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Extreme Value Theory with High-Frequency Financial Data

By Abhinay Sawant

Extreme Value Theory (EVT) is one of the most commonly applied models in financial risk management for estimating the Value at Risk of a portfolio. However, the EVT model is practical for estimation only when data is independent and identically distributed, which usually does not characterize financial returns data. This paper aims to modify this model by using high-frequency data to standardize financial returns by their realized volatility and then tests the modified model with recent equity data. The results from the paper show an improvement in the EVT model when forward volatility can be properly forecasted.

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Advisor: George Tauchen


Undergraduate Program Assistant
Jennifer Becker

Director of the Honors Program
Michelle P. Connolly