Neural Networks for Portfolio-Level Risk Management: Portfolio Compression, Static Hedging, Counterparty Credit Risk Exposures and Impact on Capital Requirement

ArXiv ID: 2402.17941 “View on arXiv”

Authors: Unknown

Abstract

In this paper, we present an artificial neural network framework for portfolio compression of a large portfolio of European options with varying maturities (target portfolio) by a significantly smaller portfolio of European options with shorter or same maturity (compressed portfolio), which also represents a self-replicating static hedge portfolio of the target portfolio. For the proposed machine learning architecture, which is consummately interpretable by choice of design, we also define the algorithm to learn model parameters by providing a parameter initialisation technique and leveraging the optimisation methodology proposed in Lokeshwar and Jain (2024), which was initially introduced to price Bermudan options. We demonstrate the convergence of errors and the iterative evolution of neural network parameters over the course of optimization process, using selected target portfolio samples for illustration. We demonstrate through numerical examples that the Exposure distributions and Exposure profiles (Expected Exposure and Potential Future Exposure) of the target portfolio and compressed portfolio align closely across future risk horizons under risk-neutral and real-world scenarios. Additionally, we benchmark the target portfolio’s Financial Greeks (Delta, Gamma, and Vega) against the compressed portfolio at future time horizons across different market scenarios generated by Monte-Carlo simulations. Finally, we compare the regulatory capital requirement under the standardised approach for counterparty credit risk of the target portfolio against the compressed portfolio and highlight that the capital requirement for the compact portfolio substantially reduces.

Keywords: Artificial Neural Network, Portfolio Compression, Hedging, Exposure Profiles (EE/PFE), Financial Greeks, Equity Options (European)

Complexity vs Empirical Score

• Math Complexity: 8.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Holy Grail
• Why: The paper uses advanced neural network architectures, optimization theory, and risk-neutral pricing mathematics, while also presenting detailed numerical examples, Monte-Carlo simulations, and specific metrics (exposure profiles, Greeks, capital requirements) demonstrating a backtest-ready implementation.

  flowchart TD
    A["Research Goal: Develop interpretable ANN<br>for portfolio compression & static hedging"] --> B["Input: Large Target Portfolio<br>European Options"]
    B --> C["Key Methodology: ANN Architecture<br>with Parameter Initialization & Optimization"]
    C --> D["Computation: Monte-Carlo Simulations<br>Iterative Parameter Learning"]
    D --> E["Outcome 1: Aligned Risk Exposures<br>EE & PFE across Risk Horizons"]
    D --> F["Outcome 2: Consistent Greeks<br>Delta, Gamma, Vega Benchmark"]
    D --> G["Outcome 3: Reduced Capital Requirements<br>Standardised Approach for CCR"]