Forecasting the Volatility of the Stock Index with Deep Learning Using Asymmetric Hurst Exponents

The prediction of the stock price index is a challenge even with advanced deep-learning technology. As a result, the analysis of volatility, which has been widely studied in traditional finance, has attracted attention among researchers. This paper presents a new forecasting model that combines asym...

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Bibliographic Details
Published inFractal and fractional Vol. 6; no. 7; p. 394
Main Authors Cho, Poongjin, Lee, Minhyuk
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.07.2022
Subjects
Algorithms
Asymmetry
Deep learning
Economic forecasting
Exponents
Forecasting
Foreign exchange rates
Hurst exponent
Machine learning
Mathematical models
Methods
multifractal
Neural networks
Prices
Recurrent neural networks
Securities markets
Stock exchanges
Trends
Volatility
United States > US
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Summary:The prediction of the stock price index is a challenge even with advanced deep-learning technology. As a result, the analysis of volatility, which has been widely studied in traditional finance, has attracted attention among researchers. This paper presents a new forecasting model that combines asymmetric fractality and deep-learning algorithms to predict a one-day-ahead absolute return series, the proxy index of stock price volatility. Asymmetric Hurst exponents are measured to capture the asymmetric long-range dependence behavior of the S&P500 index, and recurrent neural network groups are applied. The results show that the asymmetric Hurst exponents have predictive power for one-day-ahead absolute return and are more effective in volatile market conditions. In addition, we propose a new two-stage forecasting model that predicts volatility according to the magnitude of volatility. This new model shows the best forecasting performance regardless of volatility.
ISSN:2504-3110
2504-3110
DOI:10.3390/fractalfract6070394