New Economics Papers
on Risk Management
Issue of 2008‒07‒30
seven papers chosen by

  1. Risk Measurement and Management in a Crisis-Prone World By Wong, Woon K; Copeland, Laurence
  2. Credit risk models: why they failed in the credit crisis By Wilson Sy
  3. Credit risk and business cycle over different regimes By Juri Marcucci; Mario Quagliariello
  4. Conditional Loss Estimation Using a South African Global Error Correcting Macroeconometric Model By Albert H. De Wet; Renee Van Eyden; Rangan Gupta
  5. Innovations in credit risk transfer: implications for financial stability By Darrell Duffie
  6. Co-integration and Causality Among Jakarta Stock Exchange, Singapore Stock Exchange, and Kuala Lumpur Stock Exchange By Febrian, Erie; Herwany, Aldrin
  7. The Credit Risk Premium in a Disaster-Prone World By Zhu, Yanhui; Copeland, Laurence

  1. By: Wong, Woon K (Cardiff Business School); Copeland, Laurence (Cardiff Business School)
    Abstract: The current subprime crisis has prompted us to look again into the nature of risk at the tail of the distribution. In particular, we investigate the risk contribution of an asset, which has infrequent but huge losses, to a portfolio using two risk measures, namely Value-at-Risk (VaR) and Expected Shortfall (ES). While ES is found to measure the tail risk contribution effectively, VaR is consistent with intuition only if the underlying return distribution is well behaved. To facilitate the use of ES, we present a power function formula that can calculate accurately the critical values of the ES test statistic. This in turn enables us to derive a size-based multiplication factor for risk capital requirement.
    Keywords: Value-at-Risk; expected shortfall; tail risk contribution; saddle point technique; risk capital
    JEL: G11 G32
    Date: 2008–07
  2. By: Wilson Sy (Australian Prudential Regulation Authority)
    Abstract: Abstract: Credit risk models are shown to play a key part in the global credit crisis. We discuss how the credit market has exposed the shortcomings of the credit risk models and we identify their main shortcomings. To overcome the shortcomings, a new causal framework is proposed to build deductive credit default models which have predictive capabilities.
    Date: 2008–07–10
  3. By: Juri Marcucci (Bank of Italy, Economic Research Department); Mario Quagliariello (Bank of Italy, Banking and Financial Supervision)
    Abstract: In the recent banking literature on the relationship between credit risk and the business cycle, the presence of asymmetric effects both across credit risk regimes and through the business cycle has been generally neglected. Employing threshold regression models both at the aggregate and the bank level and exploiting a unique dataset on Italian bank borrowersÂ’ default rates, this paper analyzes whether this relationship is characterized by regime switches and thus by asymmetries, determining the thresholds endogenously. Our results show that not only are the effects of the business cycle on credit risk more pronounced during downturns but also when credit risk conditions are poor.
    Keywords: Credit Risk, Panel Threshold Regression Models, Regime Switching, Default Rate, Business Cycle, Cyclicality, Basel 2
    JEL: C22 C23 G21 G28
    Date: 2008–06
  4. By: Albert H. De Wet (FirstRand Bank, South Africa); Renee Van Eyden (Department of Economics, University of Pretoria); Rangan Gupta (Department of Economics, University of Pretoria)
    Abstract: Active credit portfolio management is becoming a central part of capital and credit management within the banking industry. Stimulated by the Basel II capital accord the estimation of risk sensitive credit and capital management is central to success in an increasingly competitive environment. If any risk mitigation or value-enhancing activity is to be pursued, a credit portfolio manager must be able to identify the interdependencies between exposures in a portfolio, but more importantly, be able to relate credit risk to tangible portfolio effects on which specific actionable items can be taken. This analysis draws on the macroeconometric vector error correcting model (VECM) developed by De Wet and Van Eyden (2007) and applies the proposed methodology of PSTW (2006) to a fictitious portfolio of corporate bank loans within the South African economy. It illustrates that it is not only possible to link macroeconomic factors to a South African specific credit portfolio, but that scenario and sensitivity analysis can also be performed within the credit portfolio model. These results can be used in credit portfolio management or standalone credit risk analytics which is ideal for practical credit portfolio management applications.
    Keywords: Credit portfolio modelling, macroeconometric correlation model, economic capital, scenario analysis, default threshold
    JEL: G32 E17
    Date: 2008–07
  5. By: Darrell Duffie
    Abstract: Banks and other lenders often transfer credit risk to liberate capital for further loan intermediation. This paper aims to explore the design, prevalence and effectiveness of credit risk transfer (CRT). The focus is on the costs and benefits for the efficiency and stability of the financial system. After an overview of recent credit risk transfer activity, the following points are discussed: motivations for CRT by banks; risk retention; theories of CDO design; specialty finance companies. As an illustration of CLO design, an example is provided showing how the credit quality of the borrowers can deteriorate if efforts to control their default risks are costly for issuers. An appendix is provided on CDS index tranches.
    Keywords: credit derivatives, credit risk transfer, financial innovations, financial stability
    Date: 2008–07
  6. By: Febrian, Erie; Herwany, Aldrin
    Abstract: For both risk management and portfolio selection purposes, modeling the linkage across financial markets is crucial, especially among neighboring stock markets. In investigating the dependence or co-movement of three or more stock markets in different countries, researchers frequently use co-integration and causality analysis. Nevertheless, they conducted the causality in mean tests but not the causality in variance tests. This paper examines the co-integration and causal relations among three major stock exchanges in Southeast Asia, i.e Jakarta Stock Exchange, Singapore Stock Exchange, and Kuala Lumpur Stock Exchange. It employs the recently developed techniques for investigating unit roots, co-integration, time-varying volatility, and causality in variance. For estimating market risk of portfolio, this paper employs Value-at-Risk with delta-normal approach.
    Keywords: Risk Management, Causality, Co-integration, Stock Markets
    JEL: G1 D53
    Date: 2007–10–15
  7. By: Zhu, Yanhui (Cardiff Business School); Copeland, Laurence (Cardiff Business School)
    Abstract: The seminal Barro (2006) closed-economy model of the equity risk premium in the presence of extreme events ("disasters") allowed for leverage in the form of risky corporate debt which defaulted only in states when the Government defaulted on its debt. The probability of default was therefore exogenous and independent of the degree of leverage. In this paper, we take the model a step closer to reality by assuming that, on the one hand, the Government never defaults, and on the other hand, that the .corporate sector. in the form of the Lucas tree owner pays its debts in full if and only if its asset value is sufficient, which is always the case in non-crisis states. Otherwise, in exceptionally severe crises, it defaults and hands over the whole .firm. to its creditors. The probability of default by the tree owner is thus endogenous, dependent both on the volume of debt issued (taken as exogenous) and on the uncertain value of output. We show, using data from both Barro (2006) and Barro and Ursua (2008), that the model can generate values of the riskless rate, equity risk premium and credit risk spread broadly consistent with those typically observed in the data.
    Keywords: equity risk premium; default risk; credit spread; leverage; corporate debt
    JEL: F3 G1
    Date: 2008–07

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