2026-09-20T15:11:26Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1513902023-08-01T04:12:22Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Kim, Yoon
Camelo Sa, Lucas
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2023-07-31T19:36:12Z
2023-07-31T19:36:12Z
2023-06
2023-06-06T16:34:36.387Z
https://hdl.handle.net/1721.1/151390
Deep learning has recently risen as a dominant technique in a variety of settings comprising large-scale and high-dimensional data. In the particular case of financial modeling, one of the most important data analysis problems consists of predicting the future volatility of a given asset. In this thesis, we investigate how the Transformer architecture performs at the task of volatility forecasting by comparing its performance against that of previously explored deep learning architectures such as the LSTM.
M.Eng.
Massachusetts Institute of Technology
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https://rightsstatements.org/page/InC-EDU/1.0/
Applications of Deep Learning to Financial Time Series Forecasting
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
unknown
unknown
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