Stress Test Scenarios Research Articles

Stress Testing: Observations of Its Possible Effect

This article examines important aspects of regulatory stress testing. It states that the way stress testing is implemented might increase systemic risk faced by the banking industry. It also states that despite the effort of using uniform stress testing scenarios, the results are not transparent and uniform for different banks due to the fact that different empirical models behave materially different when stressed parameters are used as an input. Several improvements are suggested in the article.

A Rolling Optimisation Model of the UK Natural Gas Market

Daily gas demand in the UK is variable. This is partly due to weather patterns and the changing nature of electricity markets, where intermittent wind energy levels lead to variations in the demand for gas needed to produce electricity. This uncertainty makes it difficult for traders in the market to analyse the market. As a result, there is an increasing need for models of the UK natural gas market that include stochastic demand. In this paper, a Rolling Optimisation Model (ROM) of the UK natural gas market is introduced. It takes as an input stochastically generated scenarios of demand. The outputs of ROM are the flows of gas, i.e., how the different sources of supply meet demand, as well as how gas flows in to and out of gas storage facilities. The outputs also include the daily System Average Price of gas in the UK. The model was found to fit reasonably well to historic data (from the UK National Grid) for the years starting on the 1st of April for both 2010 and 2011. These results allow ROM to be used to predict future flows and prices of gas and to investigate various stress-test scenarios in the UK natural gas market.

People’s Republic of China–Hong Kong Special Administrative Region: Financial Sector Assessment Program-Stress Testing the Banking Sector-Technical Note

This Technical Note on Stress Testing the Banking Supervision was prepared in the context of the Financial Sector Assessment Program (FSAP) for the People’s Republic of China–Hong Kong Special Administrative Region (HKSAR). Bank liquidity tests focus on sudden, sizable withdrawals of funding and the sufficiency of existing assets to withstand those shocks under stressed conditions. The stress test results confirm a high degree of resilience of the sector. This reflects the strength of the banks at the starting position, which reduces their fundamental vulnerability to shocks. Banks in HKSAR hold very high levels of capital, are very profitable, and have a low level of asset impairments amid stable funding profiles. The Hong Kong Monetary Authority is encouraged to continue its integration of risk-based supervision in the development of stress test scenarios for macroprudential policy and surveillance. Banking supervisors routinely conduct stress tests and, from time to time, modify relevant assumptions in order to support thematic reviews of identified vulnerabilities against emerging risks.

Stress test design

This paper offers thoughts and ideas on the implementation of a company-specific stress test programme and focuses on the design of the stress test scenarios. In particular, it discusses the integration and coherence of the macroeconomic, market, credit, counterparty and operational risk scenarios. Certain specific features are recommended to enhance the stress test of market, counterparty and operational risks: the explicit consideration of the hedging and liquidity dynamics of trading portfolios, automated reverse stress testing for identification of market risk vulnerabilities, simulation of dynamic hedging costs of credit valuation adjustments (CVAs) and the averaging across different models to mitigate misspecification error and obtain more reliable estimates of stressed operational losses. All the above is framed within the context of the Federal Reserve's Comprehensive Capital Analysis and Review (CCAR) programme.

Synthetic Hardware Performance Analysis in Virtualized Cloud Environment for Healthcare Organization

The main obstacles in mass adoption of cloud computing for database operations in healthcare organization are the data security and privacy issues. In this paper, it is shown that IT services particularly in hardware performance evaluation in virtual machine can be accomplished effectively without IT personnel gaining access to actual data for diagnostic and remediation purposes. The proposed mechanisms utilized the hypothetical data from TPC-H benchmark, to achieve 2 objectives. First, the underlying hardware performance and consistency is monitored via a control system, which is constructed using TPC-H queries. Second, the mechanism to construct stress-testing scenario is envisaged in the host, using a single or combination of TPC-H queries, so that the resource threshold point can be verified, if the virtual machine is still capable of serving critical transactions at this constraining juncture. This threshold point uses server run queue size as input parameter, and it serves 2 purposes: It provides the boundary threshold to the control system, so that periodic learning of the synthetic data sets for performance evaluation does not reach the host's constraint level. Secondly, when the host undergoes hardware change, stress-testing scenarios are simulated in the host by loading up to this resource threshold level, for subsequent response time verification from real and critical transactions.

