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Predicting financial volatility: High‐frequency time‐series forecasts vis‐à‐vis implied volatility

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  • Martin Martens
  • Jason Zein

Abstract

Recent evidence suggests option implied volatilities provide better forecasts of financial volatility than time‐series models based on historical daily returns. In this study both the measurement and the forecasting of financial volatility is improved using high‐frequency data and long memory modeling, the latest proposed method to model volatility. This is the first study to extract results for three separate asset classes, equity, foreign exchange, and commodities. The results for the S&P 500, YEN/USD, and Light, Sweet Crude Oil provide a robust indication that volatility forecasts based on historical intraday returns do provide good volatility forecasts that can compete with and even outperform implied volatility. © 2004 Wiley Periodicals, Inc. Jrl Fut Mark 24:1005–1028, 2004

Suggested Citation

  • Martin Martens & Jason Zein, 2004. "Predicting financial volatility: High‐frequency time‐series forecasts vis‐à‐vis implied volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 24(11), pages 1005-1028, November.
  • Handle: RePEc:wly:jfutmk:v:24:y:2004:i:11:p:1005-1028
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    Cited by:

    1. Bollerslev, Tim & Kretschmer, Uta & Pigorsch, Christian & Tauchen, George, 2009. "A discrete-time model for daily S & P500 returns and realized variations: Jumps and leverage effects," Journal of Econometrics, Elsevier, vol. 150(2), pages 151-166, June.
    2. repec:eee:phsmap:v:493:y:2018:i:c:p:148-154 is not listed on IDEAS
    3. Duong T Le, 2015. "Ex-ante Determinants of Volatility in the Crude Oil Market," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 6(1), pages 1-13, January.
    4. Luo, Xingguo & Ye, Zinan, 2015. "Predicting volatility of the Shanghai silver futures market: What is the role of the U.S. options market?," Finance Research Letters, Elsevier, vol. 15(C), pages 68-77.
    5. Sévi, Benoît, 2014. "Forecasting the volatility of crude oil futures using intraday data," European Journal of Operational Research, Elsevier, vol. 235(3), pages 643-659.
    6. de Truchis, Gilles & Keddad, Benjamin, 2016. "On the risk comovements between the crude oil market and U.S. dollar exchange rates," Economic Modelling, Elsevier, vol. 52(PA), pages 206-215.
    7. Birkelund, Ole Henrik & Haugom, Erik & Molnár, Peter & Opdal, Martin & Westgaard, Sjur, 2015. "A comparison of implied and realized volatility in the Nordic power forward market," Energy Economics, Elsevier, vol. 48(C), pages 288-294.
    8. Mario Domingues de Paula Simões & Marcelo Cabus Klotzle & Antonio Carlos Figueiredo Pinto & Leonardo Lima Gomes, 2016. "Electricity prices forecast analysis using the extreme value theory," International Journal of Financial Markets and Derivatives, Inderscience Enterprises Ltd, vol. 5(1), pages 1-22.
    9. Wing Hong Chan & Ranjini Jha & Madhu Kalimipalli, 2009. "The Economic Value Of Using Realized Volatility In Forecasting Future Implied Volatility," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 32(3), pages 231-259.
    10. repec:eee:jbfina:v:84:y:2017:i:c:p:53-67 is not listed on IDEAS
    11. Shackleton, Mark B. & Taylor, Stephen J. & Yu, Peng, 2010. "A multi-horizon comparison of density forecasts for the S&P 500 using index returns and option prices," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2678-2693, November.
    12. Martens, Martin & van Dijk, Dick & de Pooter, Michiel, 2009. "Forecasting S&P 500 volatility: Long memory, level shifts, leverage effects, day-of-the-week seasonality, and macroeconomic announcements," International Journal of Forecasting, Elsevier, vol. 25(2), pages 282-303.
    13. Chatrath, Arjun & Miao, Hong & Ramchander, Sanjay & Wang, Tianyang, 2016. "An examination of the flow characteristics of crude oil: Evidence from risk-neutral moments," Energy Economics, Elsevier, vol. 54(C), pages 213-223.
    14. repec:spr:empeco:v:55:y:2018:i:2:d:10.1007_s00181-017-1294-6 is not listed on IDEAS
    15. Rothonis, Stephanie & Tran, Duy & Wu, Eliza, 2016. "Does national culture affect the intensity of volatility linkages in international equity markets?," Research in International Business and Finance, Elsevier, vol. 36(C), pages 85-95.
    16. repec:eee:ecofin:v:44:y:2018:i:c:p:92-108 is not listed on IDEAS
    17. Chong, Terence Tai Leung & Lu, Chenxi & Chan, Wing H., 2016. "Long Range Dependence and Structural Breaks in the Gold Markets," MPRA Paper 80553, University Library of Munich, Germany.
    18. repec:eee:intfor:v:33:y:2017:i:4:p:848-863 is not listed on IDEAS
    19. Luo, Xingguo & Qin, Shihua & Ye, Zinan, 2016. "The information content of implied volatility and jumps in forecasting volatility: Evidence from the Shanghai gold futures market," Finance Research Letters, Elsevier, vol. 19(C), pages 105-111.
    20. Apostolos Kourtis & Raphael N. Markellos & Lazaros Symeonidis, 2016. "An International Comparison of Implied, Realized, and GARCH Volatility Forecasts," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(12), pages 1164-1193, December.
    21. Ying Jiang & Shamim Ahmed & Xiaoquan Liu, 2017. "Volatility forecasting in the Chinese commodity futures market with intraday data," Review of Quantitative Finance and Accounting, Springer, vol. 48(4), pages 1123-1173, May.
    22. Guimaraes, Jonathan S. & Cruz, Jose Cesar, 2017. "Future volatility forecast in agricultural commodity markets," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258480, Agricultural and Applied Economics Association.
    23. Kasman, Adnan & Kasman, Saadet & Torun, Erdost, 2009. "Dual long memory property in returns and volatility: Evidence from the CEE countries' stock markets," Emerging Markets Review, Elsevier, vol. 10(2), pages 122-139, June.
    24. Haugom, Erik & Westgaard, Sjur & Solibakke, Per Bjarte & Lien, Gudbrand, 2011. "Realized volatility and the influence of market measures on predictability: Analysis of Nord Pool forward electricity data," Energy Economics, Elsevier, vol. 33(6), pages 1206-1215.

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