QFRG and DSLab Seminar – Professor Daniel Traian Pele’s Lecure at the Faculty of Economic Sciences

We cordially invite you to attend the lecture as part of the seminar series organized by the QFRG and DSLab Centres. The seminar will focus on the application of large language models (LLMs) in modelling Value at Risk (VaR) and Expected Shortfall (ES) in financial markets.

Professor Daniel Traian Pele (Bucharest University of Economic Studies) will present his study titled „In the Beginning Was the Word: LLM-VaR and LLM-ES”.

The seminar will take place on March 17, 2025 at 17:00 and will be held exclusively online (via the Zoom platform).

Link to the meeting: https://uw-edu-pl.zoom.us/j/99320668932?pwd=hyB11zMyGobDLAlNQddg2DGFUggQIV.1

Please ensure to log in by 16:55 to facilitate the smooth running of the event, and kindly turn off both your camera and microphone. Questions for the speaker can be submitted via the chat.

Please note that joining the meeting implies consent to the recording.

Below, we present the abstract. We encourage you to review it and participate in the seminar.

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This study introduces LLM-VaR and LLM-ES, novel approaches utilizing general-purpose large language models (LLMs) for zero-shot forecasting of Value at Risk (VaR) and Expected Shortfall (ES). Using the LLMTime framework, these methods process financial time series data encoded as numerical strings, providing a flexible, assumption-free alternative to traditional risk estimation models such as GARCH and EWMA. Our empirical analysis reveals that LLMs perform effectively within a short-term historical context, particularly in highly volatile markets like cryptocurrencies. However, as the historical context lengthens, the accuracy of LLM-based methods diminishes, with conventional models proving superior for capturing long-term dependencies. These findings highlight the potential of LLMs as adaptable tools for risk assessment over recent historical windows, while underscoring the continued importance of traditional models for robust, long-term financial risk management.