
on Risk Management 
By:  Jens Hilscher (International Business School, Brandeis University); Mungo Wilson (University of Oxford) 
Abstract:  This paper investigates the information in corporate credit ratings. We examine the extent to which firms' credit ratings measure raw probability of default as opposed to systematic risk of default, a firm's tendency to default in bad times. We find that credit ratings are dominated as predictors of corporate failure by a simple model based on publicly available financial information (`failure score'), indicating that ratings are poor measures of raw default probability. However, ratings are strongly related to a straightforward measure of systematic default risk: the sensitivity of firm default probability to its common component (`failure beta'). Furthermore, this systematic risk measure is strongly related to credit default swap risk premia. Our findings can explain otherwise puzzling qualities of ratings. 
Keywords:  Credit Rating, Credit Risk, Default Probability, Forecast Accuracy, Systematic Default Risk 
JEL:  G12 G24 G33 
Date:  2011–06 
URL:  http://d.repec.org/n?u=RePEc:brd:wpaper:31&r=rmg 
By:  Liu, L.; Daniels, H.A.M. 
Abstract:  This paper proposes a risk assessment framework as a research roadmap, with the aim of developing a protocol that integrates the risk management requirements from the perspectives of the business and the government. We take the viewpoint of value modeling and interpret the risk management problem as a control problem. Four steps of risk assessment are identified in the framework, forming the risk management cycle. 
Keywords:  risk assessment;value modeling;controlling system;auditing 
Date:  2011–05–30 
URL:  http://d.repec.org/n?u=RePEc:dgr:eureri:1765023492&r=rmg 
By:  Chen, C.W.S.; Gerlach, R.; Hwang, B.B.K.; McAleer, M.J. 
Abstract:  ValueatRisk (VaR) is commonly used for financial risk measurement. It has recently become even more important, especially during the 200809 global financial crisis. We propose some novel nonlinear threshold conditional autoregressive VaR (CAViar) models that incorporate intraday price ranges. Model estimation and inference are performed using the Bayesian approach via the link with the SkewedLaplace distribution. We examine how a range of risk models perform during the 200809 financial crisis, and evaluate how the crisis affects the performance of risk models via forecasting VaR. Empirical analysis is conducted on five AsiaPacific Economic Cooperation stock market indices and two exchange rates????. We examine violation rates, backtesting criteria, market risk charges and quantile loss function to measure the forecasting performance of a variety of risk models. The proposed threshold CAViaR model, incorporating range information, is shown to forecast VaR more efficiently than other models, which should be useful for financial practitioners. 
Keywords:  ValueatRisk;CAViaR model;SkewedLaplace distribution;intraday range;backtesting;Markov chain Monte Carlo 
Date:  2011–06–30 
URL:  http://d.repec.org/n?u=RePEc:dgr:eureir:1765023795&r=rmg 
By:  Pu Huang; Dan A. Iancu; Marek Petrik; Dharmashankar Subramanian 
Abstract:  In this paper, we investigate two different frameworks for assessing the risk in a multiperiod decision process: a dynamically inconsistent formulation (whereby a single, static risk measure is applied to the entire sequence of future costs), and a dynamically consistent one, obtained by suitably composing onestep risk mappings. We characterize the class of dynamically consistent measures that provide a tight approximation for a given inconsistent measure, and discuss how the approximation factors can be computed. For the case where the consistent measures are given by Average ValueatRisk, we derive a polynomialtime algorithm for approximating an arbitrary inconsistent distortion measure. We also present exact analytical bounds for the case where the dynamically inconsistent measure is also given by Average ValueatRisk, and briefly discuss managerial implications in multiperiod riskassessment processes. Our theoretical and algorithmic constructions exploit interesting connections between the study of risk measures and the theory of submodularity and lattice programming, which may be of independent interest. 
Date:  2011–06 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1106.6102&r=rmg 
By:  Rocco Ciciretti (Faculty of Economics, University of Rome "Tor Vergata"); Raffaele Corvino (Faculty of Economics, University of Rome "Tor Vergata") 
Abstract:  The last financial crisis sheds dramatically light on the instability threatened by systemic risk. In this regard no common view appears to exist on the definition, the measurement and real impact on financial system. This paper aims to analyze the relation between systemic risk and portfolio diversification, highlighting the differences between heterogeneous and homogeneous diversification. Diversification is generally accepted to be the main tool for reducing idiosyncratic or portfoliospecific financial risk, but the homogeneous diversification produces also effects on systemic risk. The research consists of three steps to investigate how diversification affects the two components of portfolio risk: (i) systematic, and (ii) idiosyncratic risk. Through the impact on the level of portfolios allocation homogeneity, we assess how (iii) the diversification affects systemic risk. The empirical research implements the estimation strategy through balanced investment funds data, examining the change in asset allocation and the impact on the measures of dfferent types of risk. The results suggest that funds' portfolio diversification reduces at the same time the portfoliospecific risk increasing the likelihood of a simultaneous collapse of financial institutionsgiven that a systemic event occurs. 
