Published February 28, 2024 | Version CC-BY-NC-ND 4.0
Journal article Open

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

  • 1. Senior Software Engineering, Microsoft, Northlake, Texas, USA.


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.



Files (817.5 kB)

Name Size Download all
817.5 kB Preview Download

Additional details



Manuscript received on 09 January 2024 | Revised Manuscript received on 18 January 2024 | Manuscript Accepted on 15 February 2024 | Manuscript published on 28 February 2024.


  • Yoon, J., Jarrett, D., van der Schaar, M.: Time-series Generative Adversarial Networks. Advances in Neural Information Processing Systems. 32, (2019).
  • Kashyap, G.S., Malik, K., Wazir, S., Khan, R.: Using Machine Learning to Quantify the Multimedia Risk Due to Fuzzing. Multimedia Tools and Applications. 81, 36685–36698 (2022).
  • Marwah, N., Singh, V.K., Kashyap, G.S., Wazir, S.: An analysis of the robustness of UAV agriculture field coverage using multi-agent reinforcement learning. International Journal of Information Technology (Singapore). 15, 2317–2327 (2023).
  • Wazir, S., Kashyap, G.S., Malik, K., Brownlee, A.E.I.: Predicting the Infection Level of COVID-19 Virus Using Normal DistributionBased Approximation Model and PSO. Presented at the (2023).
  • Kanojia, M., Kamani, P., Kashyap, G.S., Naz, S., Wazir, S., Chauhan, A.: Alternative Agriculture Land-Use Transformation Pathways by Partial-Equilibrium Agricultural Sector Model: A Mathematical Approach. (2023).
  • Habib, H., Kashyap, G.S., Tabassum, N., Nafis, T.: Stock Price Prediction Using Artificial Intelligence Based on LSTM– Deep Learning Model. In: Artificial Intelligence & Blockchain in Cyber Physical Systems: Technologies & Applications. pp. 93–99. CRC Press (2023).
  • Kashyap, G.S., Mahajan, D., Phukan, O.C., Kumar, A., Brownlee, A.E.I., Gao, J.: From Simulations to Reality: Enhancing Multi-Robot Exploration for Urban Search and Rescue. (2023).
  • Kashyap, G.S., Brownlee, A.E.I., Phukan, O.C., Malik, K., Wazir, S.: Roulette-Wheel Selection-Based PSO Algorithm for Solving the Vehicle Routing Problem with Time Windows. (2023).
  • Wazir, S., Kashyap, G.S., Saxena, P.: MLOps: A Review. (2023).
  • Vasicek, O.: An equilibrium characterization of the term structure. Journal of Financial Economics. 5, 177–188 (1977).
  • Hull, J., White, A.: Pricing Interest-Rate-Derivative Securities. Review of Financial Studies. 3, 573–592 (1990).
  • Cox, J.C., Ingersoll, J.E., Ross, S.A.: An Intertemporal General Equilibrium Model of Asset Prices. Econometrica. 53, 363 (1985).
  • LONGSTAFF, F.A., SCHWARTZ, E.S.: Interest Rate Volatility and the Term Structure: A Two‐Factor General Equilibrium Model. The Journal of Finance. 47, 1259–1282 (1992).
  • Duffee, G.R., Stanton, R.H.: Estimation of Dynamic Term Structure Models. Quarterly Journal of Finance. 2, (2012).
  • Ang, A., Piazzesi, M.: A no-arbitrage vector autoregression of term structure dynamics with macroeconomic and latent variables. Journal of Monetary Economics. 50, 745–787 (2003).
  • Taylor, J.B.: Discretion versus policy rules in practice. CarnegieRochester Confer. Series on Public Policy. 39, 195–214 (1993).
  • Fleming, M.J., Remolona, E.M.: Price formation and liquidity in the U.S. treasury market: The response to public information,, (1999).
  • Goyenko, R.Y., Ukhov, A.D.: Stock and bond market liquidity: A long-run empirical analysis. Journal of Financial and Quantitative Analysis. 44, 189–212 (2009).
  • Beber, A., Brandt, M.W., Kavajecz, K.A.: Flight-to-quality or flightto-liquidity? Evidence from the euro-area bond market. Review of Financial Studies. 22, 925–957 (2009).
  • Bühler, W., Trapp, M.: Credit and Liquidity Risk in Bond and CDS Markets. SSRN Electronic Journal. (2011).
  • Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., WardeFarley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems. pp. 2672–2680 (2014).
  • Ballard, D.H.: Modular learning in neural networks. Proceedings of the sixth National conference on Artificial intelligence - Volume 2. 838 (1987).
  • Hochreiter, S., Computation, J.S.-N., 1997, U.: Long short-term memory. Neural computation. 9, 1735–1780 (1997).
  • Wang, X., Smith, K., Hyndman, R.: Characteristic-based clustering for time series data. Data Mining and Knowledge Discovery. 13, 335–364 (2006). X/FIGURES/11.
  • Hyndman, R.J., Wang, E., Laptev, N.: Large-Scale Unusual Time Series Detection. In: Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015. pp. 1616– 1619. Institute of Electrical and Electronics Engineers Inc. (2016).
  • Borji, A.: Pros and cons of GAN evaluation measures. Computer Vision and Image Understanding. 179, 41–65 (2019).
  • Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: Advances in Neural Information Processing Systems. pp. 2951–2959 (2012).
  • Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., Abbeel, P.: InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. Advances in Neural Information Processing Systems. 29, (2016).
  • Pham, C.H., Ladjal, S., Newson, A.: PCA-AE: Principal Component Analysis Autoencoder for Organising the Latent Space of Generative Networks. Journal of Mathematical Imaging and Vision. 64, 569– 585 (2022). Z/FIGURES/9.
  • 31 Macroeconomic Factors as a Predictor of Stock Market: Empirical Evidences from India, U.S. and U.K. (2019). In International Journal of Recent Technology and Engineering (Vol. 8, Issue 2S10, pp. 743–751).
  • 32 Ranamagar, U. B., & Upadhyaya, N. R. (2022). Remittances and Economic Growth: A Causality Analysis for Nepal. In Indian Journal of Economics and Finance (Vol. 2, Issue 2, pp. 25–33). .
  • 33 Rajeev, H., & Chakkravarthy, Dr. M. (2023). Detection of Malware using Phishing Alarm. In Indian Journal of Artificial Intelligence and Neural Networking (Vol. 3, Issue 4, pp. 1–4).