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Beyond Traditional Models: Assessing the Role of LSTM Networks in Volatility Prediction

October 10 @ 12:00 pm - 1:00 pm CDT

Speaker: Prof. Massimo Guidolin, Baffi Carefin Center, Bocconi Univ., Milan

Abstract: 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.

Details

Date:
October 10
Time:
12:00 pm - 1:00 pm CDT
Event Category:
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