Calibrating Initial Shocks in Bank Stress Test Scenarios: An Outlier Detection Based Approach

We propose a rigorous and flexible methodological framework to select and calibrate initial shocks to be used in bank stress test scenarios based on statistical techniques for detecting outliers in time series of risk factors. Our approach allows us to characterize not only the magnitude, but also the persistence of the initial shock. The stress testing exercises regularly conducted by supervisors distinguish between two types of shocks, transitory and permanent. One of the main advantages of our framework, particularly relevant as regards the calibration of transitory shocks, is that it allows considering various reverting patterns for the stressed variables and informs the choice of the appropriate stress horizon. We illustrate the proposed methodology by implementing outlier detection algorithms to several time series of (macro)economic and financial variables typically used in bank stress testing.

Secure Hardware Performance Analysis in Virtualized Cloud Environment

The main obstacle in mass adoption of cloud computing for database operations is the data security issue. In this paper, it is shown that IT services particularly in hardware performance evaluation invirtual machinecan be accomplished effectively without IT personnel gaining access to real data for diagnostic and remediation purposes. The proposed mechanisms utilizedTPC-Hbenchmark to achieve 2 objectives. First, the underlying hardware performance and consistency is supervised via a control system, which is constructed using a combination ofTPC-Hqueries,linear regression, andmachine learningtechniques. Second,linear programmingtechniques are employed to provide input to the algorithms that construct stress-testing scenarios in thevirtual machine, using the combination ofTPC-Hqueries. These stress-testing scenarios serve 2 purposes. They provide the boundary resource threshold verification to the first control system, so that periodic training of the synthetic data sets for performance evaluation is not constrained by hardware inadequacy, particularly when the resources in thevirtual machineare scaled up or down which results in the change of the utilization threshold. Secondly, they provide a platform for response time verification on critical transactions, so that the expected Quality of Service (QoS) from these transactions is assured.

Calibrating Initial Shocks in Bank Stress Test Scenarios: An Outlier Detection Based Approach

We propose a rigorous and flexible methodological framework to select and calibrate initial shocks to be used in bank stress test scenarios based on statistical techniques for detecting outliers in time series of risk factors. Our approach allows us to characterize not only the magnitude, but also the persistence of the initial shock. The stress testing exercises regularly conducted by supervisors distinguish between two types of shocks, transitory and permanent. One of the main advantages of our framework, particularly relevant as regards the calibration of transitory shocks, is that it allows considering various reverting patterns for the stressed variables and informs the choice of the appropriate stress horizon. We illustrate the proposed methodology by implementing outlier detection algorithms to several time series of (macro)economic and financial variables typically used in bank stress testing.

Further recipes for quantitative reverse stress testing

As a consequence of the 2007-9 financial crisis, the regulatory authorities now oblige banks to carry out reverse stress tests. This new instrument in the stress test toolbox aims to find those scenarios that cause a bank to cross the frontier between survival and default. Afterward, the scenario that is most likely to occur has to be identified. In a 2011 paper by Grundke it is argued that bottom-up approaches, which are a specific integrated risk measurement technique, are basically well suited to solving the inversion problem inherent in a reverse stress test and, in particular, for computing the probabilities of reverse stress test scenarios while taking existing risk dependencies into account. Building upon the earlier work of Grundke, this paper shows how the modeling framework previously presented can be extended to incorporate more real-world features such as a time-varying credit quality of the bank or contagion effects. The consequences on the results of the reverse stress test are analyzed for each modification.

Survey of Research on Financial Sector Modeling within DSGE Models: What Central Banks Can Learn from It

This survey gives insight into the ongoing research in financial frictions modeling. The recent financial turmoil has fueled interest in operationalizing financial frictions concepts and introducing them into tools for policy makers. The rapid growth of the literature on these issues is the motivation for our review of the presented approaches. The empirical facts that motivate the inclusion of financial frictions are surveyed. This survey provides a description of the basic approaches for introducing financial frictions into dynamic stochastic general equilibrium models. The significance and empirical identification of the financial accelerator effect is then discussed. The role of financial frictions models in CNB monetary and macroprudential policy is also described. It is concluded that given the heterogeneity of the approaches to financial frictions it is beneficial for the conduct of monetary policy to focus on the development of satellite approaches. The role of financial frictions in DSGE models for macroprudential policy is also discussed, as these models can be used to generate stress-testing scenarios. It can be concluded that DSGE models with financial frictions could complement current stress-testing practice, but are not able to replace stress tests.

Systemic Risk Contributions

We adopt a systemic risk indicator measured by the price of insurance against systemic financial distress and assess individual banks’ marginal contributions to the systemic risk. The methodology is applied using publicly available data to the 19 bank holding companies covered by the U.S. Supervisory Capital Assessment Program (SCAP), with the systemic risk indicator peaking around $1.1 trillion in March 2009. Our systemic risk contribution measure shows interesting similarity to and divergence from the SCAP loss estimates under stress test scenarios. In general, we find that a bank’s contribution to the systemic risk is roughly linear in its default probability but highly nonlinear with respect to institution size and asset correlation.

