IDEAS home Printed from https://ideas.repec.org/a/oup/revfin/v19y2015i6p2359-2399..html
   My bibliography  Save this article

An Out-of-Sample Evaluation of Dynamic Portfolio Strategies

Author

Listed:
  • Chunhua Lan

Abstract

This article evaluates out-of-sample portfolio performance for a real-time investor who can exploit time variation in the conditional mean and volatility of stock returns in optimizing a multiperiod portfolio choice problem. With the presence of parameter uncertainty, our out-of-sample analysis shows that ignoring time variation in the first two return moments leads to significant utility costs of at least 1.97% of annualized certainty equivalent return. Accounting for the time-varying risk premium plays a more important role than considering time-varying volatility in improving portfolio performance. Interestingly, behaving myopically or ignoring the hedge against changes in future investment opportunities can lead to small out-of-sample utility losses or even utility gains.

Suggested Citation

  • Chunhua Lan, 2015. "An Out-of-Sample Evaluation of Dynamic Portfolio Strategies," Review of Finance, European Finance Association, vol. 19(6), pages 2359-2399.
    • Handle: RePEc:oup:revfin:v:19:y:2015:i:6:p:2359-2399.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/rof/rfu052
    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. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    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. Chung, San-Lin & Hung, Mao-Wei & Wei, Tzu-Wen & Yeh, Chung-Ying, 2024. "Strategic asset allocation with distorted beliefs," International Review of Economics & Finance, Elsevier, vol. 89(PB), pages 804-831.
    2. Laborda, Ricardo & Olmo, Jose, 2017. "Optimal asset allocation for strategic investors," International Journal of Forecasting, Elsevier, vol. 33(4), pages 970-987.
    3. Castañeda, Pablo & Reus, Lorenzo, 2019. "Suboptimal investment behavior and welfare costs: A simulation based approach," Finance Research Letters, Elsevier, vol. 30(C), pages 170-180.
    4. Michaelides, Alexander & Zhang, Yuxin, 2022. "Life-cycle portfolio choice with imperfect predictors," Journal of Banking & Finance, Elsevier, vol. 135(C).
    5. Fischer, Marcel & Gallmeyer, Michael F., 2016. "Heuristic portfolio trading rules with capital gain taxes," Journal of Financial Economics, Elsevier, vol. 119(3), pages 611-625.
    6. Michael Senescall & Rand Kwong Yew Low, 2024. "Quantitative Portfolio Management: Review and Outlook," Mathematics, MDPI, vol. 12(18), pages 1-25, September.
    7. Lioui, Abraham & Tarelli, Andrea, 2020. "Factor Investing for the Long Run," Journal of Economic Dynamics and Control, Elsevier, vol. 117(C).
    8. Zhou, Zhongbao & Xiao, Helu & Jin, Qianying & Liu, Wenbin, 2018. "DEA frontier improvement and portfolio rebalancing: An application of China mutual funds on considering sustainability information disclosure," European Journal of Operational Research, Elsevier, vol. 269(1), pages 111-131.

    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. Sucarrat, Genaro, 2009. "Forecast Evaluation of Explanatory Models of Financial Variability," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 3, pages 1-33.
    2. Leopoldo Catania & Stefano Grassi, 2017. "Modelling Crypto-Currencies Financial Time-Series," CEIS Research Paper 417, Tor Vergata University, CEIS, revised 11 Dec 2017.
    3. 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.
    4. Graham Elliott & Allan Timmermann, 2016. "Economic Forecasting," Economics Books, Princeton University Press, edition 1, number 10740.
    5. Richard T. Baillie & Fabio Calonaci & Dooyeon Cho & Seunghwa Rho, 2019. "Long Memory, Realized Volatility and HAR Models," Working Papers 881, Queen Mary University of London, School of Economics and Finance.
    6. Stavros Degiannakis & Apostolos Kiohos, 2014. "Multivariate modelling of 10-day-ahead VaR and dynamic correlation for worldwide real estate and stock indices," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 41(2), pages 216-232, March.
    7. Degiannakis, Stavros & Filis, George, 2018. "Forecasting oil prices: High-frequency financial data are indeed useful," Energy Economics, Elsevier, vol. 76(C), pages 388-402.
    8. Golosnoy, Vasyl & Gribisch, Bastian & Liesenfeld, Roman, 2015. "Intra-daily volatility spillovers in international stock markets," Journal of International Money and Finance, Elsevier, vol. 53(C), pages 95-114.
    9. Jeremy Berkowitz & Peter Christoffersen & Denis Pelletier, 2011. "Evaluating Value-at-Risk Models with Desk-Level Data," Management Science, INFORMS, vol. 57(12), pages 2213-2227, December.
    10. Gadea Rivas, María Dolores & Gonzalo, Jesús, 2020. "Trends in distributional characteristics: Existence of global warming," Journal of Econometrics, Elsevier, vol. 214(1), pages 153-174.
    11. Cem Cakmakli & Dick van Dijk, 2010. "Getting the Most out of Macroeconomic Information for Predicting Stock Returns and Volatility," Tinbergen Institute Discussion Papers 10-115/4, Tinbergen Institute.
    12. Aymanns, Christoph & Farmer, J. Doyne, 2015. "The dynamics of the leverage cycle," Journal of Economic Dynamics and Control, Elsevier, vol. 50(C), pages 155-179.
    13. Tim Bollerslev & Benjamin Hood & John Huss & Lasse Heje Pedersen, 2018. "Risk Everywhere: Modeling and Managing Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2729-2773.
    14. Davide Pettenuzzo & Rossen Valkanov & Allan Timmermann, 2014. "A Bayesian MIDAS Approach to Modeling First and Second Moment Dynamics," Working Papers 76, Brandeis University, Department of Economics and International Business School.
    15. Steffen R. Henzel & Malte Rengel, 2017. "Dimensions Of Macroeconomic Uncertainty: A Common Factor Analysis," Economic Inquiry, Western Economic Association International, vol. 55(2), pages 843-877, April.
    16. Elena Andreou & Constantinos Kourouyiannis & Andros Kourtellos, 2012. "Volatility Forecast Combinations using Asymmetric Loss Functions," University of Cyprus Working Papers in Economics 07-2012, University of Cyprus Department of Economics.
    17. Julien Chevallier & Benoît Sévi, 2013. "A Fear Index to Predict Oil Futures Returns," Working Papers 2013.62, Fondazione Eni Enrico Mattei.
    18. Konstantinos Metaxoglou & Davide Pettenuzzo & Aaron Smith, 2019. "Option-Implied Equity Premium Predictions via Entropic Tilting," Journal of Financial Econometrics, Oxford University Press, vol. 17(4), pages 559-586.
    19. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
    20. Lucien Boulet, 2021. "Forecasting High-Dimensional Covariance Matrices of Asset Returns with Hybrid GARCH-LSTMs," Papers 2109.01044, arXiv.org.

    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:oup:revfin:v:19:y:2015:i:6:p:2359-2399.. 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: Oxford University Press (email available below). General contact details of provider: https://edirc.repec.org/data/eufaaea.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.