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A new proxy of the average volatility of a basket of returns: A Monte Carlo study

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Author Info
Fulvia Focker () (Banca d'Italia)
Umberto Triacca () (University of L'Aquila)

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Abstract

The volatility of returns plays a pivotal role in modern finance and an accurate evaluation of this parameter is crucial in portfolio and risk management decisions. Until quite recent it was common practice in the literature to use the squared return as proxy of volatility. However, as pointed out by several authors, this measure of volatility includes a large noisy component. In this paper we propose a procedure, based on a generalized dynamic factors model methodology, to obtain a more accurate estimate of volatility of a basket of returns.

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File URL: http://economicsbulletin.vanderbilt.edu/2006/volume3/EB-06C00005A.pdf
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Article provided by Economics Bulletin in its journal Economics Bulletin.

Volume (Year): 3 (2006)
Issue (Month): 15 ()
Pages: 1-14
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Handle: RePEc:ebl:ecbull:v:3:y:2006:i:15:p:1-14

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C0 - Mathematical and Quantitative Methods - - General
C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics

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  1. repec:att:wimass:199317r is not listed on IDEAS
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    Other versions:
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  10. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-47, August. [Downloadable!] (restricted)
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  12. Yasuhiro Omori & Siddhartha Chib & Neil Shephard & Jouchi Nakajima, 2004. "Stochastic Volatility with Leverage: Fast Likelihood Inference," CIRJE F-Series CIRJE-F-297, CIRJE, Faculty of Economics, University of Tokyo. [Downloadable!]
    Other versions:
  13. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
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    Other versions:
  15. Friedman, Moshe & Harris, Lawrence, 1998. "A Maximum Likelihood Approach for Non-Gaussian Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(3), pages 284-91, July.
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  17. Gallant, A. Ronald & Hsieh, David & Tauchen, George, 1995. "Estimation of Stochastic Volatility Models with Diagnostics," Working Papers 95-36, Duke University, Department of Economics.
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