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X-WR-CALNAME:Mathematical Finance
X-ORIGINAL-URL:https://www.math.ttu.edu/mathematicalfinance
X-WR-CALDESC:Events for Mathematical Finance
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DTSTART:20250309T080000
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DTSTART:20251102T070000
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DTSTART;TZID=America/Chicago:20251010T120000
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DTSTAMP:20260520T164801
CREATED:20250701T183111Z
LAST-MODIFIED:20250707T150143Z
UID:2015-1760097600-1760101200@www.math.ttu.edu
SUMMARY:Beyond Traditional Models: Assessing the Role of LSTM Networks in Volatility Prediction
DESCRIPTION:Speaker: Prof. Massimo Guidolin\, Baffi Carefin Center\, Bocconi Univ.\, Milan \nAbstract: This paper examines the out-of-sample accuracy of recurrent artificial neural networks (ANNs) compared to traditional econometric models for the prediction of realized volatility. We focus on a horserace between the heterogeneous autoregressive (HAR) model\, its Markov-switching extension (MS-HAR)\, multi-layer perceptrons (MLP)\, and long short-term memory (LSTM) networks across 31 international equity indices. Using high-frequency realized volatility data\, we evaluate predictive performance based on four loss functions and test for equal accuracy using Diebold-Mariano tests. Our results suggest that the HAR and MS-HAR models often deliver the most accurate forecasts\, outperforming LSTMs. However\, differences in out-of-sample accuracy between LSTM\, HAR\, and MS-HAR models are not always statistically significant. We conclude that ANNs do not consistently outperform HAR-based models in equity volatility forecasting.
URL:https://www.math.ttu.edu/mathematicalfinance/event/seminar-date-reserved/
LOCATION:via Zoom
CATEGORIES:Fall 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2023/07/Guidolin-e1751900326458.jpg
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