A systematic approach to multi-period stress testing of portfolio credit risk

We propose a new method for analysing multi-period stress scenarios for portfolio credit risk more systematically than in current macro stress tests. The plausibility of a scenario is quantified by its distance from an average scenario. For a given level of plausibility, we search systematically for the most adverse scenario. This ensures that no plausible scenario will be missed. We show how this method can be applied to some models already in use by practitioners. While worst case search requires numerical optimisation we show that we can work with reasonably good linear approximations to the portfolio loss function. This makes systematic multi-period stress testing computationally efficient and easy to implement. Applying our approach to data from the Spanish loan register we show that, compared to standard stress test procedures, our method identifies more harmful scenarios that are equally plausible.     

Bayesian Risk Measures for Derivatives via Random Esscher Transform

Abstract

This paper proposes a model for measuring risks for derivatives that is easy to implement and satisfies a set of four coherent properties introduced in Artzner et al. (1999). We construct our model within the context of Gerber-Shiu’s option-pricing framework. A new concept, namely Bayesian Esscher scenarios, which extends the concept of generalized scenarios, is introduced via a random Esscher transform. Our risk measure involves the use of the risk-neutral Bayesian Esscher scenario for pricing and a family of real-world Bayesian Esscher scenarios for risk measurement. Closed-form expressions for our risk measure can be obtained in some special cases.     

Dark Markets,by Darrell Duffie

This paper develops a method for selecting and analysing stress scenarios for financial risk assessment, with particular emphasis on identifying sensible combinations of stresses to multiple factors. We focus primarily on reverse stress testing – finding the most likely scenarios leading to losses exceeding a given threshold. We approach this problem using a nonparametric empirical likelihood estimator of the conditional mean of the underlying market factors given large losses. We then scale confidence regions for the conditional mean by a coefficient that depends on the tails of the market factors to estimate the most likely loss scenarios. We provide rigorous justification for the confidence regions and the scaling procedure when the joint distribution of the market factors and portfolio loss is elliptically contoured. We explicitly characterize the impact of the heaviness of the tails of the distribution, contrasting a broad spectrum of cases including exponential tails and regularly varying tails. The key to this analysis lies in the asymptotics of the conditional variances and covariances in extremes. These results also lead to asymptotics for marginal expected shortfall and the corresponding variance, conditional on a market stress; we combine these results with empirical likelihood significance tests of systemic risk rankings based on marginal expected shortfall in stress scenarios.     

A Theoretical Framework for Incorporating Scenarios into Operational Risk Modeling

In this paper, I introduce a theoretically justified framework that incorporates scenario analysis into operational risk modeling. The basis for the framework is the idea that only worst-case scenarios contain valuable information about the tail behavior of operational losses. In addition, worst-case scenarios introduce a natural order among scenarios that makes possible a comparison of the ordered scenario losses with the corresponding quantiles of the severity distribution that research derives from historical losses. Worst-case scenarios contain information that enters the quantification process in the form of lower bound constraints on the specific quantiles of the severity distribution. The framework gives rise to several alternative approaches to incorporating scenarios.     

Exploring techno-economic scenarios in an input-output model

The dynamic input-output model DIMITRI (Dynamic Input-output Model to study the Impacts of Technology Resulted Innovations) can be used for long-term scenario explorations on technology, demand and environmental effects. The model describes at a sectoral level the relations and dynamics between consumption, production and emissions. Technologies are introduced bottom-up at a sectoral level, through variations on the inputs from other sectors and by changes in the coefficients for capital, labour and emissions. This paper presents a methodology for future explorations of technologies in four scenarios, based on the International Panel on Climate Change (IPCC) scenario framework. Trend analysis combines detailed information on specific technologies. Differentiations are made between scenarios, based on their specific storylines. The adjustment of coefficients influences model outcomes such as production, balance of trade and emissions. This paper briefly outlines the methodology and presents the main outcomes for four scenarios for the period 2000-2030.     

