Deep learning in predicting cryptocurrency volatility

D’Amato, Valeria, Susanna Levantesi and Gabriella Piscopo

Physica A: Statistical Mechanics and its Applications, 2022, vol. 596, issue C

Abstract: This paper focuses on the prediction of cryptocurrency volatility. The stock market volatility represents a very influential aspect that affects a wide range of decisions in business and finance. Recently, the volatility spillovers between the cryptocurrency market and other financial markets are detecting. Nevertheless, the cryptocurrency volatility forecasts underperform the market dynamics. This paper develops a suitable model to capture the cryptocurrency volatility dynamics. We base on deep learning techniques, which produce more reliable results than standard methods in finance by capturing complex data interactions. Specifically, we refer to a Jordan Neural Network, which is a parsimonious recurrent neural network showing more predictability power compared to other models designed for time series, the Self Exciting Threshold Autoregressive model models and the Non-Linear Autoregressive Neural Networks. Empirical evidence is provided using data from three different cryptocurrencies, Bitcoin, Ripple, and Ethereum.

Keywords: Deep learning; Neural networks; Cryptocurrency; Volatility (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:596:y:2022:i:c:s0378437122001704

DOI: 10.1016/j.physa.2022.127158

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