Can LSTM outperform volatility-econometric models?
Analyzes LSTM neural networks for financial volatility prediction, comparing them with traditional econometric models.
What it examines
This paper investigates whether Long Short-Term Memory (LSTM) recurrent neural networks can outperform traditional econometric models in predicting financial asset volatility. It addresses the complexity of volatility prediction due to factors like noise, market microstructure, and heteroscedasticity.
What it concludes
The research demonstrates that LSTM models, with proper hyperparameter and window size optimization, can significantly improve volatility prediction accuracy. This has potential applications in financial risk management and trading strategies, offering a robust alternative to traditional econometric models.
Evidence objects
The research demonstrates that LSTM models, with proper hyperparameter and window size optimization, can significantly improve volatility prediction accuracy. This has potential applications in financial risk management and trading strategies, offering a robust alternative to traditional econometric models.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
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 mi… ▽ More 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. △ Less
Source row: 360 · abstract type: unknown