IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v55y2023ipbs1544612323003641.html
   My bibliography  Save this article

Do extreme range estimators improve realized volatility forecasts? Evidence from G7 Stock Markets

Author

Listed:
  • Korkusuz, Burak
  • Kambouroudis, Dimos
  • McMillan, David G.

Abstract

This paper investigates whether range estimators contain important information in forecasting future realized volatility. We use widely applied range-based estimators: Parkinson, Garman-Klass, Roger-Satchell, and Yang-Zhang within a HAR-RV-X framework. Overnight volatility and close-to-close volatility estimators are also included, and the forecasting exercise is applied to G7 stock markets using a rolling window. Using QLIKE, HMSE and MCS forecast criteria, several noteworthy points are reported. The overall findings suggest that while no single model dominates, overnight return volatility achieves the most consistent performance. For example, HAR-RV model forecasts for CAC and DAX indices are improved only by overnight volatility, with some evidence also for SPX. For other indices, forecasts are improved by Parkinson and/or Garman-Klass volatility estimators. Of note, simpler range estimators outperform more complex range estimators. The findings could be important for investors in managing portfolio risk.

Suggested Citation

  • Korkusuz, Burak & Kambouroudis, Dimos & McMillan, David G., 2023. "Do extreme range estimators improve realized volatility forecasts? Evidence from G7 Stock Markets," Finance Research Letters, Elsevier, vol. 55(PB).
    • Handle: RePEc:eee:finlet:v:55:y:2023:i:pb:s1544612323003641
    DOI: 10.1016/j.frl.2023.103992
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612323003641
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2023.103992?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. I‐Ming Jiang & Jui‐Cheng Hung & Chuan‐San Wang, 2014. "Volatility Forecasts: Do Volatility Estimators and Evaluation Methods Matter?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 34(11), pages 1077-1094, November.
    2. Garman, Mark B & Klass, Michael J, 1980. "On the Estimation of Security Price Volatilities from Historical Data," The Journal of Business, University of Chicago Press, vol. 53(1), pages 67-78, January.
    3. Martens, Martin & van Dijk, Dick, 2007. "Measuring volatility with the realized range," Journal of Econometrics, Elsevier, vol. 138(1), pages 181-207, May.
    4. Fulvio Corsi & Roberto Renò, 2012. "Discrete-Time Volatility Forecasting With Persistent Leverage Effect and the Link With Continuous-Time Volatility Modeling," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 368-380, January.
    5. Bollerslev, Tim & Ghysels, Eric, 1996. "Periodic Autoregressive Conditional Heteroscedasticity," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(2), pages 139-151, April.
    6. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil price realized volatility using information channels from other asset classes," Journal of International Money and Finance, Elsevier, vol. 76(C), pages 28-49.
    7. Turan G. Bali & David Weinbaum, 2005. "A comparative study of alternative extreme‐value volatility estimators," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 25(9), pages 873-892, September.
    8. Yarovaya, Larisa & Brzeszczyński, Janusz & Lau, Chi Keung Marco, 2016. "Volatility spillovers across stock index futures in Asian markets: Evidence from range volatility estimators," Finance Research Letters, Elsevier, vol. 17(C), pages 158-166.
    9. Liu, Lily Y. & Patton, Andrew J. & Sheppard, Kevin, 2015. "Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes," Journal of Econometrics, Elsevier, vol. 187(1), pages 293-311.
    10. Liu, Jing & Ma, Feng & Zhang, Yaojie, 2019. "Forecasting the Chinese stock volatility across global stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 466-477.
    11. Neda Todorova & Sven Husmann, 2012. "A comparative study of range‐based stock return volatility estimators for the German market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 32(6), pages 560-586, June.
    12. 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.
    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. Eugenie Hol & Siem Jan Koopman, 2002. "Stock Index Volatility Forecasting with High Frequency Data," Tinbergen Institute Discussion Papers 02-068/4, Tinbergen Institute.
    15. Peter Reinhard Hansen & Asger Lunde & James M. Nason, 2003. "Choosing the Best Volatility Models: The Model Confidence Set Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 839-861, December.
    16. Parkinson, Michael, 1980. "The Extreme Value Method for Estimating the Variance of the Rate of Return," The Journal of Business, University of Chicago Press, vol. 53(1), pages 61-65, January.
    17. Wang, Hui, 2019. "VIX and volatility forecasting: A new insight," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 533(C).
    18. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
    19. Brooks, C. & Clare, A. D. & Persand, G., 2000. "A word of caution on calculating market-based minimum capital risk requirements," Journal of Banking & Finance, Elsevier, vol. 24(10), pages 1557-1574, October.
    20. Karthik Raju & Saravanan Rangaswamy, 2017. "Forecasting volatility in the Indian equity market using return and range-based models," Applied Economics, Taylor & Francis Journals, vol. 49(49), pages 5027-5039, October.
    21. Peng, Huan & Chen, Ruoxun & Mei, Dexiang & Diao, Xiaohua, 2018. "Forecasting the realized volatility of the Chinese stock market: Do the G7 stock markets help?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 78-85.
