The impact of covariance misspecification in risk-based portfolios

David Ardia*, Guido Bolliger, Kris Boudt, Jean Philippe Gagnon-Fleury

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

The equal-risk-contribution, inverse-volatility weighted, maximum-diversification and minimum-variance portfolio weights are all direct functions of the estimated covariance matrix. We perform a Monte Carlo study to assess the impact of covariance matrix misspecification to these risk-based portfolios at the daily, weekly and monthly forecasting horizon. Our results show that the equal-risk-contribution and inverse-volatility weighted portfolio weights are relatively robust to covariance misspecification. In contrast, the minimum-variance portfolio weights are highly sensitive to errors in both the estimated variances and correlations, while errors in the estimated correlations can have a large effect on the weights of the maximum-diversification portfolio.

Original languageEnglish
Pages (from-to)1-16
Number of pages16
JournalAnnals of Operations Research
Volume254
Issue number1-2
DOIs
Publication statusPublished - 2017

Keywords

  • Covariance misspecification
  • Monte Carlo study
  • Risk-based portfolios

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