|
on Risk Management |
|
Issue of 2010‒03‒20
nine papers chosen by |
| By: | Dominique Guegan (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I, EEP-PSE - Ecole d'Économie de Paris - Paris School of Economics - Ecole d'Économie de Paris); Wayne Tarrant (Wingate University - Department of Mathematics) |
| Abstract: | The banking systems that deal with risk management depend on underlying risk measures. Following the recommendation of the Basel II accord, most banks have developed internal models to determine their capital requirement. The Value at Risk measure plays an important role in computing this capital. In this paper we analyze in detail the errors produced by use of this measure. We then discuss other measures, pointing out their strengths and shortcomings. We give detailed examples, showing the need for five risk measures in order to compute a capital in relation to the risk to which the bank is exposed. In the end, we suggest using five different risk measures for computing capital requirements. |
| Keywords: | Risk measure ; Value at Risk ; Bank capital ; Basel II Accord |
| Date: | 2010–01 |
| URL: | http://d.repec.org/n?u=RePEc:hal:cesptp:halshs-00460901_v1&r=rmg |
| By: | McAleer, Michael; Jimenez-Martin, Juan-Angel; Perez Amaral, Teodosio |
| Abstract: | In this paper we advance the idea that optimal risk management under the Basel II Accord will typically require the use of a combination of different models of risk. This idea is illustrated by analyzing the best empirical models of risk for five stock indexes before, during, and after the 2008-09 financial crisis. The data used are the Dow Jones Industrial Average, Financial Times Stock Exchange 100, Nikkei, Hang Seng and Standard and Poor’s 500 Composite Index. The primary goal of the exercise is to identify the best models for risk management in each period according to the minimization of average daily capital requirements under the Basel II Accord. It is found that the best risk models can and do vary before, during and after the 2008-09 financial crisis. Moreover, it is found that an aggressive risk management strategy, namely the supremum strategy that combines different models of risk, can result in significant gains in average daily capital requirements, relative to the strategy of using single models, while staying within the limits of the Basel II Accord. |
| Keywords: | Optimal risk management; average daily capital requirements; alternative risk strategies; value-at-risk forecasts; combining risk models |
| JEL: | G11 C53 C22 G32 |
| Date: | 2009–09–19 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:20975&r=rmg |
| By: | Liuling Li (Nankai University); Bruce Mizrach (Rutgers University) |
| Abstract: | We compare several models for Bear Stearns' credit default swap spreads estimated via a Markov chain Monte Carlo algorithm. The Bayes Factor selects a CKLS model with GARCH-EPD errors as the best model. This model captures the volatility clustering and extreme tail returns of the swaps during the crisis. Prior to November 2007, only four months ahead of Bear Stearns' collapse though, the swap spreads were indistinguishable statistically from the risk free rate. |
| Keywords: | Bear Stearns, credit default swap, Bayesian analysis, exponential power distribution |
| JEL: | C11 G13 G24 |
| Date: | 2010–03–10 |
| URL: | http://d.repec.org/n?u=RePEc:rut:rutres:201003&r=rmg |
| By: | Michael Pykhtin; Dan Rosen |
| Abstract: | We address the problem of allocating the counterparty-level credit valuation adjustment (CVA) to the individual trades composing the portfolio. We show that this problem can be reduced to calculating contributions of the trades to the counterparty-level expected exposure (EE) conditional on the counterparty's default. We propose a methodology for calculating conditional EE contributions for both collateralized and non-collateralized counterparties. Calculation of EE contributions can be easily incorporated into exposure simulation processes that already exist in a financial institution. We also derive closed-form expressions for EE contributions under the assumption that trade values are normally distributed. Analytical results are obtained for the case when the trade values and the counterparty's credit quality are independent as well as when there is a dependence between them (wrong-way risk). |
| Date: | 2010 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2010-10&r=rmg |
| By: | Juan Carlos Hatchondo (Federal Reserve Bank of Richmond); Leonardo Martinez; Horacio Sapriza |
| Date: | 2010–03 |
| URL: | http://d.repec.org/n?u=RePEc:red:append:08-133&r=rmg |
| By: | Chang, C.; McAleer, M.J.; Tansuchat, R. (Erasmus Econometric Institute) |
