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Technological Shocks and Stock Market Volatility Over a Century: A GARCH-MIDAS Approach

Author

Listed:
  • Afees A. Salisu

    (Centre for Econometrics & Applied Research, Ibadan, Nigeria; Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

  • Riza Demirer

    (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1102, USA)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

Abstract

This paper provides a novel perspective to the innovation-stock market nexus by examining the predictive relationship between technological shocks and stock market volatility using data over a period of more than 140 years. Utilizing annual patent data for the U.S. and a large set of economies to create proxies for local and global technological shocks and a mixed-sampling data (MIDAS) framework, we present robust evidence that technological shocks capture significant predictive information regarding future realizations of stock market volatility, both in- and out-of-sample and at both the short and long forecast horizons. Further economic analysis shows that investment portfolios created by the volatility forecasts obtained from the forecasting models that incorporate technological shocks as predictors in volatility models experience significantly lower return volatility in the out-of-sample horizons, which in turn helps to improve the risk-return profile of those portfolios. Our findings present a novel take on the nexus between technological innovations and stock market dynamics and paves the way for several interesting avenues for future research regarding the role of technological innovations on asset pricing tests and portfolio models.

Suggested Citation

  • Afees A. Salisu & Riza Demirer & Rangan Gupta, 2023. "Technological Shocks and Stock Market Volatility Over a Century: A GARCH-MIDAS Approach," Working Papers 202308, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:202308
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    as
    1. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    2. Nasr, Adnen Ben & Lux, Thomas & Ajmi, Ahdi Noomen & Gupta, Rangan, 2016. "Forecasting the volatility of the Dow Jones Islamic Stock Market Index: Long memory vs. regime switching," International Review of Economics & Finance, Elsevier, vol. 45(C), pages 559-571.
    3. Leonid Kogan & Dimitris Papanikolaou, 2014. "Growth Opportunities, Technology Shocks, and Asset Prices," Journal of Finance, American Finance Association, vol. 69(2), pages 675-718, April.
    4. Rasmus Lentz & Dale T. Mortensen, 2008. "An Empirical Model of Growth Through Product Innovation," Econometrica, Econometric Society, vol. 76(6), pages 1317-1373, November.
    5. Aghion, Philippe & Howitt, Peter, 1992. "A Model of Growth through Creative Destruction," Econometrica, Econometric Society, vol. 60(2), pages 323-351, March.
    6. Shiller, Robert J, 1981. "Do Stock Prices Move Too Much to be Justified by Subsequent Changes in Dividends?," American Economic Review, American Economic Association, vol. 71(3), pages 421-436, June.
    7. Robert F. Engle & Eric Ghysels & Bumjean Sohn, 2013. "Stock Market Volatility and Macroeconomic Fundamentals," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 776-797, July.
    8. Lucia Foster & John C. Haltiwanger & C. J. Krizan, 2001. "Aggregate Productivity Growth: Lessons from Microeconomic Evidence," NBER Chapters, in: New Developments in Productivity Analysis, pages 303-372, National Bureau of Economic Research, Inc.
    9. Bernanke, Ben S, 1983. "Nonmonetary Effects of the Financial Crisis in Propagation of the Great Depression," American Economic Review, American Economic Association, vol. 73(3), pages 257-276, June.
    10. Sharma, Susan Sunila & Narayan, Paresh Kumar, 2022. "Technology shocks and stock returns: A long-term perspective," Journal of Empirical Finance, Elsevier, vol. 68(C), pages 67-83.
    11. Murray Carlson & Adlai Fisher & Ron Giammarino, 2004. "Corporate Investment and Asset Price Dynamics: Implications for the Cross-section of Returns," Journal of Finance, American Finance Association, vol. 59(6), pages 2577-2603, December.
    12. Christian Conrad & Karin Loch, 2015. "Anticipating Long‐Term Stock Market Volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(7), pages 1090-1114, November.
    13. Ruipeng Liu & Rangan Gupta, 2022. "Investors’ Uncertainty and Forecasting Stock Market Volatility," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 23(3), pages 327-337, July.
    14. Ufuk Akcigit & William R. Kerr, 2018. "Growth through Heterogeneous Innovations," Journal of Political Economy, University of Chicago Press, vol. 126(4), pages 1374-1443.
    15. Luboš Pástor & Pietro Veronesi, 2009. "Technological Revolutions and Stock Prices," American Economic Review, American Economic Association, vol. 99(4), pages 1451-1483, September.
    16. Nicolae Gârleanu & Stavros Panageas & Jianfeng Yu, 2012. "Technological Growth and Asset Pricing," Journal of Finance, American Finance Association, vol. 67(4), pages 1265-1292, August.
    17. Hossein Asgharian & Ai Jun Hou & Farrukh Javed, 2013. "The Importance of the Macroeconomic Variables in Forecasting Stock Return Variance: A GARCH‐MIDAS Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(7), pages 600-612, November.
