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Forecasting stock market volatility with a large number of predictors: New evidence from the MS-MIDAS-LASSO model.
Li, Xiafei; Liang, Chao; Ma, Feng.
  • Li X; School of Economics & Management, Southwest Jiaotong University, No. 111, North 1St Section, 2nd Ring Road, Chengdu, 610031 China.
  • Liang C; School of Economics & Management, Southwest Jiaotong University, No. 111, North 1St Section, 2nd Ring Road, Chengdu, 610031 China.
  • Ma F; School of Economics & Management, Southwest Jiaotong University, No. 111, North 1St Section, 2nd Ring Road, Chengdu, 610031 China.
Ann Oper Res ; : 1-40, 2022 Apr 26.
Article in English | MEDLINE | ID: covidwho-1813719
ABSTRACT
This paper explores the effectiveness of predictors, including nine economic policy uncertainty indicators, four market sentiment indicators and two financial stress indices, in predicting the realized volatility of the S&P 500 index. We employ the MIDAS-RV framework and construct the MIDAS-LASSO model and its regime switching extension (namely, MS-MIDAS-LASSO). First, among all considered predictors, the economic policy uncertainty indices (especially the equity market volatility index) and the CBOE volatility index are the most noteworthy predictors. Although the CBOE volatility index has the best predictive ability for stock market volatility, its predictive ability has weakened during the COVID-19 epidemic, and the equity market volatility index is best during this period. Second, the MS-MIDAS-LASSO model has the best predictive performance compared to other competing models. The superior forecasting performance of this model is robust, even when distinguishing between high- and low-volatility periods. Finally, the prediction accuracy of the MS-MIDAS-LASSO model even outperforms the traditional LASSO strategy and its regime switching extension. Furthermore, the superior predictive performance of this model has not changed with the outbreak of the COVID-19 epidemic.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Ann Oper Res Year: 2022 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Ann Oper Res Year: 2022 Document Type: Article