Abstract
In the field of portfolio management, practitioners are focusing increasingly on risk-based portfolios rather than on mean-variance portfolios. Risk-based portfolios are constructed based solely on covariance matrices, and include methods such as minimum variance (MV), risk parity (RP), and maximum diversification (MD). It is well known that the performance of a mean-variance portfolio depends on the accuracy of the estimations of the inputs. However, no studies have examined the relationship between the performance of risk-based portfolios and the estimated accuracy of covariance matrices. In this research, we compare the performance of risk-based portfolios for several estimation methods of covariance matrices in the Japanese stock market. In addition, we propose a highly accurate estimation method called cDCC-NLS, which incorporates nonlinear shrinkage into the cDCC-GARCH model. The results confirm that (1) the cDCC-NLS method shows the best estimation accuracy, (2) the RP and MD do not depend on the estimation accuracy of the covariance matrix, and (3) the MV does depend on the estimation accuracy of the covariance matrix.
Highlights
In the field of portfolio management, practitioners are focusing increasingly on risk-based portfolios, which use methods such as the minimum variance (MV), risk parity (RP), and maximum diversification (MD) to construct portfolios based solely on covariance matrices
We confirm that, unlike MV, RP and MD do not depend on the estimation accuracy of the covariance matrix
We conclude the RP and MD do not depend on the estimation accuracy of the covariance matrix, whereas the MV and minimum variance without short constraint (MVS) do depend on the estimation accuracy of the covariance matrix
Summary
In the field of portfolio management, practitioners are focusing increasingly on risk-based portfolios, which use methods such as the minimum variance (MV), risk parity (RP), and maximum diversification (MD) to construct portfolios based solely on covariance matrices. Ardia et al (2017) estimated the accuracy of covariance matrices in terms of the weights of several risk-based portfolios. To the best of our knowledge, no studies have examined the relation between the performance of risk-based portfolios and the estimated accuracy of covariance matrices. We compare the performances of risk-based portfolios for several estimation methods of covariance matrices. We combine the cDCC model and the NLS method to deliver an improved estimation of the covariance matrix. To test whether the estimation accuracy of the covariance matrices affects the performance of the risk-based portfolio, we compare the accuracy of each method, including the proposed method.
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