IDEAS home Printed from https://ideas.repec.org/p/pre/wpaper/202247.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: http://www.up.ac.za/media/shared/61/WP/wp_2022_47.zp225794.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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. 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.
    4. Ole E. Barndorff-Nielsen & Neil Shephard, 2006. "Econometrics of Testing for Jumps in Financial Economics Using Bipower Variation," The Journal of Financial Econometrics, Society for Financial Econometrics, vol. 4(1), pages 1-30.
    5. 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.
    6. 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).
    7. Zhi Da & Joseph Engelberg & Pengjie Gao, 2015. "Editor's Choice The Sum of All FEARS Investor Sentiment and Asset Prices," Review of Financial Studies, Society for Financial Studies, vol. 28(1), pages 1-32.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," The Journal of Financial Econometrics, Society for Financial Econometrics, vol. 7(2), pages 174-196, Spring.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. Michael McAleer & Marcelo Medeiros, 2008. "Realized Volatility: A Review," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 10-45.
    18. 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.
    19. repec:hal:spmain:info:hdl:2441/75koqefued8i7pihbrl9u84p4u is not listed on IDEAS
    20. 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.
    21. 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.
    22. 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.
    23. Guofu Zhou, 2018. "Measuring Investor Sentiment," Annual Review of Financial Economics, Annual Reviews, vol. 10(1), pages 239-259, November.
    24. 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.
    25. 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.
    26. Rangan Gupta & Jacobus Nel & Christian Pierdzioch, 2021. "Investor Confidence and Forecastability of US Stock Market Realized Volatility : Evidence from Machine Learning," Working Papers 202118, University of Pretoria, Department of Economics.
    27. Ole E. Barndorff-Nielsen, 2004. "Power and Bipower Variation with Stochastic Volatility and Jumps," The Journal of Financial Econometrics, Society for Financial Econometrics, vol. 2(1), pages 1-37.
    28. 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.
    29. 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).
    30. 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.
    31. 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.
    32. 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.
    33. 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.
    34. 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.
    35. 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).
    36. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2022. "Climate Risks and State-Level Stock-Market Realized Volatility," Working Papers 202246, University of Pretoria, Department of Economics.
    2. Rangan Gupta & Jacobus Nel & Christian Pierdzioch, 2021. "Investor Confidence and Forecastability of US Stock Market Realized Volatility : Evidence from Machine Learning," Working Papers 202118, University of Pretoria, Department of Economics.
    3. Matteo Bonato & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2020. "Investor Happiness and Predictability of the Realized Volatility of Oil Price," Sustainability, MDPI, vol. 12(10), pages 1-11, May.
    4. Bonato, Matteo & Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2021. "A note on investor happiness and the predictability of realized volatility of gold," Finance Research Letters, Elsevier, vol. 39(C).
    5. Yang, Cai & Gong, Xu & Zhang, Hongwei, 2019. "Volatility forecasting of crude oil futures: The role of investor sentiment and leverage effect," Resources Policy, Elsevier, vol. 61(C), pages 548-563.
    6. Riza Demirer & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2022. "Risk aversion and the predictability of crude oil market volatility: A forecasting experiment with random forests," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 73(8), pages 1755-1767, August.
    7. Elie Bouri & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2021. "Forecasting Realized Volatility of Bitcoin: The Role of the Trade War," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 29-53, January.
    8. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020. "Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss," Journal of International Money and Finance, Elsevier, vol. 104(C).
    9. Demirer, Riza & Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2019. "Time-varying risk aversion and realized gold volatility," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    10. Bonato, Matteo & Çepni, Oğuzhan & Gupta, Rangan & Pierdzioch, Christian, 2021. "Do oil-price shocks predict the realized variance of U.S. REITs?," Energy Economics, Elsevier, vol. 104(C).
    11. Balcilar, Mehmet & Bonato, Matteo & Demirer, Riza & Gupta, Rangan, 2017. "The effect of investor sentiment on gold market return dynamics: Evidence from a nonparametric causality-in-quantiles approach," Resources Policy, Elsevier, vol. 51(C), pages 77-84.
    12. Riza Demirer & Rangan Gupta & Christian Pierdzioch, 2020. "Forecasting Realized Stock-Market Volatility: Do Industry Returns have Predictive Value?," Working Papers 2020107, University of Pretoria, Department of Economics.
    13. Luo, Jiawen & Demirer, Riza & Gupta, Rangan & Ji, Qiang, 2022. "Forecasting oil and gold volatilities with sentiment indicators under structural breaks," Energy Economics, Elsevier, vol. 105(C).
    14. Li, Zhao-Chen & Xie, Chi & Zeng, Zhi-Jian & Wang, Gang-Jin & Zhang, Ting, 2023. "Forecasting global stock market volatilities in an uncertain world," International Review of Financial Analysis, Elsevier, vol. 85(C).
    15. 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).
    16. Christos Floros & Konstantinos Gkillas & Christoforos Konstantatos & Athanasios Tsagkanos, 2020. "Realized Measures to Explain Volatility Changes over Time," JRFM, MDPI, vol. 13(6), pages 1-19, June.
    17. Renee van Eyden & Rangan Gupta & Joshua Nielsen & Elie Bouri, 2022. "Investor Sentiment and Multi-Scale Positive and Negative Stock Market Bubbles in a Panel of G7 Countries," Working Papers 202256, University of Pretoria, Department of Economics.
    18. Bonato, Matteo & Gupta, Rangan & Lau, Chi Keung Marco & Wang, Shixuan, 2020. "Moments-based spillovers across gold and oil markets," Energy Economics, Elsevier, vol. 89(C).
    19. Dimitrios P. Louzis & Spyros Xanthopoulos-Sisinis & Apostolos P. Refenes, 2012. "Stock index realized volatility forecasting in the presence of heterogeneous leverage effects and long range dependence in the volatility of realized volatility," Applied Economics, Taylor & Francis Journals, vol. 44(27), pages 3533-3550, September.
    20. Lu, Xinjie & Ma, Feng & Wang, Jianqiong & Dong, Dayong, 2022. "Singlehanded or joint race? Stock market volatility prediction," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 734-754.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pre:wpaper:202247. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/decupza.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Rangan Gupta (email available below). General contact details of provider: https://edirc.repec.org/data/decupza.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.