Noise sensitivity of portfolio selection in constant conditional correlation GARCH models

I. Varga-Haszonits, I. Kondor

Research output: Contribution to journalArticle

10 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)307-318
Number of pages12
JournalPhysica A: Statistical Mechanics and its Applications
Volume385
Issue number1
DOIs
Publication statusPublished - Nov 1 2007

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Keywords

  • Constant conditional correlation
  • Multivariate GARCH models
  • Noisy covariance matrices
  • Portfolio optimization

ASJC Scopus subject areas

  • Statistics and Probability
  • Condensed Matter Physics

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