An empirical evaluation of fat-tailed distributions in modeling financial time series |
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Authors: | Mike KP So Cathy WS Chen Jen-Yu Lee Yi-Ping Chang |
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Affiliation: | 1. Department of Information and Systems Management, The Hong Kong University of Science and Technology, Hong Kong;2. Graduate Institute of Statistics and Actuarial Science, Feng Chia University, Taiwan;3. Department of Business Mathematics, Soochow University, Taiwan |
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Abstract: | There is substantial evidence that many financial time series exhibit leptokurtosis and volatility clustering. We compare the two most commonly used statistical distributions in empirical analysis to capture these features: the t distribution and the generalized error distribution (GED). A Bayesian approach using a reversible-jump Markov chain Monte Carlo method and a forecasting evaluation method are adopted for the comparison. In the Bayesian evaluation of eight daily market returns, we find that the fitted t error distribution outperforms the GED. In terms of volatility forecasting, models with t innovations also demonstrate superior out-of-sample performance. |
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Keywords: | Bayesian GARCH models Generalized error distribution Reversible-jump |
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