Publication
The COVID-19 pandemic has demonstrated the importance and value of multi-period asset allocation strategies responding to rapid changes in market behavior. In this article, we formulate and solve a multi-stage stochastic optimization problem, choosing the indices' optimal weights dynamically in line with a customized data-driven Bellman's procedure. We use basic asset classes (equities, fixed income, cash and cash equivalents) and five corresponding indices for the development of optimal strategies. In our multi-period setup, the probability model describing the uncertainty about the value of asset returns changes over time and is scenario-specific. Given a high enough variation of model parameters, this allows to account for possible crises events. In this article, we construct optimal allocation strategies accounting for the influence of the COVID-19 pandemic on financial returns. We observe that the growth in the number of infections influences financial markets and makes assets' behavior more dependent. Solving the multi-stage asset allocation problem dynamically, we (i) propose a fully data-driven method to estimate time-varying conditional probability models and (ii) we implement the optimal quantization procedure for the scenario approximation. We consider optimality of quantization methods in the sense of minimal distances between continuous-state distributions and their discrete approximations. Minimizing the well-known Kantorovich-Wasserstein distance at each time stage, we bound the approximation error, enhancing accuracy of the decision-making. Using the first-stage allocation strategy developed via our method, we observe ca. 10% wealth growth on average out-of-sample with a maximum of ca. 20% and a minimum of ca. 5% over a three-month period. Further, we demonstrate that monthly reoptimization aids in reducing uncertainty at a cost of maximal wealth. Also, we show that optimistically off setted distribution parameters lead to a reduction in out-of-sample wealth due to the COVID-19 crisis.