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Dynamic betas for Canadian sector portfolios

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  • He, Zhongzhi (Lawrence)
  • Kryzanowski, Lawrence
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    Abstract

    The dynamic betas for ten Canadian sector portfolios using the Kalman filter approach are estimated herein and are found to be best described by a mix of the random walk (trend) and mean-reverting (cycle) processes. The relative importance of the trend and cycle components of sector betas is related to different sensitivities of the corresponding sectors to business cycles. Dynamic betas significantly increase the explanatory power of the market model, and particularly for the utilities sector. A dynamic hedging strategy using the one-step-ahead beta forecasts as the hedge ratios produces smaller hedging errors for every sector compared with the hedge ratios calculated from the alternative beta specifications.

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    Bibliographic Info

    Article provided by Elsevier in its journal International Review of Financial Analysis.

    Volume (Year): 17 (2008)
    Issue (Month): 5 (December)
    Pages: 1110-1122

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    Handle: RePEc:eee:finana:v:17:y:2008:i:5:p:1110-1122

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    Web page: http://www.elsevier.com/locate/inca/620166

    Related research

    Keywords: Dynamic betas Sector portfolios Kalman filter Market model performance;

    References

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    1. Adrian, Tobias & Franzoni, Francesco, 2009. "Learning about beta: Time-varying factor loadings, expected returns, and the conditional CAPM," Journal of Empirical Finance, Elsevier, vol. 16(4), pages 537-556, September.
    2. Sunder, Shyam, 1980. " Stationarity of Market Risk: Random Coefficients Tests for Individual Stocks," Journal of Finance, American Finance Association, vol. 35(4), pages 883-96, September.
    3. Sascha Mergner & Jan Bulla, 2005. "Time-varying Beta Risk of Pan-European Industry Portfolios: A Comparison of Alternative Modeling Techniques," Finance 0510029, EconWPA.
    4. Jostova, Gergana & Philipov, Alexander, 2005. "Bayesian Analysis of Stochastic Betas," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 40(04), pages 747-778, December.
    5. Schwert, G.W. & Seguin, P.J., 1988. "Heteroskedasticity In Stock Returns," Papers bc_88-02, Rochester, Business - General.
    6. Fama, Eugene F & MacBeth, James D, 1973. "Risk, Return, and Equilibrium: Empirical Tests," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 607-36, May-June.
    7. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, December.
    8. Fama, Eugene F. & French, Kenneth R., 1997. "Industry costs of equity," Journal of Financial Economics, Elsevier, vol. 43(2), pages 153-193, February.
    9. Abdul Rahman & Lawrence Kryzanowski & Ah Boon Sim, 1987. "Systematic Risk In A Purely Random Market Model: Some Empirical Evidence For Individual Public Utilities," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 10(2), pages 143-152, 06.
    10. Fabozzi, Frank J. & Francis, Jack Clark, 1978. "Beta as a Random Coefficient," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 13(01), pages 101-116, March.
    11. Robert W. Faff & David Hillier & Joseph Hillier, 2000. "Time Varying Beta Risk: An Analysis of Alternative Modelling Techniques," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 27(5&6), pages 523-554.
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    Cited by:
    1. Michael Ehrmann & Damjan Pfajfar & Emiliano Santoro, 2014. "Consumer Attitudes and the Epidemiology of Inflation Expectations," Working Papers 14-28, Bank of Canada.
    2. Radosław Kurach, 2013. "Does Beta Explain Global Equity Market Volatility – Some Empirical Evidence," Contemporary Economics, University of Finance and Management in Warsaw, vol. 7(2), June.

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