Beyond Traditional Models: Assessing the Role of LSTM Networks in Volatility Prediction
via ZoomSpeaker: 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 […]