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Predictive models for disaggregate stock market volatility

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Abstract

This paper incorporates macroeconomic determinants into the forecasting model of industry-level stock return volatility in order to detect whether different macroeconomic factors can forecast the volatility of various industries. To explain different fluctuation characteristics among industries, we identified a set of macroeconomic determinants to examine their effects. The Clark and West (J Econom 138(1):291–311, 2007) test is employed to verify whether the new forecasting models, which vary among industries based on the in-sample results, make better predictions than the two benchmark models. Our results show that default return and default yield have a significant impact on stock return volatility.

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Notes

  1. The Great Moderation was a reduction in the volatility of business cycle fluctuations starting in the mid-1980s. It was observed that real gross domestic product growth, industrial production, monthly payroll employment, and the unemployment rate began to decline in volatility, caused by institutional and structural changes in developed nations.

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Acknowledgements

We thank the editor and anonymous referees for their constructive comments, which greatly improved the quality of the paper. We owe much to Liugang Sheng and Julan Du, and seminar participants at The Chinese University of Hong Kong, for their helpful comments. We are also very much indebted to Min Chen, Margaret Loo, Yingshi Chen, Junjie Guo, and Mandy Cheung, for their assistance with the research. Any remaining errors are ours.

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  1. Department of Economics and Lau Chor Tak Institute of Global Economics and Finance, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Terence Tai-Leung Chong

  2. Department of Economics, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Shiyu Lin

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  1. Terence Tai-Leung Chong
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Correspondence to Terence Tai-Leung Chong.

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Chong, T.TL., Lin, S. Predictive models for disaggregate stock market volatility. Financ Mark Portf Manag 31, 261–288 (2017). https://doi.org/10.1007/s11408-017-0291-2

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