A comparative goodness-of-fit analysis of distributions of some Lévy processes and Heston model to stock index returns |
| |
| Institution: | 1. Research Institute of Quantitative Finance, Xian Jiaotong Liverpool University, Suzhou 215123, China;2. Boğaziçi University, Center for Research in Corporate Governance & Financial Regulation, 34342 Bebek, Istanbul, Turkey;3. Boğaziçi University, Center for Economics and Econometrics, 34342 Bebek, Istanbul, Turkey;4. Boğaziçi University, Department of Economics, Natuk Birkan Building, 34342 Bebek, Istanbul, Turkey;1. School of Mathematical Sciences, Peking University, Beijing 100871, PR China;2. Key Laboratory of Mathematical Economics and Quantitative Finance, Peking University, Beijing 100871, PR China;1. School of Management, Beijing Normal University Zhuhai, No. 18, Jinfeng Road, Tangjiawan, Zhuhai City 519087, Guangdong Province, China;2. Department of Money and Banking, National Chengchi University, No. 64, Sec.2, ZhiNan Rd., Wenshan District, Taipei City 11605, Taiwan;1. Ruhr-Universität Bochum, Fakultät für Mathematik, 44780 Bochum, Germany;2. Christian-Albrechts-Universität zu Kiel, Mathematisches Seminar, Ludewig-Meyn-Str. 4, 24118 Kiel, Germany |
| |
| Abstract: | In this paper, we investigate the goodness-of-fit of three Lévy processes, namely Variance-Gamma (VG), Normal-Inverse Gaussian (NIG) and Generalized Hyperbolic (GH) distributions, and probability distribution of the Heston model to index returns of twenty developed and emerging stock markets. Furthermore, we extend our analysis by applying a Markov regime switching model to identify normal and turbulent periods. Our findings indicate that the probability distribution of the Heston model performs well for emerging markets under full sample estimation and retains goodness of fit for high volatility periods, as it explicitly accounts for the volatility process. On the other hand, the distributions of the Lévy processes, especially the VG and NIG distributions, generally improves upon the fit of the Heston model, particularly for developed markets and low volatility periods. Furthermore, some distributions yield to significantly large test statistics for some countries, even though they fit well to other markets, which suggest that properties of the stock markets are crucial in identifying the best distribution representing empirical returns. |
| |
| Keywords: | Variance-gamma model Normal-inverse gaussian model Generalized hyperbolic model Heston model Markov regime-switching model Emerging markets |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
