IDEAS home Printed from https://ideas.repec.org/a/taf/apeclt/v14y2007i7p503-509.html
   My bibliography  Save this article

On the application of the dynamic conditional correlation model in estimating optimal time-varying hedge ratios

Author

Listed:
  • Yuan-Hung Hsu Ku
  • Ho-Chyuan Chen
  • Kuang-Hua Chen

Abstract

This article applies the dynamic conditional correlation model of Engle (2002) with error correction terms in order to investigate the optimal hedge ratios of British and Japanese currency futures markets. For a comparison, the estimates of three other models -- traditional generalized autoregressive conditional heteroskedasticity (GARCH), ordinary least square (OLS) and error correction model (ECM) -- are also reported. Results show that the dynamic conditional correlation model yields the best hedging performance in both futures markets. Nonetheless, the traditional multivariate GARCH model (which exhibits constant conditional correlations and time-varying hedge ratios) performs the worst hedging effectiveness, even inferior to the time-invariant hedging methods (OLS and ECM). The inclusion of dynamic conditional correlations in the GARCH model can therefore better capture the frequent fluctuations in futures markets.

Suggested Citation

  • Yuan-Hung Hsu Ku & Ho-Chyuan Chen & Kuang-Hua Chen, 2007. "On the application of the dynamic conditional correlation model in estimating optimal time-varying hedge ratios," Applied Economics Letters, Taylor & Francis Journals, vol. 14(7), pages 503-509.
  • Handle: RePEc:taf:apeclt:v:14:y:2007:i:7:p:503-509 DOI: 10.1080/13504850500447331
    as

    Download full text from publisher

    File URL: http://www.informaworld.com/openurl?genre=article&doi=10.1080/13504850500447331&magic=repec&7C&7C8674ECAB8BB840C6AD35DC6213A474B5
    Download Restriction: Access to full text is restricted to subscribers.

