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Business Applications and State-Level Stock Market Realized Volatility: A Forecasting Experiment

Author

Listed:
  • Matteo Bonato

    (Department of Economics and Econometrics, University of Johannesburg, Auckland Park, South Africa; IPAG Business School, 184 Boulevard Saint-Germain, 75006 Paris, France)

  • Oguzhan Cepni

    (Copenhagen Business School, Department of Economics, Porcelaenshaven 16A, Frederiksberg DK-2000, Denmark; Central Bank of the Republic of Turkey, Haci Bayram Mah. Istiklal Cad. No:10 06050, Ankara, Turkey)

  • Rangan Gupta

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

  • Christian Pierdzioch

    (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany)

Abstract

We analyze the predictive value of state-level business applications, as a proxy of local investor sentiment, for the state-level realized US stock-market volatility. We use highfrequency data for the period from September, 2011 to October, 2021 to compute realized volatility. We show, using an extended version of the popular heterogenous autoregressive realized volatility model, that business applications have predictive value at intermediate and long prediction horizons, after controlling for realized moments (realized skewness, realized kurtosis, realized tail risks), for realized state-level stock-market volatility, and for upside (``good") and downside (``bad") realized volatility.

Suggested Citation

  • Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2022. "Business Applications and State-Level Stock Market Realized Volatility: A Forecasting Experiment," Working Papers 202247, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202247
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    1. Kumari, Jyoti & Mahakud, Jitendra, 2015. "Does investor sentiment predict the asset volatility? Evidence from emerging stock market India," Journal of Behavioral and Experimental Finance, Elsevier, vol. 8(C), pages 25-39.
    2. 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.
    3. 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.
    4. Thomas Chaney & David Sraer & David Thesmar, 2012. "The Collateral Channel: How Real Estate Shocks Affect Corporate Investment," American Economic Review, American Economic Association, vol. 102(6), pages 2381-2409, October.
    5. Ole E. Barndorff-Nielsen & Neil Shephard, 2006. "Econometrics of Testing for Jumps in Financial Economics Using Bipower Variation," Journal of Financial Econometrics, Oxford University Press, vol. 4(1), pages 1-30.
    6. Elie Bouri & Afees A. Salisu & Rangan Gupta, 2023. "The predictive power of Bitcoin prices for the realized volatility of US stock sector returns," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-22, December.
    7. Malcolm Baker & Jeffrey Wurgler, 2006. "Investor Sentiment and the Cross‐Section of Stock Returns," Journal of Finance, American Finance Association, vol. 61(4), pages 1645-1680, August.
    8. Sheng, Xin & Gupta, Rangan & Çepni, Oğuzhan, 2022. "The effects of climate risks on economic activity in a panel of US states: The role of uncertainty," Economics Letters, Elsevier, vol. 213(C).
    9. Zhi Da & Joseph Engelberg & Pengjie Gao, 2015. "Editor's Choice The Sum of All FEARS Investor Sentiment and Asset Prices," The Review of Financial Studies, Society for Financial Studies, vol. 28(1), pages 1-32.
    10. Graham Elliott & Allan Timmermann & Ivana Komunjer, 2005. "Estimation and Testing of Forecast Rationality under Flexible Loss," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(4), pages 1107-1125.
    11. John Y. Campbell & Robert J. Shiller, 1988. "Stock Prices, Earnings and Expected Dividends," Cowles Foundation Discussion Papers 858, Cowles Foundation for Research in Economics, Yale University.
    12. Malcolm Baker & Jeffrey Wurgler, 2007. "Investor Sentiment in the Stock Market," Journal of Economic Perspectives, American Economic Association, vol. 21(2), pages 129-152, Spring.
    13. Muller, Ulrich A. & Dacorogna, Michel M. & Dave, Rakhal D. & Olsen, Richard B. & Pictet, Olivier V. & von Weizsacker, Jacob E., 1997. "Volatilities of different time resolutions -- Analyzing the dynamics of market components," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 213-239, June.
    14. Amaya, Diego & Christoffersen, Peter & Jacobs, Kris & Vasquez, Aurelio, 2015. "Does realized skewness predict the cross-section of equity returns?," Journal of Financial Economics, Elsevier, vol. 118(1), pages 135-167.
    15. Zhang, Wei & Li, Xiao & Shen, Dehua & Teglio, Andrea, 2016. "Daily happiness and stock returns: Some international evidence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 460(C), pages 201-209.
    16. George M. Korniotis & Alok Kumar, 2013. "State-Level Business Cycles and Local Return Predictability," Journal of Finance, American Finance Association, vol. 68(3), pages 1037-1096, June.
    17. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
    18. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    19. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    20. Mei, Dexiang & Liu, Jing & Ma, Feng & Chen, Wang, 2017. "Forecasting stock market volatility: Do realized skewness and kurtosis help?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 153-159.
    21. Deven Bathia & Don Bredin, 2013. "An examination of investor sentiment effect on G7 stock market returns," The European Journal of Finance, Taylor & Francis Journals, vol. 19(9), pages 909-937, October.
