Chen, Cathy W. S. and Gerlach, Richard and Hwang, Bruce B. K. and McAleer, Michael (2011) Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intra-day Range. [ Documentos de Trabajo del Instituto Complutense de Análisis Económico; nº 16, 2011, ] (Unpublished)
Available under License Creative Commons Attribution Non-commercial.
Official URL: http://eprints.ucm.es/12755/
Value-at-Risk (VaR) is commonly used for financial risk measurement. It has recently become even more important, especially during the 2008-09 global financial crisis. We pro-pose some novel nonlinear threshold conditional autoregressive VaR (CAViaR) models that incorporate intra-day price ranges. Model estimation and inference are performed using the Bayesian approach via the link with the Skewed-Laplace distribution. We examine how a range of risk models perform during the 2008-09 fnancial crisis, and evaluate how the crisis afects the performance of risk models via forecasting VaR. Empirical analysis is conducted
on five Asia-Pacific Economic Cooperation stock market indices as well as two exchange rate series. We examine violation rates, back-testing criteria, market risk charges and quantile loss function values to measure and assess the forecasting performance of a variety of risk models. The proposed threshold CAViaR model, incorporating range information, is shown to forecast VaR more eficiently than other models, across the series considered, which should
be useful for financial practitioners.
|Item Type:||Working Paper or Technical Report|
|Uncontrolled Keywords:||Value-at-Risk, CAViaR model, Skewed-Laplace distribution, Intra-day range, Backtesting, Markov chain Monte Carlo.|
|Subjects:||Social sciences > Economics > Econometrics|
Social sciences > Economics > Economic indicators
|Series Name:||Documentos de Trabajo del Instituto Complutense de Análisis Económico|
Alizadeh, S., Brandt, M. & Diebold, F.X. (2002). "Range-Based Estimation of Stochastic Volatility Models," Journal of Finance, 57, 1047-1092.
Beckers, S. (1983). Variance of security price return based on high, low and closing prices, Journal of Business, 56, 97-112.
Berkowitz, J., Christoerson, P.F. & Pelletier, D. (2011). Evaluating Value-at-Risk models with desk-level data, Management Science, to appear.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroscedasticity. Journal of Econometrics, 31, 307-327.
Brandt, M. & C. Jones (2006). Volatility Forecasting With Range-Based EGARCH Models, Journal of Business and Economic Statistics 24 (4), 470-486.
Chen, C. W. S., Chiang, T. C., & So, M. K. P. (2003). Asymmetrical reaction to US stock-return news: evidence from major stock markets based on a double-threshold model.
Journal of Economics and Business, 55, 487-502.
Chen, C.W.S., Gerlach, R., Lin, E.M.H. & Lee, W.C.W. (2011) Bayesian Forecasting for Finan-cial Risk Management, Pre and Post the Global Financial Crisis. Journal of Forecasting, to appear.
Chen, C.W.S., Gerlach, R., & Lin, E.M.H. (2008). Volatility forecasting using threshold het-eroskedastic models of the Intra-day Range. Computational Statistics & Data Analysis,
Chen, C.W.S., Gerlach, R., Lin, E.M.H., & Lee, W. (2010). Bayesian forecasting for Financial risk management, accepted, Journal of Forecasting.
Chen, C.W.S., & So, M.K.P. (2006). On a threshold heteroscedastic model. International Journal of Forecasting, 22, 73-89.
Chou, R. (2005). Forecasting nancial volatilities with extreme values: The conditional au-toregressive range (CARR) model. Journal of Money, Credit and Banking, 37, 561-582.
Christofersen, P. (1998). Evaluating interval forecasts. International Economic Review, 39, 841-862.
Engle, R.F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance
of united kingdom in ation. Econometrica, 50, 987-1007.
Engle, R.F., & Manganelli, S. (2004). CAViaR: conditional autoregressive value at risk by regression quantiles. Journal of Business & Economic Statistics, 22, 367-381.
Gallant, A. R., Hsu, C. T., & Tauchen, G. E. (1999). Calculating volatility difusions and extracting integrated volatility. Review of Economics and Statistics, 81, 617-631.