| Abstract: | Crude oil price volatility has been analyzed extensively for organized spot, forward and futures markets for well over a decade, and is crucial for forecasting volatility and Value-at-Risk (VaR). There are four major benchmarks in the international oil market, namely West Texas Intermediate (USA), Brent (North Sea), Dubai/Oman (Middle East), and Tapis (Asia-Pacific), which are likely to be highly correlated. This paper analyses the volatility spillover and asymmetric effects across and within the four markets, using three multivariate GARCH models, namely the constant conditional correlation (CCC), vector ARMA-GARCH (VARMA-GARCH) and vector ARMA-asymmetric GARCH (VARMA-AGARCH) models. A rolling window approach is used to forecast the 1-day ahead conditional correlations. The paper presents evidence of volatility spillovers and asymmetric effects on the conditional variances for most pairs of series. In addition, the forecast conditional correlations between pairs of crude oil returns have both positive and negative trends. Moreover, the optimal hedge ratios and optimal portfolio weights of crude oil across different assets and market portfolios are evaluated in order to provide important policy implications for risk management in crude oil markets. |
| Keywords: | volatility spillovers;multivariate GARCH;conditional correlation;crude oil prices;spot returns;forward returns;futures returns |
| Date: | 2010–03–02 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:eureir:1765018329&r=rmg |
| By: | Damiano Brigo; Mirela Predescu; Agostino Capponi |
| Abstract: | We review different approaches for measuring the impact of liquidity on CDS prices. We start with reduced form models incorporating liquidity as an additional discount rate. We review Chen, Fabozzi and Sverdlove (2008) and Buhler and Trapp (2006, 2008), adopting different assumptions on how liquidity rates enter the CDS premium rate formula, about the dynamics of liquidity rate processes and about the credit-liquidity correlation. Buhler and Trapp (2008) provides the most general and realistic framework, incorporating correlation between liquidity and credit, liquidity spillover effects between bonds and CDS contracts and asymmetric liquidity effects on the Bid and Ask CDS premium rates. We then discuss the Bongaerts, De Jong and Driessen (2009) study which derives an equilibrium asset pricing model incorporating liquidity effects. Findings include that both expected illiquidity and liquidity risk have a statistically significant impact on expected CDS returns. We finalize our review with a discussion of Predescu et al (2009), which analyzes also data in-crisis. This is a statistical model that associates an ordinal liquidity score with each CDS reference entity and allows one to compare liquidity of over 2400 reference entities. This study points out that credit and illiquidity are correlated, with a smile pattern. All these studies highlight that CDS premium rates are not pure measures of credit risk. Further research is needed to measure liquidity premium at CDS contract level and to disentangle liquidity from credit effectively. |
| Date: | 2010–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1003.0889&r=rmg |
| By: | Suarez, Ronny |
| Abstract: | In this paper we estimated not-overlapped monthly historic standard deviations of the S&P 500 Index returns for the period 1950 – 2009, then using extreme value theory we defined extreme volatility events and introduced an alternative “fear scale” that is compared with the “fear index”. |
| Keywords: | Extreme Value Theory; Peak Over Threshold; Generalized Pareto Distribution; Return Level; Extreme Volality Event |
| JEL: | C0 G0 |
| Date: | 2010–03 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:21053&r=rmg |
| By: | Daniel T. Cassidy (Department of Engineering Physics, McMaster University, Hamilton, Ontario, Canada); Michael J. Hamp (Scotiabank, Toronto, Ontario, Canada); Rachid Ouyed (Physics and Astronomy, University of Calgary, Calgary, Alberta, Canada) |
| Abstract: | European options can be priced when returns follow a Student's t-distribution, provided that the asset is capped in value or the distribution is truncated. We call pricing of options using a log Student's t-distribution a Gosset approach, in honour of W.S. Gosset. In this paper, we compare the greeks for Gosset and Black-Scholes formulae and we discuss implementation. The t-distribution requires a shape parameter \nu to match the "fat tails" of the observed returns. For large \nu, the Gosset and Black-Scholes formulae are equivalent. The Gosset formulae removes the requirement that the volatility be known, and in this sense can be viewed as an extension of the Black-Scholes formula. |
| Date: | 2010–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1003.1344&r=rmg |