    22. Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2021. "Forecasting realized volatility of bitcoin returns: tail events and asymmetric loss," The European Journal of Finance, Taylor & Francis Journals, vol. 27(16), pages 1626-1644, November.
    23. 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.
    24. Graham Elliott & Ivana Komunjer & Allan Timmermann, 2008. "Biases in Macroeconomic Forecasts: Irrationality or Asymmetric Loss?," Journal of the European Economic Association, MIT Press, vol. 6(1), pages 122-157, March.
    25. repec:bla:jfinan:v:43:y:1988:i:3:p:661-76 is not listed on IDEAS
    26. Michael McAleer & Marcelo Medeiros, 2008. "Realized Volatility: A Review," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 10-45.
    27. Lux, Thomas, 1997. "Time variation of second moments from a noise trader/infection model," Journal of Economic Dynamics and Control, Elsevier, vol. 22(1), pages 1-38, November.
    28. repec:hal:spmain:info:hdl:2441/75koqefued8i7pihbrl9u84p4u is not listed on IDEAS
    29. David E. Rapach & Jack K. Strauss & Guofu Zhou, 2010. "Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy," The Review of Financial Studies, Society for Financial Studies, vol. 23(2), pages 821-862, February.
    30. Mehmet Balcilar & Riza Demirer & Rangan Gupta & Mark E. Wohar, 2018. "Differences of opinion and stock market volatility: evidence from a nonparametric causality-in-quantiles approach," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 42(2), pages 339-351, April.
    31. Mehmet Balcilar & Rangan Gupta & Clement Kyei, 2018. "Predicting Stock Returns And Volatility With Investor Sentiment Indices: A Reconsideration Using A Nonparametric Causality†In†Quantiles Test," Bulletin of Economic Research, Wiley Blackwell, vol. 70(1), pages 74-87, January.
    32. 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.
    33. Deven Bathia & Don Bredin & Dirk Nitzsche, 2016. "International Sentiment Spillovers in Equity Returns," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 21(4), pages 332-359, October.
    34. Guofu Zhou, 2018. "Measuring Investor Sentiment," Annual Review of Financial Economics, Annual Reviews, vol. 10(1), pages 239-259, November.
    35. Stelios Bekiros & Rangan Gupta & Clement Kyei, 2016. "A non-linear approach for predicting stock returns and volatility with the use of investor sentiment indices," Applied Economics, Taylor & Francis Journals, vol. 48(31), pages 2895-2898, July.
    36. Diego Escobari & Mohammad Jafarinejad, 2019. "Investors’ Uncertainty and Stock Market Risk," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 20(3), pages 304-315, July.
    37. 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.
    38. Naeem, Muhammad Abubakr & Farid, Saqib & Faruk, Balli & Shahzad, Syed Jawad Hussain, 2020. "Can happiness predict future volatility in stock markets?," Research in International Business and Finance, Elsevier, vol. 54(C).
    39. Rangan Gupta & Jacobus Nel & Christian Pierdzioch, 2023. "Investor Confidence and Forecastability of US Stock Market Realized Volatility: Evidence from Machine Learning," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 24(1), pages 111-122, January.
    40. Ole E. Barndorff-Nielsen, 2004. "Power and Bipower Variation with Stochastic Volatility and Jumps," Journal of Financial Econometrics, Oxford University Press, vol. 2(1), pages 1-37.
    41. Giot, Pierre & Laurent, Sébastien & Petitjean, Mikael, 2010. "Trading activity, realized volatility and jumps," Journal of Empirical Finance, Elsevier, vol. 17(1), pages 168-175, January.
    42. Salisu, Afees A. & Demirer, Riza & Gupta, Rangan, 2022. "Financial turbulence, systemic risk and the predictability of stock market volatility," Global Finance Journal, Elsevier, vol. 52(C).
    43. 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.
    44. Joshua D. Coval & Tobias J. Moskowitz, 1999. "Home Bias at Home: Local Equity Preference in Domestic Portfolios," Journal of Finance, American Finance Association, vol. 54(6), pages 2045-2073, December.
    45. Joshua D. Coval & Tobias J. Moskowitz, 2001. "The Geography of Investment: Informed Trading and Asset Prices," Journal of Political Economy, University of Chicago Press, vol. 109(4), pages 811-841, August.
    46. Li, Yue & W. Goodell, John & Shen, Dehua, 2021. "Does happiness forecast implied volatility? Evidence from nonparametric wave-based Granger causality testing," The Quarterly Review of Economics and Finance, Elsevier, vol. 81(C), pages 113-122.
    47. Christo Pirinsky & Qinghai Wang, 2006. "Does Corporate Headquarters Location Matter for Stock Returns?," Journal of Finance, American Finance Association, vol. 61(4), pages 1991-2015, August.
    48. Gong, Xue & Zhang, Weiguo & Wang, Junbo & Wang, Chao, 2022. "Investor sentiment and stock volatility: New evidence," International Review of Financial Analysis, Elsevier, vol. 80(C).
    49. 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.
    50. Haritha P H & Abdul Rishad, 2020. "An empirical examination of investor sentiment and stock market volatility: evidence from India," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-15, December.
    51. repec:spo:wpmain:info:hdl:2441/75koqefued8i7pihbrl9u84p4u is not listed on IDEAS
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    More about this item

    Keywords

    State-level stock markets; State-level investor sentiment; Business applications; Realized volatility; Forecasting;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

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