1. Value-at-risk forecasting based on Gaussian mixture ARMA-GARCH model.
- Author
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Lee, Sangyeol and Lee, Taewook
- Subjects
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VALUE at risk , *MATHEMATICAL models , *ALGORITHMS , *FORECASTING , *ESTIMATION theory , *LEAST squares , *SIMULATION methods & models , *DATA analysis , *GAUSSIAN distribution - Abstract
In this paper, we develop a new forecasting algorithm for value-at-risk (VaR) based on ARMA-GARCH (autoregressive moving average-generalized autoregressive conditional heteroskedastic) models whose innovations follow a Gaussian mixture distribution. For the parameter estimation, we employ the conditional least squares and quasi-maximum-likelihood estimator (QMLE) for ARMA and GARCH parameters, respectively. In particular, Gaussian mixture parameters are estimated based on the residuals obtained from the QMLE of GARCH parameters. Our algorithm provides a handy methodology, spending much less time in calculation than the existing resampling and bias-correction method developed in Hartz et al. [Accurate value-at-risk forecasting based on the normal-GARCH model, Comput. Stat. Data Anal. 50 (2006), pp. 3032-3052]. Through a simulation study and a real-data analysis, it is shown that our method provides an accurate VaR prediction. [ABSTRACT FROM AUTHOR]
- Published
- 2011
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