DeepVaR: a framework for portfolio risk assessment leveraging probabilistic deep neural networks

Georgios Fatouros (), Georgios Makridis (), Dimitrios Kotios (), John Soldatos (), Michael Filippakis () and Dimosthenis Kyriazis ()
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Georgios Fatouros: University of Piraeus
Georgios Makridis: University of Piraeus
Dimitrios Kotios: University of Piraeus
John Soldatos: Innov-Acts Ltd
Michael Filippakis: University of Piraeus
Dimosthenis Kyriazis: University of Piraeus

Digital Finance, 2023, vol. 5, issue 1, No 3, 29-56

Abstract: Abstract Determining and minimizing risk exposure pose one of the biggest challenges in the financial industry as an environment with multiple factors that affect (non-)identified risks and the corresponding decisions. Various estimation metrics are utilized towards robust and efficient risk management frameworks, with the most prevalent among them being the Value at Risk (VaR). VaR is a valuable risk-assessment approach, which offers traders, investors, and financial institutions information regarding risk estimations and potential investment insights. VaR has been adopted by the financial industry for decades, but the generated predictions lack efficiency in times of economic turmoil such as the 2008 global financial crisis and the COVID-19 pandemic, which in turn affects the respective decisions. To address this challenge, a variety of well-established variations of VaR models are exploited by the financial community, including data-driven and data analytics models. In this context, this paper introduces a probabilistic deep learning approach, leveraging time-series forecasting techniques with high potential of monitoring the risk of a given portfolio in a quite efficient way. The proposed approach has been evaluated and compared to the most prominent methods of VaR calculation, yielding promising results for VaR 99% for forex-based portfolios.

Keywords: Probabilistic deep neural networks; Time-series; Forex; Finance; VaR; Risk assessment; VaR prediction (search for similar items in EconPapers)
JEL-codes: C22 C45 C53 C63 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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DOI: 10.1007/s42521-022-00050-0

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