Risk Management Lessons from the Credit Crisis

Risk management, even if flawlessly executed, does not guarantee that big losses will not occur. Big losses can occur because of business decisions and bad luck. Even so, the events of 2007 and 2008 have highlighted serious deficiencies in risk models. For some firms, risk models failed because of known unknowns. These include model risk, liquidity risk, and counterparty risk. In 2008, risk models largely failed due to unknown unknowns, which include regulatory and structural changes in capital markets. Risk management systems need to be improved and place a greater emphasis on stress tests and scenario analysis. In practice, this can only be based on position-based risk measures that are the basis for modern risk measurement architecture. Overall, this crisis has reinforced the importance of risk management.     

Conditional sovereign CDS in market basket risk scenario: A dynamic vine-copula analysis

This study examines the sensitivity of sovereign CDS markets in G7 and BRICS, which is conditional on a joint market basket risk scenario consisting of crude oil, gold, stock indices, exchange rates, freight indices, and copper prices. By compare the conditional and unconditional sovereign CDS returns using dynamic Vine-Copula model, we find that: 1) The conditional sovereign CDS returns will be less than (greater than) the unconditional ones, when scenario settings is at upper (lower) quantile level. Extreme scenario risk level settings (e.g., 1% or 99%) do not always make a significant difference between conditional and unconditional sovereign CDS. 2) Major black swan evens have significant impact on the difference between the conditional and unconditional sovereign CDS, but such an impact is short-lived especially in G7 countries. 3) Taking into account of the covariate effects, the conditional risk scenarios of sovereign CDS are heterogeneous across countries, down- and up-ward tail as well risk factors associated with the market basket.     

Portfolio Analysis with Factors and Scenarios

Recently there has been a growing interest in the scenario model of covariance as an alternative to the one-factor or many-factor models. We show how the covariance matrix resulting from the scenario model can easily be made diagonal by adding new variables linearly related to the amounts invested; note the meanings of these new variables; note how portfolio variance divides itself into “within scenario” and “between scenario” variances; and extend the results to models in which scenarios and factors both appear where factor distributions and effects may or may not be scenario sensitive.     

基于外部信息的信托公司 信用风险压力测试案例分析

鉴于目前信托公司在压力测试方法应用上的缺乏,本文基于宏观经济变量与不良 贷款率的相关性,建立了宏观经济波动冲击对信托公司信用风险影响的压力测试方法,并以某 信托公司为例进行案例分析。该压力测试方法较为有效地测试出了某信托公司面临极端压力情 景时的风险承受能力。为提高压力测试的精确度,可结合业务的行业结构进行多个压力测试, 并按业务比例进行加权汇总。     

Comprehensive scenario analysis for mitigation of risks of the maritime traffic in the Strait of Istanbul

In this paper, the results of a scenario analysis are presented using the models developed by the authors in their prior work. Therein an aggregate risk-simulation model of the maritime traffic in the Strait of Istanbul was developed giving consideration to traffic rules, vessel profiles, pilotage services, meteorological, geographical conditions, and vessel scheduling. A risk assessment was carried out by incorporating a probabilistic accident risk model into the simulation model to establish a baseline level of risk. Herein, 16 scenarios are described as modifications of the baseline scenario for the purpose of studying maritime risk mitigation in this geographic context. We have evaluated the impact of several factors such as vessel arrival rates, vessel pursuit distances, number of pilots, local traffic density, and vessel scheduling practices such as the single-lane traffic regime start time on safety risks. We also present a discussion on the impact of each factor on vessel passages and waiting times at both entrances of the Strait. We conclude with risk mitigation recommendations that reduce both risk and waiting times, and thus can be considered ‘win-win’ from both an operational and risk reduction perspective.     

Macroprudential Stress-Testing Practices of Central Banks in Central and Southeastern Europe: Comparison and Challenges Ahead

This paper reviews and compares stress-testing practices of central banks in Central and Southeastern Europe (CSEECBs) and outlines challenges in the area of stress testing going forward. The authors, focusing their comparison on CSEECBs, construct the baseline and stress scenarios, map macroeconomic scenarios and microeconomic factors to risk factors, calculate risk exposures to different risk indicators, and estimate outcome indicators to inform macroprudential policy. The main challenges going forward concern data reliability, consideration of quantitative microprudential indicators, incorporation of feedback effects in stress tests, institutionalization of macroprudential policy responses to alarming stress-test results, and information exchange for better cross-border supervision.     

