Constructing Robust Portfolios, the Role of Parameter Uncertainty in Dynamic Optimal Portfolio Allocations
This paper investigates the ability to improve traditional portfolio optimisation rules in practice. Speciﬁcally, I examine the eﬀect of parameter uncertainty on Markowitz portfolio performance and quantify the corresponding losses. Frequentist methods, in the form of di-rect parameter shrinkage and assigned portfolio weight shrinkage are employed to suppress eﬀects of estimation errors. A Bayesian approach of the traditional Markowitz portfolio is used to account for estimation risks implicitly and novel Bayesian portfolio combinations are deﬁned in search of an optimal investment rule. Moreover, the assumption of normal re-turns is relaxed by considering Markov Switching Gaussian Mixture models. I demonstrate the mean-variance modiﬁcations to be eﬀective in improving out-of-sample performance and show ability to beat the equally weighted benchmark for empirical samples of the 48 Industry portfolios and Fama-French 100 portfolios. Lastly, I validate the competence of producing robust portfolios over periods of changing economic times when parameter uncertainty is considered in the Markowitz model.
|Keywords||Portfolio Optimisation, Parameter Uncertainty, Shrinkage, Bayesian Statistics JEL Classiﬁcations: G11, C38, C11, C53|
|Thesis Advisor||Naghi, A.A.|
Pelt, T.G. van. (2020, July 17). Constructing Robust Portfolios, the Role of Parameter Uncertainty in Dynamic Optimal Portfolio Allocations. Econometrie. Retrieved from http://hdl.handle.net/2105/52358