Liquidity risk in banking has been attributed to transactions deposits and their potential to spark runs or panics. We show instead that transactions deposits help banks hedge liquidity risk from ...unused loan commitments. Bank stock-return volatility increases with unused commitments, but only for banks with low levels of transactions deposits. This depositlending hedge becomes more powerful during periods of tight liquidity, when nervous investors move funds into their banks. Our results reverse the standard notion of liquidity risk at banks, where runs from depositors had been seen as the cause of trouble.
Integrated risk management for financial institutions requires an approach for aggregating risk types (market, credit, and operational) whose distributional shapes vary considerably. We construct the ...joint risk distribution for a typical large, internationally active bank using the method of copulas. This technique allows us to incorporate realistic marginal distributions that capture essential empirical features of these risks such as skewness and fat-tails while allowing for a rich dependence structure. We explore the impact of business mix and inter-risk correlations on total risk. We then compare the copula-based method with several conventional approaches to computing risk.
This paper considers the problem of forecasting economic and financial variables across a large number of countries in the global economy. To this end a global vector autoregressive (GVAR) model, ...previously estimated by Dees, di Mauro, Pesaran, and Smith (2007) and Dees, Holly, Pesaran, and Smith (2007) over the period 1979Q1–2003Q4, is used to generate out-of-sample forecasts one and four quarters ahead for real output, inflation, real equity prices, exchange rates and interest rates over the period 2004Q1–2005Q4. Forecasts are obtained for 134 variables from 26 regions, which are made up of 33 countries and cover about 90% of the world output. The forecasts are compared to typical benchmarks: univariate autoregressive and random walk models. Building on the forecast combination literature, the effects of model and estimation uncertainty on forecast outcomes are examined by pooling forecasts obtained from different GVAR models estimated over alternative sample periods. Given the size of the modelling problem, and the heterogeneity of the economies considered–industrialised, emerging, and less developed countries–as well as the very real likelihood of possibly multiple structural breaks, averaging forecasts across both models and windows makes a significant difference. Indeed, the double-averaged GVAR forecasts perform better than the benchmark competitors, especially for output, inflation and real equity prices.
Financial institutions are ultimately exposed to macroeconomic fluctuations in the global economy. This article proposes and builds a compact global model capable of generating forecasts for a core ...set of macroeconomic factors (or variables) across a number of countries. The model explicitly allows for the interdependencies that exist between national and international factors. Individual region-specific vector error-correcting models are estimated in which the domestic variables are related to corresponding foreign variables constructed exclusively to match the international trade pattern of the country under consideration. The individual country models are then linked in a consistent and cohesive manner to generate forecasts for all of the variables in the world economy simultaneously. The global model is estimated for 25 countries grouped into 11 regions using quarterly data over 1979Q1-1999Q1. The degree of regional interdependencies is investigated via generalized impulse responses where the effects of shocks to a given variable in a given country on the rest of the world are provided. The model is then used to investigate the effects of various global risk scenarios on a bank's loan portfolio.
Credit migration matrices are cardinal inputs to many risk management applications; their accurate estimation is therefore critical. We explore two approaches: cohort and two variants of duration – ...one imposing, the other relaxing time homogeneity – and the resulting differences, both statistically through matrix norms and economically using a credit portfolio model. We propose a new metric for comparing these matrices based on singular values and apply it to credit rating histories of S&P rated US firms from 1981–2002. We show that the migration matrices have been increasing in “size” since the mid-1990s, with 2002 being the “largest” in the sense of being the most dynamic. We develop a testing procedure using bootstrap techniques to assess statistically the differences between migration matrices as represented by our metric. We demonstrate that it can matter substantially which estimation method is chosen: economic credit risk capital differences implied by different estimation techniques can be as large as differences between economic regimes, recession vs. expansion. Ignoring the efficiency gain inherent in the duration methods by using the cohort method instead is more damaging than imposing a (possibly false) assumption of time homogeneity.
In this paper we conduct a systematic comparison of confidence intervals around estimated probabilities of default (PD) using several analytical approaches as well as parametric and nonparametric ...bootstrap methods. We do so for two different PD estimation methods, cohort and duration (intensity), with 22 years of credit ratings data. We find that the bootstrapped intervals for the duration-based estimates are relatively tight when compared to either analytic or bootstrapped intervals around the less efficient cohort estimator. We show how the large differences between the point estimates and confidence intervals of these two estimators are consistent with non-Markovian migration behavior. Surprisingly, even with these relatively tight confidence intervals, it is impossible to distinguish notch-level PDs for investment grade ratings, e.g. a PDAA− from a PDA+. However, once the speculative grade barrier is crossed, we are able to distinguish quite cleanly notch-level estimated PDs. Conditioning on the state of the business cycle helps: it is easier to distinguish adjacent PDs in recessions than in expansions.
The turmoil in the capital markets in 1997 and 1998 has highlighted the need for systematic stress testing of banks' portfolios, including both their trading and lending books. We propose that ...underlying macroeconomic volatility is a key part of a useful conceptual framework for stress testing credit portfolios, and that credit migration matrices provide the specific linkages between underlying macroeconomic conditions and asset quality. Credit migration matrices, which characterize the expected changes in credit quality of obligors, are cardinal inputs to many applications, including portfolio risk assessment, modeling the term structure of credit risk premia, and pricing of credit derivatives. They are also an integral part of many of the credit portfolio models used by financial institutions. By separating the economy into two states or regimes, expansion and contraction, and conditioning the migration matrix on these states, we show that the loss distribution of credit portfolios can differ greatly, as can the concomitant level of economic capital to be assigned.
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