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

Author

Listed:
  • Georgios Fatouros

    (University of Piraeus
    Innov-Acts Ltd)

  • 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)

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.

Suggested Citation

  • Georgios Fatouros & Georgios Makridis & Dimitrios Kotios & John Soldatos & Michael Filippakis & Dimosthenis Kyriazis, 2023. "DeepVaR: a framework for portfolio risk assessment leveraging probabilistic deep neural networks," Digital Finance, Springer, vol. 5(1), pages 29-56, March.
    • Handle: RePEc:spr:digfin:v:5:y:2023:i:1:d:10.1007_s42521-022-00050-0
    DOI: 10.1007/s42521-022-00050-0
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    References listed on IDEAS

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    More about this item

    Keywords

    Probabilistic deep neural networks; Time-series; Forex; Finance; VaR; Risk assessment; VaR prediction;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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