Can LSTM outperform volatility-econometric models?

TL;DR

This paper investigates whether LSTM neural networks can outperform traditional econometric models in predicting financial asset volatility, considering the task's inherent complexity and various market factors.

Contribution

It provides a comparative analysis of LSTM models against established econometric volatility models for financial prediction tasks.

Findings

LSTM models face challenges due to market noise and heteroscedasticity.

Econometric models remain competitive in volatility prediction.

The paper highlights the complexity of financial volatility forecasting.

Abstract

Volatility prediction for financial assets is one of the essential questions for understanding financial risks and quadratic price variation. However, although many novel deep learning models were recently proposed, they still have a "hard time" surpassing strong econometric volatility models. Why is this the case? The volatility prediction task is of non-trivial complexity due to noise, market microstructure, heteroscedasticity, exogenous and asymmetric effect of news, and the presence of different time scales, among others. In this paper, we analyze the class of long short-term memory (LSTM) recurrent neural networks for the task of volatility prediction and compare it with strong volatility-econometric models.