Lunch Seminar Economics (LSE) - 2020/2021

Interviene: Gabriele Torri, Università di Bergamo

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Abstract

Accurate estimation and optimal control of tail risk is important for building portfolios with desirable properties, especially when dealing with a large set of assets. In this work, we consider optimal asset allocation strategies based on the minimization of two asymmetric deviation measures, related to quantile and expectile regression, respectively. Their properties are discussed in relation with the ‘risk quadrangle’ framework introduced by Rockafellar and Uryasev (2013), and compared to traditional strategies, such as the mean-variance portfolio. In order to control estimation error and improve the out-of-sample performances of the proposed models, we include ridge and elastic-net regularization penalties. We also propose an application of the framework to the enhanced index replication problem, that aims to minimize the asymmetric risk measure related to the expectile, while controlling for the distance from a benchmark by penalizing the deviation of the portfolio weights compared to the ones in an index. Our approach aims to address the needs of investors interested in smart beta products (systematic strategies that  aim to maintain costs smaller than traditional active strategies) in a market context where low-cost ETFs are available. We finally bring attention to a new portfolio statistics: the correlation between tracking error and index return. The analysis is supported by an empirical study on the S&P100 US market index and the FTSE 100 index.