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Orthogonal GARCH matrixes in the active portfolio management of defined benefit pension plans: A test for Michoacán

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  • Oscar De la Torre Torres.

    (Universidad Michoacana de San Nicolás de Hidalgo.)

Abstract

This paper presents the usefulness of an active portfolio management process with orthogonal garch (ogarch) matrixes in order to achieve a 7.5% actuarial target return in defined benefit pension funds such as the Dirección de Pensiones Civiles del Estado de Michoacán. To prove this, four discrete event simulations were performed using, in the first scenario, a passive portfolio management process with a target position rebalancing discipline and, in the other three, an active portfolio management with a range portfolio rebalancing one. In these last three simulations, a constant covariance, a Gaussian distribution ogarch and a Student's t-distribution ogarch covariance matrix were used. The attained results suggest that the Student's t-distribution ogarch matrix is the most suitable for the investment process.

Suggested Citation

  • Oscar De la Torre Torres., 2013. "Orthogonal GARCH matrixes in the active portfolio management of defined benefit pension plans: A test for Michoacán," Economía: teoría y práctica, Universidad Autónoma Metropolitana, México, vol. 39(2), pages 119-144, Julio-Dic.
  • Handle: RePEc:ety:journl:v:39:y:2013:i:2:p:119-144
    DOI: 10.24275/ETYPUAM/NE/392013/DelaTorre
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    References listed on IDEAS

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    1. William F. Sharpe, 1963. "A Simplified Model for Portfolio Analysis," Management Science, INFORMS, vol. 9(2), pages 277-293, January.
    2. Weide, R. van der, 2002. "Generalized Orthogonal GARCH. A Multivariate GARCH model," CeNDEF Working Papers 02-02, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    3. Vernon L. Smith, 1962. "An Experimental Study of Competitive Market Behavior," Journal of Political Economy, University of Chicago Press, vol. 70(3), pages 322-322.
    4. Daniel, Kent, et al, 1997. "Measuring Mutual Fund Performance with Characteristic-Based Benchmarks," Journal of Finance, American Finance Association, vol. 52(3), pages 1035-1058, July.
    5. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    6. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    7. Roy van der Weide, 2002. "GO-GARCH: a multivariate generalized orthogonal GARCH model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 549-564.
    8. Best, Michael J & Grauer, Robert R, 1991. "On the Sensitivity of Mean-Variance-Efficient Portfolios to Changes in Asset Means: Some Analytical and Computational Results," The Review of Financial Studies, Society for Financial Studies, vol. 4(2), pages 315-342.
    9. Levy, H & Markowtiz, H M, 1979. "Approximating Expected Utility by a Function of Mean and Variance," American Economic Review, American Economic Association, vol. 69(3), pages 308-317, June.
    10. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    portfolio choice; asset pricing; financial forecasting and simulation; hypothesis testing.;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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