    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. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    2. Charles Engel & Kenneth D. West, 2005. "Exchange Rates and Fundamentals," Journal of Political Economy, University of Chicago Press, vol. 113(3), pages 485-517, June.
    3. Stephen G. Cecchetti & Michael Ehrmann, 2002. "Does Inflation Targeting Increase Output Volatility?: An International Comparison of Policymakers' Preferences and Outcomes," Central Banking, Analysis, and Economic Policies Book Series,in: Norman Loayza & Klaus Schmidt-Hebbel & Norman Loayza (Series Editor) & Klaus Schmidt-Hebbel (Series (ed.), Monetary Policy: Rules and Transmission Mechanisms, edition 1, volume 4, chapter 9, pages 247-274 Central Bank of Chile.
    4. Ang, Andrew & Piazzesi, Monika & Wei, Min, 2006. "What does the yield curve tell us about GDP growth?," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 359-403.
    5. Hans Joachim Voth, 1998. "Inflationary expectations during Germany's great slump," Economics Working Papers 333, Department of Economics and Business, Universitat Pompeu Fabra.
    6. James H. Stock & Mark W. Watson, 1989. "New Indexes of Coincident and Leading Economic Indicators," NBER Chapters,in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409 National Bureau of Economic Research, Inc.
    7. Chen, Nai-Fu, 1991. " Financial Investment Opportunities and the Macroeconomy," Journal of Finance, American Finance Association, vol. 46(2), pages 529-554, June.
    8. Robert D. Laurent, 1988. "An interest rate-based indicator of monetary policy," Economic Perspectives, Federal Reserve Bank of Chicago, issue Jan, pages 3-14.
    9. Peel, David A. & Ioannidis, Christos, 2003. "Empirical evidence on the relationship between the term structure of interest rates and future real output changes when there are changes in policy regimes," Economics Letters, Elsevier, vol. 78(2), pages 147-152, February.
    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. Chang, Chia-Lin & McAleer, Michael & Tansuchat, Roengchai, 2011. "Crude oil hedging strategies using dynamic multivariate GARCH," Energy Economics, Elsevier, pages 912-923.
    2. Wang, Yudong & Wu, Chongfeng, 2012. "Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?," Energy Economics, Elsevier, vol. 34(6), pages 2167-2181.
    3. El Hedi Arouri, Mohamed & Jouini, Jamel & Nguyen, Duc Khuong, 2011. "Volatility spillovers between oil prices and stock sector returns: Implications for portfolio management," Journal of International Money and Finance, Elsevier, vol. 30(7), pages 1387-1405.
    4. Fabio Filipozzi & Kersti Harkmann, 2014. "Currency hedge – walking on the edge?," Bank of Estonia Working Papers wp2014-5, Bank of Estonia, revised 10 Oct 2014.
    5. López Cabrera, Brenda & Schulz, Franziska, 2016. "Volatility linkages between energy and agricultural commodity prices," Energy Economics, Elsevier, pages 190-203.
    6. El Hedi Arouri, Mohamed & Lahiani, Amine & Nguyen, Duc Khuong, 2015. "World gold prices and stock returns in China: Insights for hedging and diversification strategies," Economic Modelling, Elsevier, vol. 44(C), pages 273-282.
    7. Chia-Lin Chang & Michael McAleer, 2013. "Ranking Leading Econometrics Journals Using Citations Data from ISI and RePEc," Econometrics, MDPI, Open Access Journal, pages 1-19.
    8. Yudong Wang & Li Liu, 2016. "Crude oil and world stock markets: volatility spillovers, dynamic correlations, and hedging," Empirical Economics, Springer, pages 1481-1509.
    9. Zanotti, Giovanna & Gabbi, Giampaolo & Geranio, Manuela, 2010. "Hedging with futures: Efficacy of GARCH correlation models to European electricity markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 20(2), pages 135-148, April.
    10. Jebabli, Ikram & Arouri, Mohamed & Teulon, Frédéric, 2014. "On the effects of world stock market and oil price shocks on food prices: An empirical investigation based on TVP-VAR models with stochastic volatility," Energy Economics, Elsevier, pages 66-98.
    11. Khalifa, Ahmed A.A. & Hammoudeh, Shawkat & Otranto, Edoardo, 2014. "Extracting portfolio management strategies from volatility transmission models in regime-changing environments: Evidence from GCC and global markets," Economic Modelling, Elsevier, vol. 41(C), pages 365-374.
    12. Caporin, Massimiliano & Jimenez-Martin, Juan-Angel & Gonzalez-Serrano, Lydia, 2014. "Currency hedging strategies in strategic benchmarks and the global and Euro sovereign financial crises," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 31(C), pages 159-177.
    13. Асатуров К.Г. & Теплова Т.В., 2014. "Построение Коэффициентов Хеджирования Для Высоколиквидных Акций Российского Рынка На Основе Моделей Класса Garch," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 50(1), pages 37-54, январь.
    14. Chang, Chia-Lin & González-Serrano, Lydia & Jimenez-Martin, Juan-Angel, 2013. "Currency hedging strategies using dynamic multivariate GARCH," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 164-182.
    15. Chkili, Walid, 2016. "Dynamic correlations and hedging effectiveness between gold and stock markets: Evidence for BRICS countries," Research in International Business and Finance, Elsevier, pages 22-34.
    16. Basher, Syed Abul & Sadorsky, Perry, 2016. "Hedging emerging market stock prices with oil, gold, VIX, and bonds: A comparison between DCC, ADCC and GO-GARCH," Energy Economics, Elsevier, vol. 54(C), pages 235-247.
    17. Chkili, Walid & Aloui, Chaker & Nguyen, Duc Khuong, 2014. "Instabilities in the relationships and hedging strategies between crude oil and US stock markets: Do long memory and asymmetry matter?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 33(C), pages 354-366.
    18. Joscha Beckmann & Theo Berger & Robert Czudaj & Thi-Hong-Van Hoang, 2017. "Tail dependence between gold and sectorial stocks in China: Perspectives for portfolio diversication," Chemnitz Economic Papers 012, Department of Economics, Chemnitz University of Technology, revised Jul 2017.
    19. Massimiliano Caporin & Juan Ángel Jiménez Martín & Lydia González-Serrano, 2013. "Currency hedging strategies, strategic benchmarks and the Global and Euro Sovereign financial crises," Documentos de Trabajo del ICAE 2013-36, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    20. Kotkatvuori-Örnberg, Juha, 2016. "Dynamic conditional copula correlation and optimal hedge ratios with currency futures," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 60-69.
    21. Arfaoui Mongi & Haj Ali Dhouha, 2016. "Do Structural Breaks Affect Portfolio Designs and Hedging Strategies? International Evidence from Stock-Commodity Markets Linkages," International Journal of Economics and Financial Issues, Econjournals, vol. 6(1), pages 252-270.
    22. Arfaoui Mongi & Ben Rejeb Aymen, 2015. "Return Dynamics and Volatility Spillovers Between FOREX and Stock Markets in MENA Countries: What to Remember for Portfolio Choice?," International Journal of Management and Economics, De Gruyter Open, pages 72-100.
    23. repec:eee:jrpoli:v:53:y:2017:i:c:p:88-102 is not listed on IDEAS
    24. repec:ipg:wpaper:2014-549 is not listed on IDEAS
    25. Boako, Gideon & Alagidede, Paul, 2016. "Global commodities and African stocks: A ‘market of one?’," International Review of Financial Analysis, Elsevier, vol. 44(C), pages 226-237.

    More about this item

    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:taf:apeclt:v:14:y:2007:i:7:p:503-509. 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: (Chris Longhurst). General contact details of provider: http://www.tandfonline.com/RAEL20 .

    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.

    We have no references for this item. You can help adding them by using 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.