IDEAS home Printed from https://ideas.repec.org/p/fip/feddop/2016_002.html
   My bibliography  Save this paper

Dynamic Methods for Analyzing Hedge-Fund Performance: A Note Using Texas Energy-Related Funds

Author

Abstract

We apply dynamic regression to Texas energy-related hedge funds to track changes in portfolio structure and manager performance in response to changing oil prices. We apply hidden Markov models to compute shifts in portfolio performance from boom to bust states. Using these dynamic methods, we find that, in the recent oil-price decline, these funds raised their exposure to high-grade energy-related bonds in a bet that the spread to low-grade energy bonds would widen. When the high-grade bonds eventually fell, the hedge funds entered into a bust state.

Suggested Citation

  • Jiaqi Chen & Michael Tindall, 2016. "Dynamic Methods for Analyzing Hedge-Fund Performance: A Note Using Texas Energy-Related Funds," Occasional Papers 16-2, Federal Reserve Bank of Dallas.
    • Handle: RePEc:fip:feddop:2016_002
    as

    Download full text from publisher

    File URL: https://www.dallasfed.org/-/media/documents/banking/occasional/1602.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    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. Milan Kumar Das & Anindya Goswami, 2019. "Testing of binary regime switching models using squeeze duration analysis," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 6(01), pages 1-20, March.
    2. Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020. "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 829-850.
    3. David Andolfatto & Paul Gomme, 2003. "Monetary Policy Regimes and Beliefs," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(1), pages 1-30, February.
    4. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    5. Perron, Pierre & Wada, Tatsuma, 2016. "Measuring business cycles with structural breaks and outliers: Applications to international data," Research in Economics, Elsevier, vol. 70(2), pages 281-303.
    6. Nemati, Mehdi & Saghaian, Sayed H., 2016. "Dynamics of Price Adjustment in Qualitatively Differentiated Markets in the U.S.: The Case of Organic and Conventional Apples," 2016 Annual Meeting, February 6-9, 2016, San Antonio, Texas 229950, Southern Agricultural Economics Association.
    7. Flavin, Thomas J. & Panopoulou, Ekaterini & Unalmis, Deren, 2008. "On the stability of domestic financial market linkages in the presence of time-varying volatility," Emerging Markets Review, Elsevier, vol. 9(4), pages 280-301, December.
    8. Shyh-Wei Chen, 2008. "Non-stationarity and Non-linearity in Stock Prices: Evidence from the OECD Countries," Economics Bulletin, AccessEcon, vol. 3(11), pages 1-11.
    9. David Bolder & Shudan Liu, 2007. "Examining Simple Joint Macroeconomic and Term-Structure Models: A Practitioner's Perspective," Staff Working Papers 07-49, Bank of Canada.
    10. Candelon, Bertrand & Lieb, Lenard, 2013. "Fiscal policy in good and bad times," Journal of Economic Dynamics and Control, Elsevier, vol. 37(12), pages 2679-2694.
    11. Francesco Bianchi, 2013. "Regime Switches, Agents' Beliefs, and Post-World War II U.S. Macroeconomic Dynamics," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 80(2), pages 463-490.
    12. Saki Bigio & Eduardo Zilberman, 2020. "Speculation-Driven Business Cycles," Working Papers Central Bank of Chile 865, Central Bank of Chile.
    13. René Garcia & Richard Luger & Eric Renault, 2000. "Asymmetric Smiles, Leverage Effects and Structural Parameters," Working Papers 2000-57, Center for Research in Economics and Statistics.
    14. Muñoz, Mª Pilar & Márquez, María Dolores & Sánchez, Josep A., 2011. "Contagion between United States and european markets during the recent crises," MPRA Paper 35993, University Library of Munich, Germany.
    15. Garcia, Rene, 1998. "Asymptotic Null Distribution of the Likelihood Ratio Test in Markov Switching Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(3), pages 763-788, August.
    16. Ning, Ye & Zhang, Lingxiang, 2018. "Modeling dynamics of short-term international capital flows in China: A Markov regime switching approach," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 193-203.
    17. Alain Monfort & Olivier Féron, 2012. "Joint econometric modeling of spot electricity prices, forwards and options," Review of Derivatives Research, Springer, vol. 15(3), pages 217-256, October.
    18. Chao, Hui-Ping, 1998. "Regime Switching In Us Livestock Cycles," 1998 Annual meeting, August 2-5, Salt Lake City, UT 20824, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    19. He, Hui & Yang, Jiawen, 2011. "Regime-switching analysis of ADR home market pass-through," Journal of Banking & Finance, Elsevier, vol. 35(1), pages 204-214, January.
    20. Peter McAdam, 2007. "USA, Japan and the Euro Area: Comparing Business-Cycle Features," International Review of Applied Economics, Taylor & Francis Journals, vol. 21(1), pages 135-156.

    More about this item

    Keywords

    hidden Markov models; Kalman filter; Dynamic regression;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:fip:feddop:2016_002. 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: Amy Chapman (email available below). General contact details of provider: https://edirc.repec.org/data/frbdaus.html .

    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.