IDEAS home Printed from https://ideas.repec.org/a/spr/eurasi/v3y2013i2p200-216.html
   My bibliography  Save this article

Examination on the Relationship Between VHSI, HSI and Future Realized Volatility With Kalman Filter

Author

Listed:
  • Yanhui Chen
  • Kin Lai

Abstract

Hang Seng Index Volatility (VHSI) is a new barometer to research the variance of Hang Seng Index (HSI). This paper first explores how VHSI changes are influenced by HSI returns dynamically. The time-varying coefficients achieved by Kalman filter indicate a negative and asymmetric contemporaneous relationship between VHSI changes and HSI returns. More importantly, we find that this asymmetric effect is stronger in Hong Kong than that in US since the investors are more sensitive to negative returns. Second, this paper studies the relationship between VHSI and the future realized volatility of HSI, and predicts the future realized volatility of HSI with Kalman filter. The empirical findings suggest that VHSI is an unbiased and efficient estimate of the future realized volatility and includes information of the future realized volatility when employing monthly data. In addition, the predication performance of Kalman filter is better than linear regression model. Copyright Eurasia Business and Economics Society 2013

Suggested Citation

  • Yanhui Chen & Kin Lai, 2013. "Examination on the Relationship Between VHSI, HSI and Future Realized Volatility With Kalman Filter," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 3(2), pages 200-216, December.
    • Handle: RePEc:spr:eurasi:v:3:y:2013:i:2:p:200-216
    DOI: 10.14208/ebr.2013.03.02.005
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.14208/ebr.2013.03.02.005
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.14208/ebr.2013.03.02.005?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. Becker, Ralf & Clements, Adam E., 2008. "Are combination forecasts of S&P 500 volatility statistically superior?," International Journal of Forecasting, Elsevier, vol. 24(1), pages 122-133.
    2. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    3. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737, January.
    4. Charlotte Strunk Hansen, 2001. "The relation between implied and realised volatility in the Danish option and equity markets," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 41(3), pages 197-228, November.
    5. Canina, Linda & Figlewski, Stephen, 1993. "The Informational Content of Implied Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 6(3), pages 659-681.
    6. Guilkey, David K. & Schmidt, Peter, 1989. "Extended tabulations for Dickey-Fuller tests," Economics Letters, Elsevier, vol. 31(4), pages 355-357, December.
    7. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    8. 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.
    9. George J. Jiang & Yisong S. Tian, 2005. "The Model-Free Implied Volatility and Its Information Content," The Review of Financial Studies, Society for Financial Studies, vol. 18(4), pages 1305-1342.
    10. Mihaela Manoliu & Stathis Tompaidis, 2002. "Energy futures prices: term structure models with Kalman filter estimation," Applied Mathematical Finance, Taylor & Francis Journals, vol. 9(1), pages 21-43.
    11. Bedendo, Mascia & Hodges, Stewart D., 2009. "The dynamics of the volatility skew: A Kalman filter approach," Journal of Banking & Finance, Elsevier, vol. 33(6), pages 1156-1165, June.
    12. Christensen, B. J. & Prabhala, N. R., 1998. "The relation between implied and realized volatility," Journal of Financial Economics, Elsevier, vol. 50(2), pages 125-150, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Aneta Wlodarczyk & Iwona Otola, 2016. "Analysis of the Relationship between Market Volatility and Firms Volatility on the Polish Capital Market," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 16, pages 87-116.
    2. Fassas, Athanasios P. & Siriopoulos, Costas, 2021. "Implied volatility indices – A review," The Quarterly Review of Economics and Finance, Elsevier, vol. 79(C), pages 303-329.
    3. Seokchin Kim & Cheolho Park & Youngjun Yun, 2014. "Hedging with mini gold futures: evidence from Korea," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 4(2), pages 163-176, December.
    4. Yanhui Chen & Kin Lai & Jiangze Du, 2014. "Modeling and forecasting Hang Seng index volatility with day-of-week effect, spillover effect based on ARIMA and HAR," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 4(2), pages 113-132, December.
    5. Yam Wing Siu, 2020. "Impact of Expected Shortfall Approach on Capital Requirement Under Basel," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 22(04), pages 1-34, January.
    6. Yam Wing Siu, 2018. "Volatility Forecast by Volatility Index and Its Use as a Risk Management Tool Under a Value-at-Risk Approach," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 21(02), pages 1-48, June.
    7. Song, Jianhua & Zhang, Zhepei & So, Mike K.P., 2021. "On the predictive power of network statistics for financial risk indicators," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    8. Gozgor, Giray & Lau, Chi Keung Marco & Bilgin, Mehmet Huseyin, 2016. "Commodity markets volatility transmission: Roles of risk perceptions and uncertainty in financial markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 44(C), pages 35-45.
    9. A. Can Inci, 2018. "Financials sector intraday volatility characteristics in the emerging Turkish economy," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 8(2), pages 215-229, August.
    10. Thorsten Lehnert, 2019. "Big moves of mutual funds," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 9(1), pages 1-27, March.
    11. Jacques Peeperkorn & Yudhvir Seetharam, 2016. "A learning-augmented approach to pricing risk in South Africa," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 6(1), pages 117-139, April.

