A0891
Title: DeepVol: A pre-trained universal asset volatility model
Authors: Chao Wang - The University of Sydney (Australia) [presenting]
Minh-Ngoc Tran - University of Sydney (Australia)
Richard Gerlach - University of Sydney (Australia)
Robert Kohn - University of New South Wales (Australia)
Abstract: DeepVol is a pre-trained deep-learning volatility model is introduced, which is more general than traditional econometric models. DeepVol leverages the power of transfer learning to effectively capture and model the volatility dynamics of all financial assets, including previously unseen ones, using a single universal model. This contrasts to the usual practice in the econometrics literature, which trains a separate model for each asset. The introduction of DeepVol opens up new avenues for volatility modeling in the finance industry, potentially transforming the way volatility is predicted.
