IDEAS home Printed from https://ideas.repec.org/a/eee/empfin/v38y2016ipap139-156.html
   My bibliography  Save this article

Optimal conditional hedge ratio: A simple shrinkage estimation approach

Author

Listed:
  • Kim, Myeong Jun
  • Park, Sung Y.

Abstract

A number of recent studies adopt bivariate generalized autoregressive conditional heteroskedasticity (BGARCH) models to estimate the optimal conditional hedge ratio. Since the optimal hedge ratio can be expressed by the ratio of variance of futures returns to the covariance of spot and futures, the BGARCH model is quite useful to estimate the conditional hedge ratio. However, it is well known that high variability of an estimated conditional hedge ratio results in lower hedge effectiveness. In this study, we consider a simple shrinkage method to deal with this inverse relationship between volatility of the conditional hedge ratio and hedging effectiveness. Our main idea is that the shrinkage version of the optimal hedge ratio can be obtained from a convex combination of unconditional sample covariance matrix and conditional covariance matrices of a conventional BGARCH model. Our empirical results show the usefulness of our proposed model.

Suggested Citation

  • Kim, Myeong Jun & Park, Sung Y., 2016. "Optimal conditional hedge ratio: A simple shrinkage estimation approach," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 139-156.
  • Handle: RePEc:eee:empfin:v:38:y:2016:i:pa:p:139-156
    DOI: 10.1016/j.jempfin.2016.06.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0927539816300597
    Download Restriction: Full text for ScienceDirect subscribers only

    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. Ledoit, Olivier & Wolf, Michael, 2004. "A well-conditioned estimator for large-dimensional covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 88(2), pages 365-411, February.
    2. Sung Yong Park & Sang Young Jei, 2010. "Estimation and hedging effectiveness of time‐varying hedge ratio: Flexible bivariate garch approaches," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 30(1), pages 71-99, January.
    3. Thomas Conlon & John Cotter, 2012. "An empirical analysis of dynamic multiscale hedging using wavelet decomposition," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 32(3), pages 272-299, March.
    4. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    5. Robert J. Myers & Stanley R. Thompson, 1989. "Generalized Optimal Hedge Ratio Estimation," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 71(4), pages 858-868.
    6. Cecchetti, Stephen G & Cumby, Robert E & Figlewski, Stephen, 1988. "Estimation of the Optimal Futures Hedge," The Review of Economics and Statistics, MIT Press, vol. 70(4), pages 623-630, November.
    7. Donald Lien & Y. K. Tse & Albert Tsui, 2002. "Evaluating the hedging performance of the constant-correlation GARCH model," Applied Financial Economics, Taylor & Francis Journals, vol. 12(11), pages 791-798.
    8. Donald Lien, 2010. "Effects of omitting information variables on optimal hedge ratio estimation: A note," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 30(8), pages 795-800, August.
    9. Hsiang‐Tai Lee & Jonathan K. Yoder & Ron C. Mittelhammer & Jill J. McCluskey, 2006. "A random coefficient autoregressive Markov regime switching model for dynamic futures hedging," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 26(2), pages 103-129, February.
    10. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    11. Lorenzo Cappiello & Robert F. Engle & Kevin Sheppard, 2006. "Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 4(4), pages 537-572.
    12. Beaulieu, Marie-Claude, 1998. "Time to maturity in the basis of stock market indices: Evidence from the S&P 500 and the MMI," Journal of Empirical Finance, Elsevier, vol. 5(3), pages 177-195, September.
    13. Qingfu Liu & Michael T. Chng & Dongxia Xu, 2014. "Hedging Industrial Metals With Stochastic Volatility Models," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 34(8), pages 704-730, August.
    14. Robert A. Collins, 2000. "The risk management effectiveness of multivariate hedging models in the U.S. soy complex," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 20(2), pages 189-204, February.
    15. Richard T. Baillie & Young‐Wook Han & Robert J. Myers & Jeongseok Song, 2007. "Long memory models for daily and high frequency commodity futures returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 27(7), pages 643-668, July.
    16. Chris Brooks & Alešs Černý & Joëlle Miffre, 2012. "Optimal hedging with higher moments," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 32(10), pages 909-944, October.
    17. Chris Brooks & Olan T. Henry & Gita Persand, 2002. "The Effect of Asymmetries on Optimal Hedge Ratios," The Journal of Business, University of Chicago Press, vol. 75(2), pages 333-352, April.
    18. Andrew Patton & Dimitris Politis & Halbert White, 2009. "Correction to “Automatic Block-Length Selection for the Dependent Bootstrap” by D. Politis and H. White," Econometric Reviews, Taylor & Francis Journals, vol. 28(4), pages 372-375.
    19. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. " On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    20. Chao‐Chun Chen & Wen‐Jen Tsay, 2011. "A Markov regime‐switching ARMA approach for hedging stock indices," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 31(2), pages 165-191, February.
    21. Lien, Donald & Yang, Li, 2008. "Asymmetric effect of basis on dynamic futures hedging: Empirical evidence from commodity markets," Journal of Banking & Finance, Elsevier, vol. 32(2), pages 187-198, February.
    22. Duong, Huu Nhan & Kalev, Petko S., 2008. "The Samuelson hypothesis in futures markets: An analysis using intraday data," Journal of Banking & Finance, Elsevier, vol. 32(4), pages 489-500, April.
    23. Donald Lien, 2010. "A note on the relationship between the variability of the hedge ratio and hedging performance," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 30(11), pages 1100-1104, November.
    24. Chih‐Chiang Hsu & Chih‐Ping Tseng & Yaw‐Huei Wang, 2008. "Dynamic hedging with futures: A copula‐based GARCH model," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 28(11), pages 1095-1116, November.
    25. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
    26. Richard T. Baillie & Young-Wook Han & Robert J. Myers & Jeongseok Song, 2007. "Long Memory and FIGARCH Models for Daily and High Frequency Commodity Prices," Working Papers 594, Queen Mary University of London, School of Economics and Finance.
    27. Donald Lien & Li Yang, 2006. "Spot‐futures spread, time‐varying correlation, and hedging with currency futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 26(10), pages 1019-1038, October.
    28. Anil K. Bera & Philip Garcia & Jae-Sun Roh, 1997. "Estimation of Time-Varying Hedge Ratios for Corn and Soybeans: BGARCH and Random Coefficient Approaches," Finance 9712007, University Library of Munich, Germany.
    29. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
    30. Dimitris Politis & Halbert White, 2004. "Automatic Block-Length Selection for the Dependent Bootstrap," Econometric Reviews, Taylor & Francis Journals, vol. 23(1), pages 53-70.
    31. Yen‐Ju Chen & Jin‐Chuan Duan & Mao‐Wei Hung, 1999. "Volatility and maturity effects in the Nikkei index futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 19(8), pages 895-909, December.
    32. Baillie, Richard T & Myers, Robert J, 1991. "Bivariate GARCH Estimation of the Optimal Commodity Futures Hedge," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(2), pages 109-124, April-Jun.
    33. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    34. Tina M. Galloway & Robert W. Kolb, 1996. "Futures prices and the maturity effect," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 16(7), pages 809-828, October.
    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. Hou, Yang & Holmes, Mark, 2017. "On the effects of static and autoregressive conditional higher order moments on dynamic optimal hedging," MPRA Paper 82000, University Library of Munich, Germany.

    More about this item

    Keywords

    Conditional hedge ratio; Shrinkage method; Hedge performance;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

    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:empfin:v:38:y:2016:i:pa:p:139-156. 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: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/locate/jempfin .

    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 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.

    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.