The impact of covariance misspecification in risk-based portfolios

David Ardia, Kris Boudt, Guido Bolliger & Philippe Gagnon-Fleury

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.
Citation Ardia, D., Boudt, K., Bolliger, G., & Gagnon-Fleury, P. (2017). The impact of covariance misspecification in risk-based portfolios. Annals of Operations Research, 0, 1-5.
Type Journal article (English)
Date of appearance 3-2017
Journal Annals of Operations Research
Volume 0
Pages 1-5