Mapa, Dennis and Beronilla, Nikkin (2008): Range-Based Models in Estimating Value-at-Risk (VaR). Published in: The Philippine Review of Economics , Vol. XLV, No. 2 (December 2008): pp. 87-100.
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This paper introduces new methods of estimating Value-at-Risk (VaR) using Range-Based GARCH (General Autoregressive Conditional Heteroskedasticity) models. These models, which could be either based on the Parkinson Range or Garman-Klasss Range, are applied to 10 stock market indices of selected countries in the Asia-Pacific Region. The results are compared using the traditional methods such as the econometric method based on the ARMA-GARCH models and RiskMetricsTM. The performance of the different models is assessed using the out-of-sample VaR forecasts. Series of likelihood ratio (LR) tests namely: LR of unconditional coverage (LRuc), LR of independence (LRind), and LR of conditional coverage (LRcc) are performed for comparison. The result of the assessment shows that the model based on the Parkinson Range GARCH (1,1) with Student’s t distribution is the best performing model on the 10 stock market indices. It has a failure rate, defined as the percentage of actual return that is smaller than the one-step-ahead VaR forecast, of zero in 9 out 10 stock market indices. The finding of this paper is that Range-Based GARCH Models are good alternatives in modeling volatility and in estimating VaR.
|Item Type:||MPRA Paper|
|Original Title:||Range-Based Models in Estimating Value-at-Risk (VaR)|
|Keywords:||Value-at-Risk (VaR), Parkinson Range, Garman-Klasss Range, Range-Based GARCH (General Autoregressive Conditional Heteroskedasticity)|
|Subjects:||C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation
C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection
C - Mathematical and Quantitative Methods > C2 - Single Equation Models; Single Variables > C22 - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
|Depositing User:||Dennis S. Mapa|
|Date Deposited:||08. Mar 2010 19:47|
|Last Modified:||14. Feb 2013 05:44|
Bao, Y., Lee, T., and Saltoglu, B. (2006), “Evaluating Predictive Performance of Value-at-Risk Models in Emerging Markets: A Reality Check,” Journal of Forecasting, 25:101-128.
Bollen B. and B. Inder (2003), “A Comparison of Estimators Daily Realised Volatility,” Finance Letters, 1: 29-34.
Bollerslev, T. (1986), “Generalized Autoregressive Conditional Heteroskedasticity,” Journal of Econometrics, 31: 307-327.
Box G. and G. Jenkins (1984), “Time Series Analysis: Forecasting and Control,” 2nd ed. San Francisco: Holden Day.
Christoffersen P. (1998), “Evaluating Interval Forecast,” International Economic Review, 3 (4): 841-862.
Engle, R. F. (1982), “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of U.K .Inflation,” Econometrica, 50: 987-1008.
Garman, M.B. and Klasss, M.J. (1980). On the estimation of security price volatilities from historical data. Journal of Business 53: 67-78.
Giot P. and S. Laurent (2003), “Value-at-Risk for and short trading positions,” Journal of Applied Econometrics, 18: 641-664.
Glosten L. R., R. Jagannathan and D. Runkle (1993), “On the relation between the expected value and the volatility of the nominal excess return on stocks,” Journal of Finance, 48: 1779-1801.
Jorion P. (2001), “Value at Risk: the new benchmark for managing financial risk,” McGraw-Hill USA.
Mapa D.S. (2003), “A range-based GARCH model for forecasting financial volatility,” The Philippine Review of Economics, 15(2): 73-90.
Morgan J. P., and Reuters (1996), “RiskMetricsTM – Technical Document,” ebook, 4th edition.
Parkinson, M. (1980), “The Extreme Value Method for Estimating the Variance of the Rate of Return”, Journal of Business, 53, 61-65.
Nelson D. (1991), “Conditional heteroskedasticity in asset returns a new approach,” Econometrica, 59: 347-370.
Tsay R. (2005), “Analysis of Financial Time Series,” John Wiley & Sons; 2nd edition.