COVID-19: data-driven dynamic asset allocation in times of pandemic

Anna Timonina-Farkas; Anna Timonina-Farkas

doi:10.3934/QFE.2021009

Quantitative Finance and Economics

2021, Volume 5, Issue 2: 198-227. doi: 10.3934/QFE.2021009

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COVID-19: data-driven dynamic asset allocation in times of pandemic

Anna Timonina-Farkas ^,

Technology and Operations Management Chair, École Polytechnique Fédérale de Lausanne, EPFL-CDM-TOM, 1015 Lausanne, Switzerland

Received: 22 December 2020 Accepted: 02 March 2021 Published: 16 March 2021
JEL Codes: G11, G12, G01

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 offsetted distribution parameters lead to a reduction in out-of-sample wealth due to the COVID-19 crisis.
- dynamic asset allocation,
- multi-stage stochastic optimization,
- data-driven optimization,
- optimal quantization,
- COVID-19 pandemic
Citation: Anna Timonina-Farkas. COVID-19: data-driven dynamic asset allocation in times of pandemic[J]. Quantitative Finance and Economics, 2021, 5(2): 198-227. doi: 10.3934/QFE.2021009

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Abstract

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 offsetted distribution parameters lead to a reduction in out-of-sample wealth due to the COVID-19 crisis.

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