Variance-Gamma and Normal-Inverse Gaussian models: Goodness-of-fit to Chinese high-frequency index returns |
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| Affiliation: | 1. Department of Mathematical Sciences, Xi’an Jiaotong-Liverpool University, Ren’ai Road 111, Suzhou 215123, China;2. Center for Economics and Econometrics, Bogazici University, Istanbul, Turkey;1. Faculty of Economics and Business Administration, University of Duisburg–Essen, Universitätsstraße 12, D-45117 Essen, Germany;2. University of Cologne, Germany;3. TU Dortmund, Germany;1. International Monetary Fund, Washington, United States;2. European University Institute, Florence, Italy;3. De Nederlandsche Bank, Amsterdam, The Netherlands;4. University of Groningen, The Netherlands;5. CESifo, Munich, Germany;1. IPAG Lab, IPAG Business School;2. European University Institute (EUI);3. Athens University of Economics and Business (AUEB) |
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| Abstract: | ![]()
In this study Variance-Gamma (VG) and Normal-Inverse Gaussian (NIG) distributions are compared with the benchmark of generalized hyperbolic distribution in terms of their fit to the empirical distribution of high-frequency stock market index returns in China. First, we estimate the considered models in a Markov regime switching framework for the identification of different volatility regimes. Second, the goodness-of-fit results are compared at different time scales of log-returns. Third, the goodness-of-fit results are validated through bootstrapping experiments. Our results show that as the time scale of log-returns decrease NIG model outperforms the VG model consistently and the difference between the goodness-of-fit statistics increase. For high-frequency Chinese index returns, NIG model is more robust and provides a better fit to the empirical distributions of returns at different time scales. |
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| Keywords: | Variance-Gamma Normal-Inverse Gaussian Generalized hyperbolic distribution Chinese high-frequency index returns |
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