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Volatility forecasting using stochastic conditional range model with leverage effect
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| Zeitschriftentitel: | Applied Stochastic Models in Business and Industry |
|---|---|
| Personen und Körperschaften: | , |
| In: | Applied Stochastic Models in Business and Industry, 35, 2019, 5, S. 1156-1170 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Wiley
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| Schlagwörter: |
| author_facet |
Wu, Xinyu Xie, Haibin Wu, Xinyu Xie, Haibin |
|---|---|
| author |
Wu, Xinyu Xie, Haibin |
| spellingShingle |
Wu, Xinyu Xie, Haibin Applied Stochastic Models in Business and Industry Volatility forecasting using stochastic conditional range model with leverage effect Management Science and Operations Research General Business, Management and Accounting Modeling and Simulation |
| author_sort |
wu, xinyu |
| spelling |
Wu, Xinyu Xie, Haibin 1524-1904 1526-4025 Wiley Management Science and Operations Research General Business, Management and Accounting Modeling and Simulation http://dx.doi.org/10.1002/asmb.2457 <jats:title>Abstract</jats:title><jats:p>In this paper, we propose a stochastic conditional range model with leverage effect (henceforth SCRL) for volatility forecasting. A maximum likelihood method based on the particle filters is developed to estimate the parameters of the SCRL model. Simulation results show that the proposed methodology performs well. We apply the proposed model and methodology to four stock market indices, the Shanghai Stock Exchange Composite Index of China, the Hang Seng Index of Hong Kong, the Nikkei 225 Index of Japan, and the S&P 500 Index of US. Empirical results highlight the value of incorporating leverage effect into range modeling and forecasting. In particular, the results show that our SCRL model outperforms the conditional autoregressive range model, the conditional autoregressive range model with leverage effect, and the stochastic conditional range model in both in‐sample fit and out‐of‐sample forecast.</jats:p> Volatility forecasting using stochastic conditional range model with leverage effect Applied Stochastic Models in Business and Industry |
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Wiley, 2019 |
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Applied Stochastic Models in Business and Industry |
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| title |
Volatility forecasting using stochastic conditional range model with leverage effect |
| title_unstemmed |
Volatility forecasting using stochastic conditional range model with leverage effect |
| title_full |
Volatility forecasting using stochastic conditional range model with leverage effect |
| title_fullStr |
Volatility forecasting using stochastic conditional range model with leverage effect |
| title_full_unstemmed |
Volatility forecasting using stochastic conditional range model with leverage effect |
| title_short |
Volatility forecasting using stochastic conditional range model with leverage effect |
| title_sort |
volatility forecasting using stochastic conditional range model with leverage effect |
| topic |
Management Science and Operations Research General Business, Management and Accounting Modeling and Simulation |
| url |
http://dx.doi.org/10.1002/asmb.2457 |
| publishDate |
2019 |
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1156-1170 |
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<jats:title>Abstract</jats:title><jats:p>In this paper, we propose a stochastic conditional range model with leverage effect (henceforth SCRL) for volatility forecasting. A maximum likelihood method based on the particle filters is developed to estimate the parameters of the SCRL model. Simulation results show that the proposed methodology performs well. We apply the proposed model and methodology to four stock market indices, the Shanghai Stock Exchange Composite Index of China, the Hang Seng Index of Hong Kong, the Nikkei 225 Index of Japan, and the S&P 500 Index of US. Empirical results highlight the value of incorporating leverage effect into range modeling and forecasting. In particular, the results show that our SCRL model outperforms the conditional autoregressive range model, the conditional autoregressive range model with leverage effect, and the stochastic conditional range model in both in‐sample fit and out‐of‐sample forecast.</jats:p> |
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| author | Wu, Xinyu, Xie, Haibin |
| author_facet | Wu, Xinyu, Xie, Haibin, Wu, Xinyu, Xie, Haibin |
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| container_title | Applied Stochastic Models in Business and Industry |
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| description | <jats:title>Abstract</jats:title><jats:p>In this paper, we propose a stochastic conditional range model with leverage effect (henceforth SCRL) for volatility forecasting. A maximum likelihood method based on the particle filters is developed to estimate the parameters of the SCRL model. Simulation results show that the proposed methodology performs well. We apply the proposed model and methodology to four stock market indices, the Shanghai Stock Exchange Composite Index of China, the Hang Seng Index of Hong Kong, the Nikkei 225 Index of Japan, and the S&P 500 Index of US. Empirical results highlight the value of incorporating leverage effect into range modeling and forecasting. In particular, the results show that our SCRL model outperforms the conditional autoregressive range model, the conditional autoregressive range model with leverage effect, and the stochastic conditional range model in both in‐sample fit and out‐of‐sample forecast.</jats:p> |
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| imprint | Wiley, 2019 |
| imprint_str_mv | Wiley, 2019 |
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| series | Applied Stochastic Models in Business and Industry |
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| spelling | Wu, Xinyu Xie, Haibin 1524-1904 1526-4025 Wiley Management Science and Operations Research General Business, Management and Accounting Modeling and Simulation http://dx.doi.org/10.1002/asmb.2457 <jats:title>Abstract</jats:title><jats:p>In this paper, we propose a stochastic conditional range model with leverage effect (henceforth SCRL) for volatility forecasting. A maximum likelihood method based on the particle filters is developed to estimate the parameters of the SCRL model. Simulation results show that the proposed methodology performs well. We apply the proposed model and methodology to four stock market indices, the Shanghai Stock Exchange Composite Index of China, the Hang Seng Index of Hong Kong, the Nikkei 225 Index of Japan, and the S&P 500 Index of US. Empirical results highlight the value of incorporating leverage effect into range modeling and forecasting. In particular, the results show that our SCRL model outperforms the conditional autoregressive range model, the conditional autoregressive range model with leverage effect, and the stochastic conditional range model in both in‐sample fit and out‐of‐sample forecast.</jats:p> Volatility forecasting using stochastic conditional range model with leverage effect Applied Stochastic Models in Business and Industry |
| spellingShingle | Wu, Xinyu, Xie, Haibin, Applied Stochastic Models in Business and Industry, Volatility forecasting using stochastic conditional range model with leverage effect, Management Science and Operations Research, General Business, Management and Accounting, Modeling and Simulation |
| title | Volatility forecasting using stochastic conditional range model with leverage effect |
| title_full | Volatility forecasting using stochastic conditional range model with leverage effect |
| title_fullStr | Volatility forecasting using stochastic conditional range model with leverage effect |
| title_full_unstemmed | Volatility forecasting using stochastic conditional range model with leverage effect |
| title_short | Volatility forecasting using stochastic conditional range model with leverage effect |
| title_sort | volatility forecasting using stochastic conditional range model with leverage effect |
| title_unstemmed | Volatility forecasting using stochastic conditional range model with leverage effect |
| topic | Management Science and Operations Research, General Business, Management and Accounting, Modeling and Simulation |
| url | http://dx.doi.org/10.1002/asmb.2457 |