Robust Optimization-Based Commodity Portfolio Performance

Ramesh Adhikari,Humnath Panta,Kyle J Putnam

doi:10.3390/ijfs8030054

Ramesh Adhikari, Humnath Panta + Show 1 more

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https://doi.org/10.3390/ijfs8030054

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Abstract

This paper examines the performance of a naïve equally weighted buy-and-hold portfolio and optimization-based commodity futures portfolios for various lookback and holding periods using data from January 1986 to December 2018. The application of Monte Carlo simulation-based mean-variance and conditional value-at-risk optimization techniques are used to construct the robust commodity futures portfolios. This paper documents the benefits of applying a sophisticated, robust optimization technique to construct commodity futures portfolios. We find that a 12-month lookback period contains the most useful information in constructing optimization-based portfolios, and a 1-month holding period yields the highest returns among all the holding periods examined in the paper. We also find that an optimized conditional value-at-risk portfolio using a 12-month lookback period outperforms an optimized mean-variance portfolio using the same lookback period. Our findings highlight the advantages of using robust optimization for portfolio formation in the presence of return uncertainty in the commodity futures markets. The results also highlight the practical importance of choosing the appropriate lookback and holding period when using robust optimization in the commodity portfolio formation process.

Highlights

Optimization-based portfolio construction techniques play a vital role in both pedagogy and practical applications of finance
We focus on robust optimization constructed on the traditional mean-variance and conditional value-at-risk measures of risk to create optimally weighted futures portfolios for five diverse commodity sectors—foods and fibers, grains and oilseeds, livestock, energy, and precious metals
We find that the robust optimization-based portfolios formed using both 15-month and 18-month lookback periods fail to outperform an weighted commodity futures portfolio

Summary

Introduction

Optimization-based portfolio construction techniques play a vital role in both pedagogy and practical applications of finance. The traditional optimization technique is often an unintentional error maximizer which frequently overweights uncertain statistics. It is intuitive and straightforward, the practical application of mean-variance analysis is problematic because as the number of assets grows, the weights of the individual assets do not approach zero as quickly as suggested by naïve notions of diversification (Green and Hollifield 1992). An increase of 11.6% per annum in the mean of any stock in a portfolio can drive nearly half of the constituents away Such sensitivity via input parameters is very problematic for portfolio optimization because accurate parameter estimation is complicated, for returns (Michaud 1998). The results from various traditional optimization techniques should be used carefully

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