Forecasting realized volatility in a changing world: A dynamic model averaging approach
In this study, we forecast the realized volatility of the S&P 500 index using the heterogeneous autoregressive model for realized volatility (HAR-RV) and its various extensions. Our models take into account the time-varying property of the models’ parameters and the volatility of realized volati...
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| Published in: | Journal of banking & finance Vol. 64; pp. 136 - 149 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Amsterdam
Elsevier B.V
01.03.2016
Elsevier Sequoia S.A |
| Subjects: | |
| ISSN: | 0378-4266, 1872-6372 |
| Online Access: | Get full text |
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| Abstract | In this study, we forecast the realized volatility of the S&P 500 index using the heterogeneous autoregressive model for realized volatility (HAR-RV) and its various extensions. Our models take into account the time-varying property of the models’ parameters and the volatility of realized volatility. A dynamic model averaging (DMA) approach is used to combine the forecasts of the individual models. Our empirical results suggest that DMA can generate more accurate forecasts than individual model in both statistical and economic senses. Models that use time-varying parameters have greater forecasting accuracy than models that use the constant coefficients. The superiority of time-varying parameter models is also found in volatility density forecasting. |
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| AbstractList | In this study, we forecast the realized volatility of the S&P 500 index using the heterogeneous autoregressive model for realized volatility (HAR-RV) and its various extensions. Our models take into account the time-varying property of the models' parameters and the volatility of realized volatility. A dynamic model averaging (DMA) approach is used to combine the forecasts of the individual models. Our empirical results suggest that DMA can generate more accurate forecasts than individual model in both statistical and economic senses. Models that use time-varying parameters have greater forecasting accuracy than models that use the constant coefficients. The superiority of time-varying parameter models is also found in volatility density forecasting. All rights reserved, Elsevier In this study, we forecast the realized volatility of the S&P 500 index using the heterogeneous autoregressive model for realized volatility (HAR-RV) and its various extensions. Our models take into account the time-varying property of the models’ parameters and the volatility of realized volatility. A dynamic model averaging (DMA) approach is used to combine the forecasts of the individual models. Our empirical results suggest that DMA can generate more accurate forecasts than individual model in both statistical and economic senses. Models that use time-varying parameters have greater forecasting accuracy than models that use the constant coefficients. The superiority of time-varying parameter models is also found in volatility density forecasting. In this study, we forecast the realized volatility of the S&P 500 index using the heterogeneous autoregressive model for realized volatility (HAR-RV) and its various extensions. Our models take into account the time-varying property of the models' parameters and the volatility of realized volatility. A dynamic model averaging (DMA) approach is used to combine the forecasts of the individual models. Our empirical results suggest that DMA can generate more accurate forecasts than individual model in both statistical and economic senses. Models that use time-varying parameters have greater forecasting accuracy than models that use the constant coefficients. The superiority of time-varying parameter models is also found in volatility density forecasting. [web URL: http://www.sciencedirect.com/science/article/pii/S0378426615003647] |
| Author | Ma, Feng Wei, Yu Wu, Chongfeng Wang, Yudong |
| Author_xml | – sequence: 1 givenname: Yudong surname: Wang fullname: Wang, Yudong email: wangyudongnj@126.com organization: School of Economics and Management, Nanjing University of Science and Technology, China – sequence: 2 givenname: Feng surname: Ma fullname: Ma, Feng email: mafeng575@126.com organization: School of Economics and Management, Southwest Jiao Tong University, China – sequence: 3 givenname: Yu surname: Wei fullname: Wei, Yu email: weiyusy@126.com organization: School of Economics and Management, Southwest Jiao Tong University, China – sequence: 4 givenname: Chongfeng surname: Wu fullname: Wu, Chongfeng email: cfwu@sjtu.edu.cn organization: Antai College of Economics and Management, Shanghai Jiao Tong University, China |
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| PublicationTitle | Journal of banking & finance |
| PublicationYear | 2016 |
| Publisher | Elsevier B.V Elsevier Sequoia S.A |
| Publisher_xml | – name: Elsevier B.V – name: Elsevier Sequoia S.A |
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| SubjectTerms | Averaging Dynamic model averaging Econometrics Economic models Forecasting Forecasting techniques Portfolio Realized volatility S&P 500 index Securities markets Studies Time-varying parameters Volatility |
| Title | Forecasting realized volatility in a changing world: A dynamic model averaging approach |
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