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Model Risk in Financial Derivatives and The Transformative Impact of Deep Learning: A Systematic Review

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Innovations in Smart Cities Applications Volume 7 (SCA 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 938))

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Abstract

This paper presents a comprehensive examination of model risk within the derivatives pricing context, specifically focusing on autocallable products. It identifies potential sources of model risk, encompassing insufficient model assumptions, calibration challenges, data availability limitations, and overfitting concerns. The paper explores diverse approaches for model risk mitigation in derivatives pricing, including robust model selection, rigorous model validation, risk sensitivity analysis, and model diversification. Moreover, it investigates the utilization of advanced machine learning techniques to alleviate model risk and discusses alternatives to tree methods, such as manifold learning algorithms and topological data analysis, for gaining deeper insights into intricate datasets and pricing relationships. The paper concludes with an analysis of autocallable notes, emphasizing the critical importance of accurately capturing correlations between underlying assets and their corresponding volatilities.

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Notes

  1. 1.

    Stress testing is also a regulatory process that assesses the resilience of banks and their ability to withstand adverse economic conditions by subjecting them to hypothetical scenarios. In the Eurozone, stress testing is conducted by the European Central Bank (ECB) and the European Banking Authority (EBA), while in the US, it is overseen by the Federal Reserve, the Office of the Comptroller of the Currency (OCC), and the Federal Deposit Insurance Corporation (FDIC).

  2. 2.

    Tree methods in machine learning, such as decision trees, are algorithms used for predictive modeling based on branching decision-making logic, not to confuse with binomial trees in derivative pricing which are financial models that visualize possible paths for asset prices over time using a binary, branching structure.

  3. 3.

    It is a type of stochastic volatility model which assumes that the volatility of the underlying asset is the product of both a random (stochastic) and a deterministic, function of the underlying, factors.

  4. 4.

    The dependence of the duration of an autocallable note on the level of the underlying asset introduces sensitivity to the correlation between the underlying asset and interest rates, impacting the effectiveness of hedging strategies and overall risk exposure.

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Authors and Affiliations

  1. Data and Intelligent Systems Team, FSTT AbdelMalek Esaadi University, Tetouan, Morocco

    Mohammed Ahnouch, Lotfi Elaachak & Abderrahim Ghadi

Authors
  1. Mohammed Ahnouch
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  2. Lotfi Elaachak
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  3. Abderrahim Ghadi
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Correspondence to Mohammed Ahnouch .

Editor information

Editors and Affiliations

  1. Faculty of Sciences and Techniques, Abdelmalek Essaadi University, Tangier, Morocco

    Mohamed Ben Ahmed

  2. Fac Sciences et Techniques de Tanger, Université Abdelmalek Essaâdi, Tangier, Morocco

    Anouar Abdelhakim Boudhir

  3. École Spéciale des Travaux Publics, Paris, France

    Rani El Meouche

  4. Computer Engineering Department, Karabük University, Karabük, Türkiye

    İsmail Rakıp Karaș

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Ahnouch, M., Elaachak, L., Ghadi, A. (2024). Model Risk in Financial Derivatives and The Transformative Impact of Deep Learning: A Systematic Review. In: Ben Ahmed, M., Boudhir, A.A., El Meouche, R., Karaș, İ.R. (eds) Innovations in Smart Cities Applications Volume 7. SCA 2023. Lecture Notes in Networks and Systems, vol 938. Springer, Cham. https://doi.org/10.1007/978-3-031-54376-0_14

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  • DOI: https://doi.org/10.1007/978-3-031-54376-0_14

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