Evaluating Financial Risk in the Transition from EONIA to ESTER: A TimeGAN Approach with Enhanced VaR Estimations

Abstract

Abstract This study investigates the evaluation of multivariate time series data using a Generative Adversarial Network (GAN). Calculating the Value at Risk (VaR) for the Euro Overnight Index Average (EONIA) over different time periods and evaluating the financial risk consequences of the EONIA to Euro Short-Term Rate (ESTER) transition are the main objectives. Through the use of a particular GAN called TimeGAN, which focuses on macro-finance temporal and latent representation, the study aims to predict short-rate risk for EONIA. When estimating lower VaR and the 1-day higher VaR for EONIA, the TimeGAN model performs poorly. However, it performs well when estimating upper VaR for 10-day and 20-day periods. The variation of TimeGAN with PLS+FM, which uses Positive Label Smoothing and Feature Matching shows the upper and lower VaR for EONIA over 10 and 20-day periods are excellently estimated by this enhanced model. Simulations for the 20-day EONIA show less variation between TimeGAN variations than a one-factor Vasicek model, even with the proper VaR estimations. This study evaluates the proposed transition mapping from ESTER to EONIA by the European Central Bank (ECB), calculating an ESTER+8.5bps shift with the TimeGAN with PLS+FM. The results do not refute the validity of the ECB's proposed EONIA-ESTER mapping. Additionally, the TimeGAN with PLS+FM accurately predicts VaR for 10 and 20-day periods for ESTER using the EONIA-ESTER mapping. Whereas the one-factor Vasicek model finds it difficult to estimate higher VaR for ESTER over the same time frames.