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on Risk Management |
| By: | Ozun, Alper; Cifter, Atilla; Yilmazer, Sait |
| Abstract: | Extreme returns in stock returns need to be captured for a successful risk management function to estimate unexpected loss in portfolio. Traditional value-at-risk models based on parametric models are not able to capture the extremes in emerging markets where high volatility and nonlinear behaviors in returns are observed. The Extreme Value Theory (EVT) with conditional quantile proposed by McNeil and Frey (2000) is based on the central limit theorem applied to the extremes rater than mean of the return distribution. It limits the distribution of extreme returns always has the same form without relying on the distribution of the parent variable. This paper uses 8 filtered EVT models created with conditional quantile to estimate value-at-risk for the Istanbul Stock Exchange (ISE). The performances of the filtered expected shortfall models are compared to those of GARCH, GARCH with student-t distribution, GARCH with skewed student-t distribution and FIGARCH by using alternative back-testing algorithms, namely, Kupiec test (1995), Christoffersen test (1998), Lopez test (1999), RMSE (70 days) h-step ahead forecasting RMSE (70 days), number of exception and h-step ahead number of exception. The test results show that the filtered expected shortfall has better performance on capturing fat-tails in the stock returns than parametric value-at-risk models do. Besides increase in conditional quantile decreases h-step ahead number of exceptions and this shows that filtered expected shortfall with higher conditional quantile such as 40 days should be used for forward looking forecasting. |
| Keywords: | Value at-Risk; Filtered Expected shortfall; Extreme value theory; emerging markets |
| JEL: | C32 G0 C52 |
| Date: | 2007–05–22 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:3302&r=rmg |
| By: | J. Carlos Escanciano (Indiana University, Bloomington, IN, USA); Jose Olmo (Department of Economics, City University, London) |
| Abstract: | One of the implications of the creation of Basel Committee on Banking Supervision was the implementation of Value-at-Risk (VaR) as the standard tool for measuring market risk. Thereby the correct specification of parametric VaR models became of crucial importance in order to provide accurate and reliable risk measures. If the underlying risk model is not correctly specified, VaR estimates understate/overstate risk exposure. This can have dramatic consequences on stability and reputation of financial institutions or lead to sub-optimal capital allocation. We show that the use of the standard unconditional backtesting procedures to assess VaR models is completely misleading. These tests do not consider the impact of estimation risk and therefore use wrong critical values to assess market risk. The purpose of this paper is to quantify such estimation risk in a very general class of dynamic parametric VaR models and to correct standard backtesting procedures to provide valid inference in specification analyses. A Monte Carlo study illustrates our theoretical findings in finite-samples. Finally, an application to S&P500 Index shows the importance of this correction and its impact on capital requirements as imposed by Basel Accord, and on the choice of dynamic parametric models for risk management. |
| Keywords: | Backtesting, Basel Accord, Model Risk, Risk management,Value at Risk, Conditional Quantile |
| Date: | 2007–05 |
| URL: | http://d.repec.org/n?u=RePEc:cty:dpaper:07/11&r=rmg |
| By: | Christian Huurman (Financial Engineering Associates); Francesco Ravazzolo (Erasmus Universiteit Rotterdam); Chen Zhou (Erasmus Universiteit Rotterdam) |
| Abstract: | In the literature the effects of weather on electricity sales are well-documented. However, studies that have investigated the impact of weather on electricity prices are still scarce (e.g. Knittel and Roberts, 2005), partly because the wholesale power markets have only recently been deregulated. We introduce the weather factor into well-known forecasting models to study its impact. We find that weather has explanatory power for the day-ahead power spot price. Using weather forecasts improves the forecast accuracy, and in particular new models with power transformations of weather forecast variables are significantly better in term of out-of-sample statistics than popular mean reverting models. For different power markets, such as Norway, Eastern Denmark and the Netherlands, we build specific models. The dissimilarity among these models indicates that weather forecasts influence not only the demand of electricity but also the supply side according to different electricity producing methods. |
| Keywords: | Electricity prices; forecasting; GARCH models; weather forecasts |
| JEL: | C53 G15 Q40 |
| Date: | 2007–04–25 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:uvatin:20070036&r=rmg |