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

• Published in: Fractal and fractional Vol. 6; no. 7; p. 394
• Main Authors: Cho, Poongjin, Lee, Minhyuk
• Format: Journal Article
• Language: English
• 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

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...