Using Monte Carlo simulations to establish a new house price stress test

The focus of this paper is on the house price stress test (termed ALMO) that was designed to assess the fiscal strength of Fannie Mae and Freddie Mac and, if necessary, to trigger remedial action in order to avert a crisis. We assess whether the ALMO stress test was an adequate representation of an extremely weak housing market, given the best available information leading up to the Great Recession. A Monte Carlo simulation model is developed to estimate the severity of low probability events (i.e., severe house price declines). We illustrate the complexity and subjective nature of the process used to generate a plausible house price stress test scenarios. A major finding is that the ALMO stress test scenario severely understated (possibly by 50% or more) what an updated statistical process would have suggested. Part of this stems from idiosyncrasies related to the construction and implementation of ALMO, while other factors include a fundamental shift in the relationship between housing price appreciation and key explanatory variables – especially over the past 10–15 years, which shows a heightened role of momentum in explaining changes in housing prices. We offer several suggestions for a new stress test that include: continual updates and testing; variation across markets; and, like the recent FRB stress test, the scenario should be based on real (rather than nominal) price patterns.

Modeling default probabilities: The case of Brazil

Using disaggregated data from the Brazilian stock market, we calculate default probabilities for 30 different economic sectors. Empirical results suggest that domestic macroeconomic factors can explain these default probabilities. In addition, we construct the Minimum Spanning Tree (MST) and the ultrametric hierarchical tree with the MST based on default probabilities to disclose common trends, which reveals that some sectors form clusters. The results of this paper imply that macroeconomic variables have distinct effects on default probabilities, which is important to take into account in credit risk modeling and the generation of stress test scenarios.

Enhancing the Black–Litterman and related approaches: Views and stress-test on risk factors

The Black–Litterman and related approaches modify the return distribution of a normally distributed market according to views or stress-test scenarios. We discuss how to broaden the range of applications of these approaches significantly by letting them act on the risk factors underlying the market, instead of the returns of the securities. Code implementing the models discussed here can be found at www.symmys.com>Teaching>MATLAB.

Enhancing the Black-Litterman and Related Approaches: Views and Stress-Test on Risk Factors

The Black-Litterman and related approaches modify the return distribution of a normally distributed market according to views or stress-test scenarios. We discuss how to broaden the range of applications of these approaches significantly by letting them act on the risk factors underlying the market, instead of the returns of the securities.

Emerging versus Developed Volatility Indexes: The Comparison of VIW20 and VIX Index

Modeling of financial markets volatility is one of the most significant issues of contemporary finance, especially while analyzing high-frequency data. Accurate quantification and forecast of volatility are of immense importance in risk management (VaR models, stress testing and worst case scenario), models of capital market and options valuation techniques. What we show in this paper is the methodology for calculating volatility index for Polish capital market (VIW20 - index anticipating expected volatility of WIG20 Index). The methods presented are based on VIX Index (VIX White Paper, 2003) and enriched with necessary modifications corresponding with the character of Polish options market. Quoted on CBOE, VIX Index is currently known as the best measure of capital investment risk perfectly illustrating the level of fear and emotions of market participants. The conception of volatility index is based on combination of realized volatility and implied volatility which, using methodology of Derman et al. (1999) and reconstructing volatility surface, reflects both volatility smile as well as its term structure. The research is carried out using high-frequency data (i.e. tick data) for index options on WIG20 Index for the period November 2003 - May 2007, in other words, starting with the introduction of options by Warsaw Stock Exchange. All additional simulations are carried out using data comprising 1998-2008. Having analyzed in detail VIW20 Index, we observed its characteristic behavior during the periods of strong market turmoils. What we also present is the analysis of the influence of VIW20 and VIX index-based instruments both on construction of minimum risk portfolio and the quality of derivatives portfolio management where volatility risk and liquidity risk play a key role. The main objective of this paper is to provide foundations for introducing appropriate volatility indices and volatility-based derivatives. All that paying attention to crucial methodology changes, necessary if one considers strong markets inefficiencies in emerging countries. As the introduction of appropriate instruments will enable active management of risks that are unhedgable nowadays it will significantly contribute to the development of the given markets in the course of time. In summary we additionally point to benefits Warsaw Stock Exchange might get, being one of few emerging markets possessing appropriately quantified investment risk as well as derivatives to manage it.

