Forecasting the aggregate stock market volatility in a data-rich world

ABSTRACT

In this article, we utilize the basic lasso and elastic net models to revisit the predictive performance of aggregate stock market volatility in a data-rich world. Motivated by the existing literature, we determine several candidate predictors that have 22 technical indicators and 14 macroeconomic and financial variables. Our out-of-sample results reveal several noteworthy findings. First, few macroeconomic and financial variables and most of technical indicators have superior performance relative to the benchmark model. Second, combination forecasts are able to significantly beat the benchmark and some signal predictors Third, the lasso and elastic models with all predictors can generate more accurate forecasts than the benchmark and some other predictors in both the statistical and economic sense. Fourth, the lasso and elastic models exhibit higher forecast accuracy during periods of expansions and recessions. Finally, our findings are robust to several tests, such as different forecasting windows, forecasting models, and forecasting evaluations.

KEYWORDS:

• Volatility forecasting
• Monthly realized volatility
• Combination forecasts
• Lasso and elastic net
• Economic value

JEL CLASSIFICATION:

• B22
• C22
• C53

Acknowledgments

We are thankful to the editor, Mark Taylor, who provided us the opportunity to revise this paper, and to two anonymous referees for providing very valuable comments that helped us to substantially improve the quality of our novel paper. The authors are grateful of the Natural Science Foundation of China [71671145; 71701170], the humanities and social science fund of the ministry of education [17YJC790105; 17XJCZH002], and the Fundamental research funds for the central universities [30919013232, 2682017WCX01].

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ The monthly RV series can be downloaded from Professor Amit Goyal’s homepage (http://www.hec.unil.ch/agoyal/). Specifically, Goyal and Welch (2008) provide more details on monthly RV to readers in their paper.

² The data can be freely downloaded from, http://www.hec.unil.ch/agoyal/(Goyal and Welch 2008), and http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html#Research (Fama and French 1993). The MKT can be calculated by authors from the downloaded data.

³ The monthly price data can be acquired from Yahoo Finance (https://finance.yahoo.com/quote/%5EGSPC?p = ^GSPC).

⁴ The volume data can be acquired from Yahoo Finance (https://finance.yahoo.com/quote/%5EGSPC?p = ^GSPC).

⁵ We are grateful for Professor Amit Goyal, Professor Guofu Zhou, Professor Fama and Professor French, who share their valuable data and code to save us a substantial amount of time searching for primary data.

⁶ The value of the figures in the parentheses of DMSPE (θ) represents a discount factor.

⁷ Regarding the economic evaluation, we don’t consider the strategies of Degiannakis and Filis (2019), this is main because the monthly S&P 500 index volatility is not traded index, and more importantly, we haven’t the transaction costs per trade, an annual expense ratio and management fee.

Additional information

Funding

This work was supported by the the Natural Science Foundation of China [71671145, 71701170]; the Humanities and Social Science Fund of the Ministry of Education [17YJC790105, 17XJCZH002];the Fundamental Research Funds for the Central Universities [30919013232, 682017WCX01, 2682018WXTD05].