Abstract

This paper provides a comprehensive review of the Vasicek model and its applications in finance, categorizing the literature into four key areas: Vasicek model applications, Monte Carlo simulations, negative interest rates and risk, and deep learning for financial time series. To provide deeper insights, a synthesis chart and chronological analysis are included to highlight significant trends and contributions. Building upon this foundation, we employ Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) to generate synthetic future interest rate data. These generated projections are then integrated as inputs into the Vasicek model, dynamically adjusting its parameters with the support of AI-driven synthetic data. Additionally, we propose the use of publicly available financial information, gathered via public-facing large language models (LLMs) like ChatGPT, to assess whether the models project trends in line with real-world data. Specifically, we will query ChatGPT to analyze 50 key questions related to interest rates, risk management strategies, inflation, and economic indicators from institutions like the Federal Reserve and banks. Our approach also leverages publicly available information to refine model outputs, reducing reliance on assumptions and emphasizing the alignment of AI-generated noise with observable market behaviors. By integrating these real-world insights, we aim to ensure that our models remain both innovative and grounded in current economic realities. Ultimately, this framework combines advanced generative AI models, such as GANs and VAEs, with human oversight and economic data, providing a robust, adaptable, and forward-thinking approach to financial risk modeling. Keywords: GANs, VAEs, Vasicek, GenAI, Financial Risk Modeling.