Noise sensitivity of portfolio selection in constant conditional correlation GARCH models

I. Varga-Haszonits and Imre Kondor ()

Physica A: Statistical Mechanics and its Applications, 2007, vol. 385, issue 1, 307-318

Abstract: This paper investigates the efficiency of minimum variance portfolio optimization for stock price movements following the Constant Conditional Correlation GARCH process proposed by Bollerslev. Simulations show that the quality of portfolio selection can be improved substantially by computing optimal portfolio weights from conditional covariances instead of unconditional ones. Measurement noise can be further reduced by applying some filtering method on the conditional correlation matrix (such as Random Matrix Theory based filtering). As an empirical support for the simulation results, the analysis is also carried out for a time series of S&P500 stock prices.

Keywords: Noisy covariance matrices; Multivariate GARCH models; Constant conditional correlation; Portfolio optimization (search for similar items in EconPapers)
Date: 2007
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Citations: View citations in EconPapers (19)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:385:y:2007:i:1:p:307-318

DOI: 10.1016/j.physa.2007.06.017

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