 Stock market volatility predictability in a data-rich world: A new insightFeng Ma, Jiqian Wang, M.I.M. Wahab and Yuanhui Ma International Journal of Forecasting, 2023, vol. 39, issue 4, 1804-1819 Abstract: This study develops a shrinkage method, LASSO with a Markov regime-switching model (MRS-LASSO), to predict US stock market volatility. A set of 17 well-known macroeconomic and financial factors are used. The out-of-sample results reveal that the MRS-LASSO model yields statistically and economically significant volatility predictions. We further investigate the predictability of MRS-LASSO with respect to different market conditions, business cycles, and variable selection. Three factors (equity market returns, a short-term reversal factor, and a consumer sentiment index) are the most frequent predictors. To investigate the practical implications, we construct the expected variance risk premium (VRP) by using volatility forecasts generated from the LASSO and MRS-LASSO models to forecast future stock returns and find that those models are also powerful. Keywords: Shrinkage method; Markov regime switching; Equity volatility forecasting; Variance risk premium; Forecast combination (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:39:y:2023:i:4:p:1804-1819 DOI: 10.1016/j.ijforecast.2022.08.010 Access Statistics for this article International Journal of Forecasting is currently edited by R. J. Hyndman More articles in International Journal of Forecasting from Elsevier |
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