The impact of covariance misspecification in risk-based portfolios

David Ardia, Guido Bolliger (), Kris Boudt and Jean-Philippe Gagnon-Fleury ()
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Guido Bolliger: University of Neuchâtel
Jean-Philippe Gagnon-Fleury: Université Laval

Annals of Operations Research, 2017, vol. 254, issue 1, No 1, 16 pages

Abstract: 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.

Keywords: Covariance misspecification; Monte Carlo study; Risk-based portfolios (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (22)

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DOI: 10.1007/s10479-017-2474-7

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