    22. Yang, Dennis & Zhang, Qiang, 2000. "Drift-Independent Volatility Estimation Based on High, Low, Open, and Close Prices," The Journal of Business, University of Chicago Press, vol. 73(3), pages 477-491, July.
    23. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    24. Chou, Ray Yeutien, 2005. "Forecasting Financial Volatilities with Extreme Values: The Conditional Autoregressive Range (CARR) Model," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 561-582, June.
    25. Zhang, Yaojie & Ma, Feng & Liao, Yin, 2020. "Forecasting global equity market volatilities," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1454-1475.
    26. Christensen, Kim & Podolskij, Mark, 2007. "Realized range-based estimation of integrated variance," Journal of Econometrics, Elsevier, vol. 141(2), pages 323-349, December.
    27. Dimos S. Kambouroudis & David G. McMillan & Katerina Tsakou, 2021. "Forecasting realized volatility: The role of implied volatility, leverage effect, overnight returns, and volatility of realized volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(10), pages 1618-1639, October.
    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. Lyócsa, Štefan & Todorova, Neda & Výrost, Tomáš, 2021. "Predicting risk in energy markets: Low-frequency data still matter," Applied Energy, Elsevier, vol. 282(PA).
    2. Arnerić, Josip & Matković, Mario & Sorić, Petar, 2019. "Comparison of range-based volatility estimators against integrated volatility in European emerging markets," Finance Research Letters, Elsevier, vol. 28(C), pages 118-124.
    3. Tan, Shay-Kee & Ng, Kok-Haur & Chan, Jennifer So-Kuen & Mohamed, Ibrahim, 2019. "Quantile range-based volatility measure for modelling and forecasting volatility using high frequency data," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 537-551.
    4. Bertrand B. Maillet & Jean-Philippe R. M�decin, 2010. "Extreme Volatilities, Financial Crises and L-moment Estimations of Tail-indexes," Working Papers 2010_10, Department of Economics, University of Venice "Ca' Foscari".
    5. Lyócsa, Štefan & Molnár, Peter & Výrost, Tomáš, 2021. "Stock market volatility forecasting: Do we need high-frequency data?," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1092-1110.
    6. I‐Ming Jiang & Jui‐Cheng Hung & Chuan‐San Wang, 2014. "Volatility Forecasts: Do Volatility Estimators and Evaluation Methods Matter?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 34(11), pages 1077-1094, November.
    7. Todorova, Neda & Souček, Michael, 2014. "The impact of trading volume, number of trades and overnight returns on forecasting the daily realized range," Economic Modelling, Elsevier, vol. 36(C), pages 332-340.
    8. Gong, Xu & Lin, Boqiang, 2018. "Structural changes and out-of-sample prediction of realized range-based variance in the stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 27-39.
    9. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    10. Clements, Adam & Preve, Daniel P.A., 2021. "A Practical Guide to harnessing the HAR volatility model," Journal of Banking & Finance, Elsevier, vol. 133(C).
    11. Neda Todorova, 2012. "Volatility estimators based on daily price ranges versus the realized range," Applied Financial Economics, Taylor & Francis Journals, vol. 22(3), pages 215-229, February.
    12. Shay Kee Tan & Kok Haur Ng & Jennifer So-Kuen Chan, 2022. "Predicting Returns, Volatilities and Correlations of Stock Indices Using Multivariate Conditional Autoregressive Range and Return Models," Mathematics, MDPI, vol. 11(1), pages 1-24, December.
    13. Yuta Kurose, 2021. "Stochastic volatility model with range-based correction and leverage," Papers 2110.00039, arXiv.org, revised Oct 2021.
    14. Robert Ślepaczuk & Grzegorz Zakrzewski, 2009. "High-Frequency and Model-Free Volatility Estimators," Working Papers 2009-13, Faculty of Economic Sciences, University of Warsaw.
    15. Richard Gerlach & Chao Wang, 2016. "Forecasting risk via realized GARCH, incorporating the realized range," Quantitative Finance, Taylor & Francis Journals, vol. 16(4), pages 501-511, April.
    16. Elena Ivona Dumitrescu & Georgiana-Denisa Banulescu, 2019. "Do High-frequency-based Measures Improve Conditional Covariance Forecasts?," Post-Print hal-03331122, HAL.
    17. Chao Liang & Yu Wei & Yaojie Zhang, 2020. "Is implied volatility more informative for forecasting realized volatility: An international perspective," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1253-1276, December.
    18. Wang, Jiqian & Lu, Xinjie & He, Feng & Ma, Feng, 2020. "Which popular predictor is more useful to forecast international stock markets during the coronavirus pandemic: VIX vs EPU?," International Review of Financial Analysis, Elsevier, vol. 72(C).
    19. Bhaumik, S. & Karanasos, M. & Kartsaklas, A., 2016. "The informative role of trading volume in an expanding spot and futures market," Journal of Multinational Financial Management, Elsevier, vol. 35(C), pages 24-40.
    20. Hiroyuki Kawakatsu, 2021. "Information in daily data volatility measurements," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 1642-1656, April.

    More about this item

    Keywords

    Volatility forecasting; Realized volatility; G7 stock markets; HAR-RV-X model; Rolling methods; MCS;
    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
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

    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:eee:finlet:v:55:y:2023:i:pb:s1544612323003641. See general information about how to correct material in RePEc.

    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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.