This solution introduces a novel application of machine learning techniques within model validation, specifically focusing on machine learning-based market generators. These generators address a persistent challenge in risk management by producing more accurate generated market scenarios for interest rates and FX compared to traditional methods. Market generators are defined as machine learning algorithms designed to create realistic samples of market data when historical time series data is either insufficient in length or contains gaps. While recent research has primarily concentrated on daily time horizons, the generation of realistic market data samples for horizons ranging from 1 year to 30 years and beyond has significant applications in areas such as limit management, insurance (economic scenario generation), and macro investing. The presentation details a family of market generators that leverage machine learning to produce market scenarios with accurate probability distributions over extended time horizons, even when working with limited time series data.

Features & Benefits

• Machine Learning-Based Market Generators: Generates realistic market data samples for long risk horizons (1-30+ years) from limited time series.
• Improved Accuracy: Demonstrates better accuracy in generated market scenarios for interest rates and FX compared to traditional techniques.
• Addresses Data Limitations: Solves the problem of insufficient length or gaps in historical time series data for scenario generation.
• Applications in Risk Management: Applicable to limit management, insurance (economic scenario generation), and macro investing.
• Focus on Model Validation: Applies machine learning techniques to enhance the accuracy and reliability of model validation processes.