Forecasting Realized Stock-Market Volatility: Do Industry Returns have Predictive Value?

Riza Demirer, Rangan Gupta and Christian Pierdzioch

No 2020107, Working Papers from University of Pretoria, Department of Economics

Abstract: Yes, they do. Utilizing a machine-learning technique known as random forests to compute forecasts of realized (good and bad) stock market volatility, we show that incorporating the information in lagged industry returns can help improve out-of sample forecasts of aggregate stock market volatility. While the predictive contribution of industry level returns is not constant over time, industrials and materials play a dominant predictive role during the aftermath of the 2008 global financial crisis, highlighting the informational value of real economic activity on stock market volatility dynamics. Finally, we show that incorporating lagged industry returns in aggregate level volatility forecasts benefits forecasters who are particularly concerned about under-predicting market volatility, yielding greater economic benefits for forecasters as the degree of risk aversion increases.

Keywords: Stock market; Realized volatility; Industry returns; Market efficiency and information (search for similar items in EconPapers)
JEL-codes: G17 Q02 Q47 (search for similar items in EconPapers)
Pages: 38 pages
Date: 2020-12
New Economics Papers: this item is included in nep-fmk, nep-for and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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