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Hypothetical Market Data Scenario Generation Using Generative AI
Generative AI and AI have revolutionized a wide range of industries in recent years. However major use cases have been limited to Large Language Models (LLMs) such as chatbots, code assistants and text analysis. In this white paper we explore the use of generative AI for risk management in the context of central counterparties (CCPs). Since the world of finance is ruled by exact numbers, we want to look at generative models which can handle and generate numerical data. In the paper we will first introduce common AI and machine learning concepts and then explore how to use generative AI models to create synthetic yet realistic market data with a focus on 3-Month TONA Futures contracts. We compare variational autoencoder (VAE) and principal component analysis (PCA) to generate synthetic data and analyze the generated scenarios. Subsequently, we use the generated synthetic market data to estimate the risk profile of various portfolios by calculating Expected Shortfall Value-at-Risk (ES-VaR). The results showed that the VAE generated scenarios were more diverse and affected the ES-VaR more than the PCA generated scenarios.
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