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A unified framework jointly explaining business conditions, stock returns, volatility and “volatility feedback news” effects

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  • Kim Chang-Jin

    (Department of Economics, University of Washington, Seattle, WA 98195, USA)

  • Kim Yunmi

    (Department of Economics, University of Seoul, Seoul 02504, Korea)

Abstract

One of central questions to macroeconomics and finance has been whether macroeconomic factors are useful predictors for expected stock returns. The general consensus is somewhat surprising in that financial factors, rather than macroeconomic factors, have predictive power on stock returns. Such predictability of financial factors is justified on the ground that those factors can act as a proxy for future business conditions and undiversifiable risk. Hence, they should be priced in terms of expected returns. However, as suggested by Campbell, S., and F. Diebold. 2009. “Stock Returns and Expected Business Conditions: Half a Century of Direct Evidence.” Journal of Business & Economic Statistics 27 (2): 266–278, such a justification can be puzzling because macroeconomic factors are likely to have a closer and more direct link to future business conditions than financial factors. In this paper, we will attempt to solve this puzzling problem by accounting for market volatility when measuring the relationship between stock returns and macroeconomic factors. As a result, we propose a unified framework in which the three components of macroeconomic factors, market volatility, and stock returns are jointly embedded.

Suggested Citation

  • Kim Chang-Jin & Kim Yunmi, 2019. "A unified framework jointly explaining business conditions, stock returns, volatility and “volatility feedback news” effects," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 23(2), pages 1-14, April.
    • Handle: RePEc:bpj:sndecm:v:23:y:2019:i:2:p:14:n:3
    DOI: 10.1515/snde-2016-0151
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    References listed on IDEAS

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    1. Campbell, John Y. & Hentschel, Ludger, 1992. "No news is good news *1: An asymmetric model of changing volatility in stock returns," Journal of Financial Economics, Elsevier, vol. 31(3), pages 281-318, June.
    2. Paul Beaudry & Franck Portier, 2014. "News-Driven Business Cycles: Insights and Challenges," Journal of Economic Literature, American Economic Association, vol. 52(4), pages 993-1074, December.
    3. Stephen A. Ross, 2013. "The Arbitrage Theory of Capital Asset Pricing," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 1, pages 11-30, World Scientific Publishing Co. Pte. Ltd..
    4. Campbell, Sean D. & Diebold, Francis X., 2009. "Stock Returns and Expected Business Conditions: Half a Century of Direct Evidence," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(2), pages 266-278.
    5. Yunmi Kim & Charles R. Nelson, 2014. "Pricing Stock Market Volatility: Does it Matter whether the Volatility is Related to the Business Cycle?," Journal of Financial Econometrics, Oxford University Press, vol. 12(2), pages 307-328.
    6. Kim, Chang-Jin & Morley, James C & Nelson, Charles R, 2004. "Is There a Positive Relationship between Stock Market Volatility and the Equity Premium?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 36(3), pages 339-360, June.
    7. Schwert, G William, 1981. "The Adjustment of Stock Prices to Information about Inflation," Journal of Finance, American Finance Association, vol. 36(1), pages 15-29, March.
    8. John Y. Campbell, Robert J. Shiller, 1988. "The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors," The Review of Financial Studies, Society for Financial Studies, vol. 1(3), pages 195-228.
    9. French, Kenneth R. & Schwert, G. William & Stambaugh, Robert F., 1987. "Expected stock returns and volatility," Journal of Financial Economics, Elsevier, vol. 19(1), pages 3-29, September.
    10. Paul Beaudry & Bernd Lucke, 2010. "Letting Different Views about Business Cycles Compete," NBER Chapters, in: NBER Macroeconomics Annual 2009, Volume 24, pages 413-455, National Bureau of Economic Research, Inc.
    11. Yu, Jun, 2005. "On leverage in a stochastic volatility model," Journal of Econometrics, Elsevier, vol. 127(2), pages 165-178, August.
    12. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    13. Bollerslev, Tim & Zhou, Hao, 2006. "Volatility puzzles: a simple framework for gauging return-volatility regressions," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 123-150.
    14. Filardo, Andrew J, 1994. "Business-Cycle Phases and Their Transitional Dynamics," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 299-308, July.
    15. Mark J. Flannery & Aris A. Protopapadakis, 2002. "Macroeconomic Factors Do Influence Aggregate Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 15(3), pages 751-782.
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    More about this item

    Keywords

    expected returns; macroeconomic factors; market volatility; regime-switching; “volatility feedback news” effects;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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