Forward-Looking Volatility Estimation for Risk-Managed Investment Strategies during the COVID-19 Crisis

Luca Di Persio, Matteo Garbelli and Kai Wallbaum
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Luca Di Persio: Department of Computer Science, University of Verona, 37134 Verona, Italy
Matteo Garbelli: Department of Mathematics, University of Trento, 38123 Trento, Italy
Kai Wallbaum: RiskLab, Allianz Global Investors, Seidlstrasse 24-24a, 80335 Munchen, Germany

Risks, 2021, vol. 9, issue 2, 1-16

Abstract: Under the impact of both increasing credit pressure and low economic returns characterizing developed countries, investment levels have decreased over recent years. Moreover, the recent turbulence caused by the COVID-19 crisis has accelerated the latter process. Within this scenario, we consider the so-called Volatility Target (VolTarget) strategy. In particular, we focus our attention on estimating volatility levels of a risky asset to perform a VolTarget simulation over two different time horizons. We first consider a 20 year period, from January 2000 to January 2020, then we analyse the last 12 months to emphasize the effects related to the COVID-19 virus’s diffusion. We propose a hybrid algorithm based on the composition of a GARCH model with a Neural Network (NN) approach. Let us underline that, as an alternative to standard allocation methods based on realized and backward oriented volatilities, we exploited an innovative forward-looking estimation process exploiting a Machine Learning (ML) solution. Our solution provides a more accurate volatility estimation, allowing us to derive an effective investor risk-return profile during market crisis periods. Moreover, we show that, via a forward-looking VolTarget strategy while using an ML-based prediction as the input, the average outcome for an investment in a drawdown plan is more sustainable while representing an efficient risk-control solution for long time period investments.

Keywords: volatility estimation; neural network; portfolio simulation; VolTarget strategy (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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