Garman, M.B., & Klass, M.J. (1980). On the estimation of price volatility from historical data, Journal of Business, 53, 67-78.
Geraci, M., & Bottai, M. (2007). Quantile regression for longitudinal data using the asymmetric Laplace distribution. Biostatistics, 8, 140-154.
Gerlach, R., & Chen, C.W.S. (2008). Bayesian inference and model comparison for asym-metric smooth transition heteroskedastic models. Statistics and Computing, 18 (4), 391-408.
Gerlach, R., Chen C.W.S., & Chan, N.Y.C. (2011). Bayesian time-varying quantile forecast-ing for Value-at-Risk in nancial markets, forthcoming Journal of Business & Economic
Giacomini, R., & Komunjer, I. (2005). Evaluation and combination of conditional quantile forecasts. Journal of Business & Economic Statistics, 23, 416-431.
Guidolin, M, & Timmermann, A. (2006). Term structure of risk under alternative econometric specications. Journal or Econometrics, 131, 285-308.
Haas, M., Mittnik, S. & Paolella, M. (2006) Value-at-Risk prediction: A comparison of alter-native strategies. Journal of Financial Econometrics, 4, 1, 53-89.
Hansen, B. (1994). Autoregressive Conditional Density Estimation. International Economic Review, 35, 705-730.
Hastings, W.K. (1970). Monte-carlo sampling methods using Markov chains and their appli-cations. Biometrika, 57, 97-109.
Jorion, P. (1996). Risk: measuring the risk in Value at Risk. Financial Analysis Journal, 52, 47-56.
Koenker, R. & Bassett, G. (1978). Regression quantiles. Econometrica, 1, 33-50.
Koenker, R. & J. A. F. Machado (1999). Goodness of Fit and Related Inference Processes for Quantile Regression, Journal of the American Statistical Association, 94 (448), 1296-1310.
Kuester, K., Mittnik, S., & Paolella, M.S. (2006). Value-at-risk prediction: a comparison of alternative strategies, Journal of Financial Econometrics, 4, 53-89.
Kupiec, P. (1995). Techniques for verifying the accuracy of risk measurement models. Journal of Derivatives, 2, 173-184.
Mandelbrot, B. (1971). When can price be arbitraged efciently? A limit to the validity of the random walk and martingale models. Review of Economics and Statistics, 53, 225-236.
McAleer, M., & da Veiga, B. (2008a). Forecasting value-at-risk with a parsimonious portfolio spillover GARCH (PS-GARCH) model. Journal of Forecasting, 27, 1-19.
McAleer, M., & da Veiga, B. (2008b). Single and portfolio models for forecasting Value-at-Risk thresholds. Journal of Forecasting, 27, 217-235.
McAleer, M., Jimenez-Martin, J.-A. & Perez-Amaral, T. (2010a). Has the Basel II Accord encouraged risk management during the 2008-09 fnancial crisis?, Available at SSRN:
McAleer, M., Jimenez-Martin, J.-A. & Perez-Amaral, T. (2010b). What happened to risk management during the 2008-09 fnancial crisis?, in R.W. Kolb (ed.), Lessons from the
Financial Crisis: Causes, Consequences, and Our Economic Future, Wiley, New York, 307-316.
Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., & Teller, E. (1953). Equations of state calculations by fast computing machines. Journal of Chemical Physics, 21, 1087-1091.
Parkinson, M. (1980). The extreme value method for estimating the variance of the rate of return. Journal of Business, 53, 61-65.
Tsionas, E.G. (2003). Bayesian quantile inference. Journal of Statistical Computation and Simulation, 9, 659-674.
Yu, K., & Moyeed, R.A. (2001). Bayesian quantile regression. Statistics and Probability Letters, 54, 437-447.
Yu, K., & Zhang, J. (2005). Distribution and applications - a three-parameter asymmetric
laplace distribution and its extension. Communications in Statistic-Theory and Method, 34, 1867-1879.
Yu, P.L.H., Li, W.K., & Jin, S. (2011). On some models for Value-at-Risk, forthcoming Econometric Reviews.
|Deposited On:||01 Jun 2011 06:51|
|Last Modified:||06 Feb 2014 09:31|
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