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On the economic benefit of utility based estimation of a volatility model

  • Adam Clements

    ()

    (QUT)

  • Annastiina Silvennoinen

    ()

    (QUT)

Forecasts of asset return volatility are necessary for many financial applications, including portfolio allocation. Traditionally, the parameters of econometric models used to generate volatility forecasts are estimated in a statistical setting and subsequently used in an economic setting such as portfolio allocation. Differences in the criteria under which the model is estimated and applied may inhibit reduce the overall economic benefit of a model in the context of portfolio allocation. This paper investigates the economic benefit of direct utility based estimation of the parameters of a volatility model and allows for practical issues such as transactions costs to be incorporated within the estimation scheme. In doing so, we compare the benefits stemming from various estimators of historical volatility in the context of portfolio allocation. It is found that maximal utility based estimation, taking into account transactions costs, of a simple volatility model is preferred on the basis of greater realized utility. Estimation of models using historical daily returns is preferred over historical realized volatility.

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File URL: http://www.ncer.edu.au/papers/documents/WPNo44.pdf
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Paper provided by National Centre for Econometric Research in its series NCER Working Paper Series with number 44.

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Length: 17 pages
Date of creation: 21 Jul 2009
Date of revision:
Handle: RePEc:qut:auncer:2009_57
Contact details of provider: Phone: 07 3138 5066
Fax: 07 3138 1500
Web page: http://www.ncer.edu.au

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  1. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
  2. Fleming, Jeff & Kirby, Chris & Ostdiek, Barbara, 2003. "The economic value of volatility timing using "realized" volatility," Journal of Financial Economics, Elsevier, vol. 67(3), pages 473-509, March.
  3. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
  4. Kenneth D. West & Hali J. Edison & Dongchul Cho, 1993. "A utility based comparison of some models of exchange rate volatility," International Finance Discussion Papers 441, Board of Governors of the Federal Reserve System (U.S.).
  5. Spyros Skouras, 2001. "Decisionmetrics: A Decision-Based Approach to Econometric Modeling," Working Papers 01-11-064, Santa Fe Institute.
  6. 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.
  7. Andersen T. G & Bollerslev T. & Diebold F. X & Labys P., 2001. "The Distribution of Realized Exchange Rate Volatility," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 42-55, March.
  8. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2001. "Modeling and Forecasting Realized Volatility," Center for Financial Institutions Working Papers 01-01, Wharton School Center for Financial Institutions, University of Pennsylvania.
  9. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
  10. Engle, Robert & Colacito, Riccardo, 2006. "Testing and Valuing Dynamic Correlations for Asset Allocation," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 238-253, April.
  11. Jeff Fleming, 2001. "The Economic Value of Volatility Timing," Journal of Finance, American Finance Association, vol. 56(1), pages 329-352, 02.
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