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Related Experiment Video

Updated: Oct 19, 2025

An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

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Implementation of a Commitment Machine for an Adaptive and Robust Expected Shortfall Estimation.

Marco Bagnato1, Anna Bottasso2, Pier Giuseppe Giribone2,3

  • 1Data and AI, SoftJam, Genoa, Italy.

Frontiers in Artificial Intelligence
|September 17, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces the Commitment Machine (CM), a novel metaheuristic for selecting optimal Expected Shortfall (ES) estimation models. CM dynamically adapts to asset cross-correlations, improving risk measurement for multi-asset portfolios.

Keywords:
artificial intelligencebayesian vector autoregressivecommitment machinedynamic neural networksexpected shortfallmonte carlo methodsnonlinear auto-regressive networksstochastic differential equation

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Area of Science:

  • Quantitative Finance
  • Computational Finance
  • Risk Management

Background:

  • Accurate estimation of Expected Shortfall (ES) is crucial for financial risk management.
  • Existing ES estimation methods often struggle to dynamically adapt to changing market conditions and asset interdependencies.
  • The need for adaptive and robust ES estimation techniques that account for cross-correlations is paramount.

Purpose of the Study:

  • To propose a novel metaheuristic approach, the Commitment Machine (CM), for adaptive model selection in Expected Shortfall (ES) estimation.
  • To dynamically evaluate and select the most performing ES estimation method by minimizing a loss function, considering asset cross-correlations.
  • To enhance the accuracy of ES estimation by integrating diverse methodologies and machine learning.

Main Methods:

  • Development of the Commitment Machine (CM) metaheuristic for adaptive ES model selection.
  • Comparison of four ES estimation techniques: Bayesian Vector Autoregressive (BVAR) model, Stochastic Differential Equation (SDE) with Exponential Weighted Moving Average (EWMA), Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, and a hybrid Deep Recurrent Neural Network (DRNN) with Monte Carlo simulation.
  • Dynamic evaluation of model performance through loss function minimization, emphasizing asset cross-correlation.

Main Results:

  • The CM metaheuristic effectively selects the optimal ES estimation model by dynamically considering asset cross-correlations.
  • Integration of Monte Carlo methods with Machine Learning technologies and diverse estimation techniques leads to improved ES estimation.
  • The proposed approach demonstrates superior performance in a simulated multi-asset portfolio scenario.

Conclusions:

  • The Commitment Machine (CM) offers a robust and adaptive framework for selecting appropriate Expected Shortfall (ES) estimation models.
  • The dynamic selection mechanism enhances risk measurement accuracy, particularly in complex multi-asset environments.
  • This metaheuristic approach represents a significant advancement in quantitative risk management and financial modeling.