Keywords:  Portfolio diversification, Risk, Asset allocation heterogeneity, Market crash. 
JEL:  G11 
Date:  2011–07–04 
URL:  http://d.repec.org/n?u=RePEc:rtv:ceisrp:204&r=rmg 
By:  Josep Perell\'o; Mario Guti\'errezRoig; Jaume Masoliver 
Abstract:  Financial markets provide an ideal frame for the study of firstpassage time events of nonGaussian correlated dynamics mainly because large data sets are available. Tickbytick data of six futures markets are herein considered resulting in fat tailed firstpassage time probabilities. The scaling of the return with the standard deviation collapses the probabilities of all markets considered, and also for different time horizons, into single curves, suggesting that firstpassage statistics is market independent (at least for highfrequency data). On the other hand, a very closely related quantity, the survival probability, still shows a hyperbolic $t^{1/2}$ decay typical of a diffusionlike dynamics. Modifications of the Weibull and Student distributions are good candidates for a phenomenological description of firstpassage time properties. The scaling strategies shown may be useful for risk control and algorithmic trading. 
Date:  2011–07 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1107.1174&r=rmg 
By:  Khalfaoui Rabeh, K; Boutahar Mohamed, B 
Abstract:  The paper studies the impact of different timescales on the market risk of individual stock market returns and of a given portfolio in Paris Stock Market by applying the wavelet analysis. To investigate the scaling properties of stock market returns and the lead/lag relationship between them at different scales, wavelet variance and crosscorrelations analyses are used. According to wavelet variance, stock returns exhibit long memory dynamics. The wavelet crosscorrelation analysis shows that comovements between stock returns are stronger at higher scales (lower frequencies); scales corresponding to period of 4 months and longer, i.e. scales 7 and 8. The wavelet analysis of systematic risk shows that all individual assets and the diversified portfolio have a multiscale behavior, which indicates that the systematic risk measured by Beta in the market model is not stable over time. The analysis of VaR at different time scales shows that risk is more concentrated at higher frequencies dynamics (lower time scales) of the data. 
Keywords:  Wavelets; Systematic risk; ValueatRisk 
JEL:  G12 C02 G32 
Date:  2011–06–28 
URL:  http://d.repec.org/n?u=RePEc:pra:mprapa:31938&r=rmg 
By:  Alexandre Boumezoued (R&D, Milliman, Paris  Milliman); Yoboua Angoua (R&D, Milliman, Paris  Milliman); Laurent Devineau (R&D Milliman  Milliman, SAF  Laboratoire de Sciences Actuarielle et Financière  Université Claude Bernard  Lyon I : EA2429); JeanPhilippe Boisseau (R&D, Milliman, Paris  Milliman) 
Abstract:  In this paper, we detail the main simulation methods used in practice to measure one‐year reserve risk, and describe the bootstrap method providing an empirical distribution of the Claims Development Result (CDR) whose variance is identical to the closed‐form expression of the prediction error proposed by Wüthrich et al. (2008). In particular, we integrate the stochastic modeling of a tail factor in the bootstrap procedure. We demonstrate the equivalence with existing analytical results and develop closed‐form expressions for the error of prediction including a tail factor. A numerical example is given at the end of this study. 
Keywords:  Non‐life insurance, Reserve risk, Claims Development Result, Bootstrap method, Tail factor, Prediction error, Solvency II 
Date:  2011–07–01 
URL:  http://d.repec.org/n?u=RePEc:hal:wpaper:hal00605329&r=rmg 
By:  Athanasoglou, Panayiotis 
Abstract:  This paper examines the simultaneous relationship between bank capital and risk. A model is set up which assumes that banks’ decisions regarding capital and risk are made endogenously in a dynamic pattern. A simultaneous equation system was estimated using an unbalanced panel of SEE banks from 2001 to 2009. A key result for the whole sample of banks is the relationship between regulatory (equity) capital and risk which is positive (negative). However, a positive twoway relationship between regulatory capital and risk was found only in less thanadequately capitalized banks, which also increased substantially their risk in 2009. Thus, banks’ decisions differentiate between equity capital and risk and regulatory capital and risk. A positive, significant and robust effect of liquidity on capital was identified. Both regulatory and equity capital exhibit procyclical behaviour, whilst the relationship between risk and rate of growth of GDP is ambitious. 
Keywords:  Banking; capital; risk; liquidity; regulation; panel estimation 
JEL:  G32 C33 G21 
Date:  2011–05 
URL:  http://d.repec.org/n?u=RePEc:pra:mprapa:32002&r=rmg 