IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Log in (now much improved!) to save this article

Optimization of portfolio management based on vector autoregression models and multivariate volatility models

Author Info

Listed author(s):
  • Habrov, Vladimir

    ()

    (Ministry of Finance of the Russian Federation)

Registered author(s):

    Abstract

    Theoretical part of this article examines the impact of information on the stochastic model of generating returns of assets (vector autoregressive model) on the optimal structure of assets allocation of the investment portfolio. Article includes theoretical basis for construction and characteristics of the optimal portfolios. The results of simulation showed that the characteristics of the studied portfolios under certain conditions may significantly exceed the performance of classic mean-variance portfolios. The practical part examines the characteristics of optimal portfolios whose asset returns are predicted by the VAR models and the covariance matrixes of the assets using multivariate models of volatility. The results of practical studies have shown that the model volatility significantly affect the characteristics of optimal portfolios, and also confirmed the need and importance of investigating the errors characteristics of forecasts of portfolio returns.

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

    File URL: http://pe.cemi.rssi.ru/pe_2012_4_35-62.pdf
    File Function: Full text
    Download Restriction: no

    Article provided by Publishing House "SINERGIA PRESS" in its journal Applied Econometrics.

    Volume (Year): 28 (2012)
    Issue (Month): 4 ()
    Pages: 35-62

    as
    in new window

    Handle: RePEc:ris:apltrx:0195
    Contact details of provider: Web page: http://appliedeconometrics.cemi.rssi.ru/

    Keywords: portfolio theory; vector autoregression model; multivariate volatility models; quadratic programming.;

    Find related papers by JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    References listed on IDEAS
    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

    as
    in new window


    1. Fama, Eugene F & French, Kenneth R, 1992. " The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-465, June.
    2. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(01), pages 122-150, February.
    3. Pagan, Adrian R. & Schwert, G. William, 1990. "Alternative models for conditional stock volatility," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 267-290.
    4. 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.
    5. Massimiliano Marcellino, "undated". "Instability and non-linearity in the EMU," Working Papers 211, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    6. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    7. John H. Cochrane & Monika Piazzesi, 2005. "Bond Risk Premia," American Economic Review, American Economic Association, vol. 95(1), pages 138-160, March.
    8. Alexei Kolokolov, 2011. "Futures hedging: Multivariate GARCH with dynamic conditional correlations (in Russian)," Quantile, Quantile, issue 9, pages 61-75, July.
    9. Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-131, February.
    10. Bénédicte Vidaillet & V. D'Estaintot & P. Abécassis, 2005. "Introduction," Post-Print hal-00287137, HAL.
    11. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, 03.
    12. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    13. Paul A. Samuelson, 2011. "Lifetime Portfolio Selection by Dynamic Stochastic Programming," World Scientific Book Chapters,in: THE KELLY CAPITAL GROWTH INVESTMENT CRITERION THEORY and PRACTICE, chapter 31, pages 465-472 World Scientific Publishing Co. Pte. Ltd..
    14. Merton, Robert C, 1969. "Lifetime Portfolio Selection under Uncertainty: The Continuous-Time Case," The Review of Economics and Statistics, MIT Press, vol. 51(3), pages 247-257, August.
    15. Yilmaz, Tolgahan, 2010. "Improving Portfolio Optimization by DCC And DECO GARCH: Evidence from Istanbul Stock Exchange," MPRA Paper 27314, University Library of Munich, Germany.
    16. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    17. Fama, Eugene F & Bliss, Robert R, 1987. "The Information in Long-Maturity Forward Rates," American Economic Review, American Economic Association, vol. 77(4), pages 680-692, September.
    18. Jobson, J D & Korkie, Bob M, 1981. "Performance Hypothesis Testing with the Sharpe and Treynor Measures," Journal of Finance, American Finance Association, vol. 36(4), pages 889-908, September.
    Full references (including those not matched with items on IDEAS)

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

    as
    in new window


    Cited by:

    1. Lakshina, Valeriya, 2014. "Is it possible to break the «curse of dimensionality»? Spatial specifications of multivariate volatility models," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 36(4), pages 61-78.

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    Access and download statistics

    When requesting a correction, please mention this item's handle: RePEc:ris:apltrx:0195. 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: (Anatoly Peresetsky)

    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 references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link 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 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.

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.