    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. Yanhui Chen & Kin Keung Lai, 2013. "Examination on the Relationship Between VHSI, HSI and Future Realized Volatility With Kalman Filter," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 3(2), pages 200-216, December.
    2. Taylor, Stephen J. & Yadav, Pradeep K. & Zhang, Yuanyuan, 2009. "The information content of implied volatilities and model-free volatility expectations: Evidence from options written on individual stocks," CFR Working Papers 09-07, University of Cologne, Centre for Financial Research (CFR).
    3. Tsiaras, Leonidas, 2009. "The Forecast Performance of Competing Implied Volatility Measures: The Case of Individual Stocks," Finance Research Group Working Papers F-2009-02, University of Aarhus, Aarhus School of Business, Department of Business Studies.
    4. Xiaoxi Liu & Jinming Xie, 2023. "Forecasting swap rate volatility with information from swaptions," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(4), pages 455-479, April.
    5. Chun, Dohyun & Cho, Hoon & Ryu, Doojin, 2019. "Forecasting the KOSPI200 spot volatility using various volatility measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 156-166.
    6. Florian Ielpo & Benoît Sévi, 2014. "Forecasting the density of oil futures," Working Papers 2014-601, Department of Research, Ipag Business School.
    7. Chun, Dohyun & Cho, Hoon & Ryu, Doojin, 2023. "Discovering the drivers of stock market volatility in a data-rich world," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).
    8. Taylor, Stephen J. & Yadav, Pradeep K. & Zhang, Yuanyuan, 2010. "The information content of implied volatilities and model-free volatility expectations: Evidence from options written on individual stocks," Journal of Banking & Finance, Elsevier, vol. 34(4), pages 871-881, April.
    9. Christoffersen, Peter & Jacobs, Kris & Chang, Bo Young, 2013. "Forecasting with Option-Implied Information," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 581-656, Elsevier.
    10. Leonidas S. Rompolis & Elias Tzavalis, 2010. "Risk Premium Effects On Implied Volatility Regressions," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 33(2), pages 125-151, June.
    11. Prokopczuk, Marcel & Wese Simen, Chardin, 2014. "The importance of the volatility risk premium for volatility forecasting," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 303-320.
    12. Choudhary, Sangita & Jain, Anshul & Biswal, Pratap Chandra, 2024. "Dynamic linkages among bitcoin, equity, gold and oil: An implied volatility perspective," Finance Research Letters, Elsevier, vol. 62(PB).
    13. Tanuj Nandan & Puja Agrawal, 2016. "Pricing Efficiency in CNX Nifty Index Options Using the Black–Scholes Model: A Comparative Study of Alternate Volatility Measures," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 10(2), pages 281-304, May.
    14. Yu, Wayne W. & Lui, Evans C.K. & Wang, Jacqueline W., 2010. "The predictive power of the implied volatility of options traded OTC and on exchanges," Journal of Banking & Finance, Elsevier, vol. 34(1), pages 1-11, January.
    15. Plíhal, Tomáš & Lyócsa, Štefan, 2021. "Modeling realized volatility of the EUR/USD exchange rate: Does implied volatility really matter?," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 811-829.
    16. Murad Samsudin, Najmi Ismail & Mohamad, Azhar & Sifat, Imtiaz Mohammad, 2021. "Implied volatility of structured warrants: Emerging market evidence," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 464-479.
    17. Fassas, Athanasios P. & Siriopoulos, Costas, 2021. "Implied volatility indices – A review," The Quarterly Review of Economics and Finance, Elsevier, vol. 79(C), pages 303-329.
    18. Mittnik, Stefan & Robinzonov, Nikolay & Spindler, Martin, 2015. "Stock market volatility: Identifying major drivers and the nature of their impact," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 1-14.
    19. Bianconi, Marcelo & MacLachlan, Scott & Sammon, Marco, 2015. "Implied volatility and the risk-free rate of return in options markets," The North American Journal of Economics and Finance, Elsevier, vol. 31(C), pages 1-26.
    20. Martha Misas & Enrique López, 1999. "El producto potencial en Colombia: una estimación bajo var estructural," Coyuntura Económica, Fedesarrollo, September.

    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:spr:eurasi:v:3:y:2013:i:2:p:200-216. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.