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Machine Learning for Predicting Stock Return Volatility

Damir Filipović and Amir Khalilzadeh
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Damir Filipović: Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute
Amir Khalilzadeh: Ecole Polytechnique Fédérale de Lausanne

No 21-95, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: We use machine learning methods to predict stock return volatility. Our out-of-sample prediction of realised volatility for a large cross-section of US stocks over the sample period from 1992 to 2016 is on average 44.1% against the actual realised volatility of 43.8% with an R2 being as high as double the ones reported in the literature. We further show that machine learning methods can capture the stylized facts about volatility without relying on any assumption about the distribution of stock returns. Finally, we show that our long short-term memory model outperforms other models by properly carrying information from the past predictor values.

Keywords: Volatility Prediction; Volatility Clustering; LSTM; Neural Networks; Regression Trees. (search for similar items in EconPapers)
JEL-codes: C51 C52 C53 C58 G17 (search for similar items in EconPapers)
Pages: 61 pages
Date: 2021-12
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cwa, nep-ets, nep-fmk, nep-for, nep-ore and nep-rmg
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