SCENARIO GENERATION FOR OPERATIONAL RISK

Operational risk is an increasingly important area of risk management. Scenarios are an important modelling tool in operational risk management as alternative viable methods may not exist. This can be due to challenging modelling, data and implementation issues, and other methods fail to take into account expert information. The use of scenarios has been recommended by regulators; however, scenarios can be unreliable, unrealistic and fail to take into account quantitative data. These problems have also been identified by regulators such as Basel, and presently little literature exists on addressing the problem of generating scenarios for operational risk. In this paper we propose a method for generating operational risk scenarios. We employ the method of cluster analysis to generate scenarios that enable one to combine expert opinion scenarios with quantitative operational risk data. We show that this scenario generation method leads to significantly improved scenarios and significant advantages for operational risk applications. In particular for operational risk modelling, our method leads to resolving the key problem of combining two sources of information without eliminating the information content gained from expert opinions, tractable computational implementation for operational risk modelling, improved stress testing, what‐if analyses and the ability to apply our method to a wide range of quantitative operational risk data (including multivariate distributions). We conduct numerical experiments on our method to demonstrate and validate its performance and compare it against scenarios generated from statistical property matching for comparison. Copyright © 2013 John Wiley & Sons, Ltd.     

An examination of future scenarios using historical analogy

The quality of scenario planning activities can be difficult to assess, as one cannot know how likely any projected future scenario is. Here, we introduce one approach for gaining greater confidence. Historical analogy provides the means for achieving this, whereby the model upon which scenarios are constructed is analysed in terms of how well it predicts and establishes links with recent historical environments. We apply this approach to a previously developed scenario tree, constructed using the field anomaly relaxation method, as a case study to indicate how historical analogy can be used to assess and enhance the model from which the scenarios are constructed.     

Multi-period stochastic optimization models for dynamic asset allocation

Institutional investors manage their strategic asset mix over time to achieve favorable returns subject to various uncertainties, policy and legal constraints, and other requirements. One may use a multi-period portfolio optimization model in order to determine an optimal asset mix. The concept of scenarios is typically employed for modeling random parameters in a multi-period stochastic programming model, and scenarios are constructed via a tree structure. Recently, an alternative stochastic programming model with simulated paths was proposed by Hibiki [Hibiki, N., 2001b. A hybrid simulation/tree multi-period stochastic programming model for optimal asset allocation. In: Takahashi, H. (Ed.), The Japanese Association of Financial Econometrics and Engineering. JAFEE Journal 89–119 (in Japanese); Hibiki, N., 2003. A hybrid simulation/tree stochastic optimization model for dynamic asset allocation. In: Scherer, B. (Ed.), Asset and Liability Management Tools: A Handbook for Best Practice, Risk Books, pp. 269–294], and it is called a hybrid model. The advantage of the simulated path structure compared to the tree structure is to give a better accuracy to describe uncertainties of asset returns. In this paper, we compare the two types of multi-period stochastic optimization models, and clarify that the hybrid model can evaluate and control risk better than the scenario tree model using some numerical tests. According to the numerical results, an efficient frontier of the hybrid model with the fixed-proportion strategy dominates that of the scenario tree model when we evaluate them on simulated paths. Moreover, optimal solutions of the hybrid model are more appropriate than those of the scenario tree model.     

Are people emotionally aroused by hypothetical medical scenarios in experiments? An eye tracking study with pupil dilation

Background: Many studies on the communication of medical risks use hypothetical medical scenarios. The results of these scenarios should have sufficient predictive accuracy to be generalized to real life; thus, it is important to know whether hypothetical medical scenarios work and whether there is a relationship between risk level and emotional arousal. Methods: In an eye tracking experiment (N = 67), we investigated the influence of a simple hypothetical medical scenario on pupil dilation, a measure of emotional arousal. In this medical scenario, the participants were shown three risk levels (low, middle, and high) and had to estimate the probability that a hypothetical patient has colon cancer. They were also given a non-medical scenario that controlled for changes in illumination and cognitive workload. Therefore, we supposed that the difference in pupil diameter between the medical and the non-medical scenario was due to emotional arousal. Results: We found that our hypothetical medical scenario had a significant effect on pupil diameter. The mean values of the mean pupil diameter in the first fifth of the fixations were higher for all risk levels in the medical scenario than in the non-medical scenario. In a more detailed analysis of the difference in pupil diameters between the two scenarios, we detected that, for the high-risk level, the emotional difference values (between the medical and non-medical scenarios) differed significantly from zero. Furthermore, we found that higher risk levels lead to higher emotional arousal and higher probability estimates. Conclusions: Even simple hypothetical medical scenarios cause emotional arousal. Thus, hypothetical medical scenarios work, and the results of studies not using real patients can be generalized to real medical situations.     