Introduction to Applied Stress Testing

Stress testing is a useful and increasingly popular, yet sometimes misunderstood, method of analyzing the resilience of financial systems to adverse events. This paper aims to help demystify stress tests, and illustrate their strengths and weaknesses. Using an Excel-based exercise with institution-by-institution data, readers are walked through stress testing for credit risk, interest rate and exchange rate risks, liquidity risk and contagion risk, and are guided in the design of stress testing scenarios. The paper also describes the links between stress testing and other analytical tools, such as financial soundness indicators and supervisory early warning systems. Furthermore, it includes surveys of stress testing practices in central banks and the IMF.

Introduction to Applied Stress Testing

Stress testing is a useful and increasingly popular, yet sometimes misunderstood, method of analyzing the resilience of financial systems to adverse events. This paper aims to help demystify stress tests, and illustrate their strengths and weaknesses. Using an Excel-based exercise with institution-by-institution data, readers are walked through stress testing for credit risk, interest rate and exchange rate risks, liquidity risk and contagion risk, and are guided in the design of stress testing scenarios. The paper also describes the links between stress testing and other analytical tools, such as financial soundness indicators and supervisory early warning systems. Furthermore, it includes surveys of stress testing practices in central banks and the IMF.

A note on adjusting correlation matrices

A new algorithm for adjusting correlation matrices and for comparison with Finger's algorithm, which is used to compute Value-at-Risk in RiskMetrics for stress test scenarios. The solution proposed by the new methodology is always better than Finger's approach in the sense that it alters as little as possible those correlations that one would wish not to alter, but they change in order to obtain a consistent Finger correlation matrix.

Risk Management: One Institution's Experience

I am very pleased to be part of this forward-looking conference on developments in capital regulation. Because the purpose of capital is to support risk, I decided to approach this session from the viewpoint of someone leading an institution that depends, for its success or failure, on how well it manages risk. My plan is to take you through my experiences at Chase Manhattan Corporation and to close with some thoughts on the implications of these experiences for capital regulation in the twenty-first century. What I am going to describe to you is a dynamic approach to risk management, though not a perfect one. We continually make improvements, and we need to. Nevertheless, if I look back on the last six months--and the Asian crisis that has dominated this period--I would argue that never during this time did I feel that we had failed to understand the risks we were facing. In addition, I feel fairly confident that our regulators have a reasonably good understanding of the systems we use, and that, in the event of a crisis, these regulators would have access to daily information if they needed it. Let me speak for a minute about market risk. There has been considerable discussion at this conference about the limitations of the value-at-risk approach to risk measurement. This approach is, of course, imperfect: it is built on the same kinds of assumptions that we all use routinely in our work. In my view, value at risk is important, but it cannot stand alone. At Chase, we calculate our exposure to market risk by using both a value-at-risk system and a stress-test system. These systems apply to both the mark-to-market portfolio and the accrual portfolios. We use this combination of approaches to set limits on the risks we undertake and to assign capital to cover our exposures. We came into 1997 with five stress-test scenarios built into our systems: the October 1987 stock market crash, the 1992 exchange rate mechanism crisis, the March 1994 bond market sell-off, the December 1994 peso crisis, and a hypothetical flight-to-quality scenario. We are currently expanding this set of scenarios to include four new prospective scenarios. In developing at least three of these four, we will have to use our judgment to predict how currencies, interest rates, and markets would be affected. By contrast, in the case of four of the five scenarios now in use, we already know the outcome. Our risk limits in 1997, and certainly into early 1998, have been set by assessing our risks against these stress scenarios and the value-at-risk system. In fact, in the last year, the balance between the two approaches to risk management has probably moved more to the center. In any case, this combination of approaches has enabled us to manage market risk successfully. Now, turning briefly to credit risk, let me review how our institution handles it. First, at Chase, we monitor individual transactions from several angles. We examine not only how the transaction is structured but also how it measures up against our lending standards. In this regard, an independent risk-rating process for applying and verifying risk ratings--one that is entirely independent of the units that actually carry out the bank's business--is an essential part of the credit review process at Chase. We also decide, at the time of the transaction, which credits we plan to hold in our portfolio and which we plan to sell into the market. Finally, we determine the contribution that each transaction makes to the overall risk of the portfolio because that contribution forms the basis of the capital allocation process. Second, we identify and control credit risk by looking carefully at portfolio concentrations. Many of the crises of the 1980s--the real estate crisis, the savings and loan failures, the debt buildup in developing countries--can be traced to a failure to monitor portfolio concentrations. Recognizing these concentrations--for instance, by industry or by country--is a key element of understanding the true risks of the credit portfolio. …