Forecasting Volatility with Time-Varying Coefficient Regressions
We extend the heterogeneous autoregressive- (HAR-) type models by explicitly considering the time variation of coefficients in a Bayesian framework and comprehensively comparing the performances of these time-varying coefficient models and constant coefficient models in forecasting the volatility of...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2020/3151473 |
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| _version_ | 1832547625668182016 |
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| author | Qifeng Zhu Miman You Shan Wu |
| author_facet | Qifeng Zhu Miman You Shan Wu |
| author_sort | Qifeng Zhu |
| collection | DOAJ |
| description | We extend the heterogeneous autoregressive- (HAR-) type models by explicitly considering the time variation of coefficients in a Bayesian framework and comprehensively comparing the performances of these time-varying coefficient models and constant coefficient models in forecasting the volatility of the Shanghai Stock Exchange Composite Index (SSEC). The empirical results suggest that time-varying coefficient models do generate more accurate out-of-sample forecasts than the corresponding constant coefficient models. By capturing and studying the time series of time-varying coefficients of the predictors, we find that the coefficients (predictive ability) of heterogeneous volatilities are negatively correlated and the leverage effect is not significant or inverse during certain periods. Portfolio exercises also demonstrate the superiority of time-varying coefficient models. |
| format | Article |
| id | doaj-art-32f3bdbd99a1404e905660b855a615c1 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-32f3bdbd99a1404e905660b855a615c12025-02-03T06:43:51ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2020-01-01202010.1155/2020/31514733151473Forecasting Volatility with Time-Varying Coefficient RegressionsQifeng Zhu0Miman You1Shan Wu2School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu, ChinaSchool of Mathematics and Statistics, North China University of Water Resources and Electric Power, Zhengzhou, ChinaSchool of Finance, Nanjing University of Finance and Economics, Nanjing, ChinaWe extend the heterogeneous autoregressive- (HAR-) type models by explicitly considering the time variation of coefficients in a Bayesian framework and comprehensively comparing the performances of these time-varying coefficient models and constant coefficient models in forecasting the volatility of the Shanghai Stock Exchange Composite Index (SSEC). The empirical results suggest that time-varying coefficient models do generate more accurate out-of-sample forecasts than the corresponding constant coefficient models. By capturing and studying the time series of time-varying coefficients of the predictors, we find that the coefficients (predictive ability) of heterogeneous volatilities are negatively correlated and the leverage effect is not significant or inverse during certain periods. Portfolio exercises also demonstrate the superiority of time-varying coefficient models.http://dx.doi.org/10.1155/2020/3151473 |
| spellingShingle | Qifeng Zhu Miman You Shan Wu Forecasting Volatility with Time-Varying Coefficient Regressions Discrete Dynamics in Nature and Society |
| title | Forecasting Volatility with Time-Varying Coefficient Regressions |
| title_full | Forecasting Volatility with Time-Varying Coefficient Regressions |
| title_fullStr | Forecasting Volatility with Time-Varying Coefficient Regressions |
| title_full_unstemmed | Forecasting Volatility with Time-Varying Coefficient Regressions |
| title_short | Forecasting Volatility with Time-Varying Coefficient Regressions |
| title_sort | forecasting volatility with time varying coefficient regressions |
| url | http://dx.doi.org/10.1155/2020/3151473 |
| work_keys_str_mv | AT qifengzhu forecastingvolatilitywithtimevaryingcoefficientregressions AT mimanyou forecastingvolatilitywithtimevaryingcoefficientregressions AT shanwu forecastingvolatilitywithtimevaryingcoefficientregressions |