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on Risk Management |
| By: | Andrén, Niclas (Department of Business Administration); Jankensgård, Håkan (Department of Business Administration); Oxelheim, Lars (Research Institute of Industrial Economics (IFN)) |
| Abstract: | A strategically minded CFO will realize that strategic corporate risk management is about finding the right balance between risk prevention and proactive value generation. Efficient risk and performance management requires adequate assessment of risk and risk exposures on the one hand and performance on the other. Properly designed, a risk measure should provide information on to what extend the firm's performance is at risk, what is causing that risk, the relative importance of non-value-adding and value-adding risk, and the possibilities to use risk management to reduce total risk. In this chapter, we present an approach – exposure-based cash-flow-at-risk – to calculating a firm's downside risk conditional on the firm's exposure to non-value-adding macroeconomic and market risk and to analyzing corporate performance adjusted for the impact of non-value-adding risk. |
| Keywords: | Cash-flow-at risk; Value at risk; Risk management; Value creation; Total risk |
| JEL: | E32 G32 G33 G34 M21 |
| Date: | 2010–06–21 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:iuiwop:0843&r=rmg |
| By: | Thomas Breuer (Research Centre PPE); Martin Jandačka (Research Centre PPE); Javier Mencía (Banco de España); Martin Summer (Oesterreichische Nationalbank) |
| Abstract: | We propose a new method for analysing multiperiod stress scenarios for portfolio credit risk more systematically than in the current practice of macro stress testing. Our method quantifies the plausibility of scenarios by considering the distance of the stress scenario from an average scenario. For a given level of plausibility our method searches systematically for the most adverse scenario for the given portfolio. This method therefore gives a formal criterion for judging the plausibility of scenarios and it makes sure that no plausible scenario will be missed. We show how this method can be applied to a range of models already in use among stress testing practitioners. While worst case search requires numerical optimisation we show that for practically relevant cases we can work with reasonably good linear approximations to the portfolio loss function that make the method computationally very efficient and easy to implement. Applying our approach to data from the Spanish loan register and using a portfolio credit risk model we show that, compared to standard stress test procedures, our method identifies more harmful scenarios that are equally plausible. |
| Keywords: | Stress Testing, Credit Risk, Worst Case Search, Maximum Loss |
| JEL: | G28 G32 G20 C15 |
| Date: | 2010–06 |
| URL: | http://d.repec.org/n?u=RePEc:bde:wpaper:1018&r=rmg |
| By: | Satoshi Yamashita (Associate Professor, The Institute of Statistical Mathematics (E-mail: yamasita@ism.ac.jp)); Toshinao Yoshiba (Director and Senior Economist, Institute for Monetary and Economic Studies, Bank of Japan (E-mail: toshinao.yoshiba@boj.or.jp)) |
| Abstract: | In this study, we derive an explicit solution for the expected loss of a collateralized loan, focusing on the negative correlation between default intensity and collateral value. Three requirements for the default intensity and the collateral value are imposed. First, the default event can happen at any time until loan maturity according to an exogenous stochastic process of default intensity. Second, default intensity and collateral value are negatively correlated. Third, the default intensity and collateral value are non-negative. To develop an explicit solution, we propose a square-root process for default intensity and an affine diffusion process for collateral value. Given these settings, we derive an explicit solution for the integrand of the expected recovery value within an extended affine model. From the derived solution, we find the expected recovery value is given by a Stieltjes integral with a measure-changed survival probability. |
| Keywords: | stochastic recovery, default intensity model, affine diffusion, extended affine, survival probability, measure change |
| JEL: | G21 G32 G33 |
| Date: | 2010–06 |
| URL: | http://d.repec.org/n?u=RePEc:ime:imedps:10-e-10&r=rmg |
| By: | Henry Laniado; Rosa E. Lillo; Juan Romo |
| Abstract: | We propose a new multivariate order based on a concept that we will call extremality". Given a unit vector, the extremality allows to measure the "farness" of a point with respect to a data cloud or to a distribution in the vector direction. We establish the most relevant properties of this measure and provide the theoretical basis for its nonparametric estimation. We include two applications in Finance: a multivariate Value at Risk (VaR) with level sets constructed through extremality and a portfolio selection strategy based on the order induced by extremality. |
| Keywords: | Extremality, Oriented cone, Value at risk, Portfolio selection |
| Date: | 2010–06 |
| URL: | http://d.repec.org/n?u=RePEc:cte:wsrepe:ws101908&r=rmg |
| By: | Dötz, Niko; Fischer, Christoph |
| Abstract: | This paper presents a new approach for analysing the recent development of EMU sovereign bond spreads. Based on a GARCH-in-mean model originally used in the exchange rate target zone literature, spreads are decomposed into a risk premium, an expected loss component and a liquidity premium. Time-varying default probabilities are derived. The results suggest that the rise in sovereign spreads during the recent financial crisis mainly reflects an increased expected loss component. In addition, the rescue of Bear Stearns in March 2008 seems to mark a change in market perceptions of sovereign bond risk. The government bonds of some countries lost their former role as a safe haven. While price competitiveness always helps to explain sovereign spreads, it increasingly moved into investors' focus as financial sector soundness weakened. -- |
| Keywords: | Sovereign bond spread,GARCH-in-mean,default probability |
| JEL: | E43 G15 C32 H63 F36 |
| Date: | 2010 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bubdp1:201011&r=rmg |
| By: | Sebastian D\"ohler |
| Abstract: | We apply multiple testing procedures to the validation of estimated default probabilities in credit rating systems. The goal is to identify rating classes for which the probability of default is estimated inaccurately, while still maintaining a predefined level of committing type I errors as measured by the familywise error rate (FWER) and the false discovery rate (FDR). For FWER, we also consider procedures that take possible discreteness of the data resp. test statistics into account. The performance of these methods is illustrated in a simulation setting and for empirical default data. |
| Date: | 2010–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1006.4968&r=rmg |
| By: | David E. Allen; Michael McAleer (University of Canterbury); Marcel Scharth |
| Abstract: | In this paper we document that realized variation measures constructed from high- frequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation measures are nearly gaussian, this unpredictability brings considerably more uncertainty to the empirically relevant ex ante distribution of returns. Carefully modeling this volatility risk is fundamental. We propose a dually asymmetric realized volatility (DARV) model, which incorporates the important fact that realized volatility series are systematically more volatile in high volatility periods. Returns in this framework display time varying volatility, skewness and kurtosis. We provide a detailed account of the empirical advantages of the model using data on the S&P 500 index and eight other indexes and stocks. |
| Keywords: | Realized volatility; volatility of volatility; volatility risk; value-at-risk; forecasting; conditional heteroskedasticity |
| Date: | 2010–05–01 |
| URL: | http://d.repec.org/n?u=RePEc:cbt:econwp:10/26&r=rmg |
| By: | Khudnitskaya, Alesia S. |
| Abstract: | Paper introduces the improved version of a credit scoring model which assesses credit worthiness of applicants for a loan. The scorecard has a two-level multilevel structure which nests applicants for a loan within microenvironments. Paper discusses several versions of the multilevel scorecards which includes random-intercept, random-coefficients and group-level variables. The primary benefit of the multilevel scorecard compared to a conventional scoring model is a higher accuracy of the model predictions. |
| Keywords: | Credit scoring; Hierarchical clustering; Multilevel model; Random-coefficient; Random-intercept; Monte Carlo Markov chain |
| JEL: | C53 D14 G21 |
| Date: | 2009–12 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:23175&r=rmg |