Subjective Economic Risk to Beneficiaries in Notional Defined Contribution Accounts

This article aims to quantify the aggregate subjective economic risk to which beneficiaries would be exposed if a retirement pension system based on notional account philosophy were introduced. We use scenario generation techniques to make projections of the factors that determine the real expected internal rate of return (IRR) and the expected replacement rate (RR) for the beneficiary according to six retirement formulae based on the most widely accepted rates or indices. We then apply the model to the case of Spain. Our projections are based on Herce and Alonso's macroeconomic scenario 2000–2050 (2000) and include information about the past performance of the indices and the time period the forecast is to cover. The results of the IRR calculation—average value, standard deviation, and value‐at‐risk (VaR)—are analyzed both in objective terms and for different degrees of participants' risk aversion.     

Futures of privacy protection: A framework for creating scenarios of institutional change

The future of privacy is a topical issue in the context of debates on mass surveillance and the increasing prevalence of social media sites in everyday life. Previous scenario studies on privacy have focused on macro trends and on forecasting technological developments, and claims about causal influences have remained implicit. This article presents an alternative approach for constructing scenarios of privacy protection. The article focuses on privacy protection as a social institution and builds on the theory of gradual institutional change. The article presents a scenario framework which includes three stages: (1) outlining the dynamics of privacy protection, (2) tracing historical processes and constructing a causal narrative, and (3) creating event-based scenarios. The resulting scenarios are narratives of plausible chains of events which are based on the results of the previous stages. The key difference to typical scenario approaches is the focus on specific actors and types of event sequences in privacy protection. The argument is that by lowering the level of abstraction in this way, researchers and decision-makers can gain a more profound understanding of possible future challenges in privacy protection and of key leverage points in the institutional change process.     

M‐PRESS‐CreditRisk: Microprudential and Macroprudential Capital Requirements for Credit Risk under Systemic Stress

M‐PRESS‐CreditRisk is a novel stress testing approach that can help authorities gauge banks' capital adequacy related to credit risk. For the first time, it combines the assessment of microprudential capital requirements under Pillars 1 and 2 and macroprudential buffers in a unified, coherent framework. Its core element is an advanced credit portfolio model—SystemicCreditRisk—built upon a rich, nonlinear dependence structure for correlated bank portfolios. The model is applied to a sample of 12 systemically important German banking groups and delivers measures for systemic credit risk and the banks' contributions to it in both baseline and stress scenarios.     

我国银行业信用风险宏观压力测试研究

本文通过实证研究提出并论证了一种宏观压力测试方法,该方法可用于银行业监管和系统性风险的防范.首先采用有序多分类Logistic模型测算行业原始违约概率,再运用MFD违约概率模型将宏观冲击因子引入以求得渗入宏观经济因子的违约概率,然后采用CreditRisk+模型分别测算不同宏观压力情景下与信用风险对应的经济资本变化,经...     

The Coordination Role of Stress Tests in Bank Risk‐Taking

We examine whether stress tests distort banks' risk‐taking decisions. We study a model in which a regulator may choose to rescue banks in the event of concurrent bank failures. Our analysis reveals a novel coordination role of stress tests. Disclosure of stress‐test results informs banks of the failure likelihood of other banks, which can reduce welfare by facilitating banks' coordination in risk‐taking. However, conducting stress tests also enables the regulator to more effectively intervene banks, coordinating them preemptively into taking lower risks. We find that, if the regulator has a strong incentive to bail out, stress tests improve welfare, whereas if the regulator's incentive to bail out is weak, stress tests impair welfare.