Forecasting VIX with Hurst Exponent

Bianchi, Sergio; Sciorio, Fabrizio Di; Mattera, Raffaele

doi:10.1007/978-3-030-99638-3_15

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…Therefore, there have been many efforts to interpret stock price movement through multifractality [23][24][25][26][27][28]. The application of the pattern is used not only to predict stock price movement, but also to forecast VIX [29], foreign exchange rates [30], and oil price [31]. The authors in Garcin [30] showed that the forecast of foreign exchange rates with time-varying Hurst exponents estimation is effective when the Hurst exponent is over 0.5.…”

Section: Introductionmentioning

confidence: 99%

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

Cho

Lee

2022

Fractal Fract

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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.

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