    18. José Rangel & Robert Engle, 2012. "The Factor–Spline–GARCH Model for High and Low Frequency Correlations," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 109-124.
    19. Colacito, Riccardo & Engle, Robert F. & Ghysels, Eric, 2011. "A component model for dynamic correlations," Journal of Econometrics, Elsevier, vol. 164(1), pages 45-59, September.
    20. David Hirshleifer & Po-Hsuan Hsu & Dongmei Li, 2018. "Innovative Originality, Profitability, and Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2553-2605.
    21. Hsu, Po-Hsuan, 2009. "Technological innovations and aggregate risk premiums," Journal of Financial Economics, Elsevier, vol. 94(2), pages 264-279, November.
    22. Tor Jakob Klette & Samuel Kortum, 2004. "Innovating Firms and Aggregate Innovation," Journal of Political Economy, University of Chicago Press, vol. 112(5), pages 986-1018, October.
    23. Das, Sonali & Demirer, Riza & Gupta, Rangan & Mangisa, Siphumlile, 2019. "The effect of global crises on stock market correlations: Evidence from scalar regressions via functional data analysis," Structural Change and Economic Dynamics, Elsevier, vol. 50(C), pages 132-147.
    24. Xiaoquan Jiang, 2010. "Return dispersion and expected returns," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 24(2), pages 107-135, June.
    25. Robert F. Engle & Jose Gonzalo Rangel, 2008. "The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes," The Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1187-1222, May.
    26. Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna, 2022. "A moving average heterogeneous autoregressive model for forecasting the realized volatility of the US stock market: Evidence from over a century of data," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 384-400, January.
    27. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    28. Hirshleifer, David & Hsu, Po-Hsuan & Li, Dongmei, 2013. "Innovative efficiency and stock returns," Journal of Financial Economics, Elsevier, vol. 107(3), pages 632-654.
    29. Laurent A. F. Callot & Anders B. Kock & Marcelo C. Medeiros, 2017. "Modeling and Forecasting Large Realized Covariance Matrices and Portfolio Choice," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(1), pages 140-158, January.
    30. Jonathan B. Berk & Richard C. Green & Vasant Naik, 1999. "Optimal Investment, Growth Options, and Security Returns," Journal of Finance, American Finance Association, vol. 54(5), pages 1553-1607, October.
    31. Liu, Jing & Ma, Feng & Tang, Yingkai & Zhang, Yaojie, 2019. "Geopolitical risk and oil volatility: A new insight," Energy Economics, Elsevier, vol. 84(C).
    32. Ron Giammarino & Murray Carlson & Adlai Fisher, 2004. "Corporate Investment and Asset Price Dynamics: Implications for Post-SEO Performance," 2004 Meeting Papers 812, Society for Economic Dynamics.
    33. Mark Doms & Eric J. Bartelsman, 2000. "Understanding Productivity: Lessons from Longitudinal Microdata," Journal of Economic Literature, American Economic Association, vol. 38(3), pages 569-594, September.
    34. Adnen Ben Nasr & Ahdi Noomen Ajmi & Rangan Gupta, 2014. "Modelling the volatility of the Dow Jones Islamic Market World Index using a fractionally integrated time-varying GARCH (FITVGARCH) model," Applied Financial Economics, Taylor & Francis Journals, vol. 24(14), pages 993-1004, July.
    35. Clements, Michael P & Galvão, Ana Beatriz, 2008. "Macroeconomic Forecasting With Mixed-Frequency Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 546-554.
    36. Fang, Tong & Lee, Tae-Hwy & Su, Zhi, 2020. "Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 36-49.
    37. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    38. Howard Kung & Lukas Schmid, 2015. "Innovation, Growth, and Asset Prices," Journal of Finance, American Finance Association, vol. 70(3), pages 1001-1037, June.
    39. Zhanhui Chen & Ralitsa Petkova, 2012. "Does Idiosyncratic Volatility Proxy for Risk Exposure?," The Review of Financial Studies, Society for Financial Studies, vol. 25(9), pages 2745-2787.
    40. Dimitris Papanikolaou, 2011. "Investment Shocks and Asset Prices," Journal of Political Economy, University of Chicago Press, vol. 119(4), pages 639-685.
    41. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    42. Hsu, Po-Hsuan & Huang, Dayong, 2010. "Technology prospects and the cross-section of stock returns," Journal of Empirical Finance, Elsevier, vol. 17(1), pages 39-53, January.
    43. Daron Acemoglu & Ufuk Akcigit & Murat Alp Celik, 2022. "Radical and Incremental Innovation: The Roles of Firms, Managers, and Innovators," American Economic Journal: Macroeconomics, American Economic Association, vol. 14(3), pages 199-249, July.
    44. Charles Cao & Timothy Simin & Jing Zhao, 2008. "Can Growth Options Explain the Trend in Idiosyncratic Risk?," The Review of Financial Studies, Society for Financial Studies, vol. 21(6), pages 2599-2633, November.
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    More about this item

    Keywords

    Patents; Technology shocks; Stock market volatility; Forecasting;
    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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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