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

Cho, Poongjin; Lee, Minhyuk

doi:10.3390/fractalfract6070394

Cited by 9 publications

(2 citation statements)

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“…Tis is also the basis of the FTSP model. In this study, R/S (rescaled range) analysis was used to calculate the Hurst index [44][45][46][47]. Te specifc steps are as follows:…”

Section: Fractal Characteristics Of Time-seriesmentioning

confidence: 99%

Using a Novel Fractal-Time-Series Prediction Model to Predict Coal Consumption

Guo-dang

Guo

Xin

et al. 2023

Discrete Dynamics in Nature and Society

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Currently, coal is still the main energy source for China’s economic development, and the environmental issues caused by coal consumption have aroused widespread concern. Predicting and analyzing coal consumption will help to formulate a higher level of energy consumption planning and economic development strategy. The main purpose of this study is to develop a new fractal time-series prediction (FTSP) model to predict coal consumption, providing reference for formulating effective energy consumption planning and economic development strategies. Hurst index is an important indicator of whether time-series data have long memory and fractal characteristics. This paper uses the rescaled range (R/S) method to calculate Hurst index of the coal consumption per-10000-Yuan-GDP (CC/GDP). The Hurst index is 0.8025, exceeding 0.5, indicating that the time-series data CC/GDP has a long memory and fractal characteristic. Based on this, this paper uses the FTSP model to predict China’s coal consumption (CC/GDP). The prediction results show that the FTSP model accurately predicted China’s coal consumption with a relative error rate (RER) of less than 5% and a mean absolute percentage error (MAPE) of 6.62%. The model predicts a decrease in China’s coal consumption from 0.25 tons of standard coal in 2021 to 0.11 tons of standard coal in 2030, a decrease of 44%. To sum up, the FTSP model provides a new and accurate way to predict coal consumption, and the results suggest that China’s coal consumption is sustainable and will decrease significantly in the coming years. This study has important implications for energy consumption planning and economic development strategies.

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“…Tis is also the basis of the FTSP model. In this study, R/S (rescaled range) analysis was used to calculate the Hurst index [44][45][46][47]. Te specifc steps are as follows:…”

Section: Fractal Characteristics Of Time-seriesmentioning

confidence: 99%

Using a Novel Fractal-Time-Series Prediction Model to Predict Coal Consumption

Guo-dang

Guo

Xin

et al. 2023

Discrete Dynamics in Nature and Society

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“…Mensi et al [37] compared the market efficiency of the European stock market using the MF-DFA approach, where Greece was the most inefficient market. The asymmetric multifractal detrended fluctuation analysis (A-MFDFA) model considers the asymmetric nature of the market efficiency, which is a measure used to better understand the market Cho and Lee [38], Mensi et al [39] measured the market efficiency of the metals' futures markets during financial and oil crises through the A-MFDFA. Zhuang and Wei [40] ranked the informational inefficiency levels of green finance markets using A-MFDFA.…”

Section: Literature Reviewmentioning

confidence: 99%

Global Collective Dynamics of Financial Market Efficiency Using Attention Entropy with Hierarchical Clustering

Cho

Kim

2022

Fractal Fract

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The efficient market hypothesis (EMH) assumes that all available information in an efficient financial market is ideally fully reflected in the price of an asset. However, whether the reality that asset prices are not informational efficient is an opportunity for profit or a systemic risk of the financial system that needs to be corrected is still a ubiquitous concept, so many economic participants and research scholars have conducted related studies in order to understand the phenomenon of the financial market. This research employed attention entropy of the log-returns of 27 global assets to analyze the time-varying informational efficiency. International markets could be classified hierarchically into groups with similar long-term efficiency trends; however, at the same time, the ranks and clusters were found to remain stable only for a short period of time in terms of short-term efficiency. Therefore, a complex network representation analysis was performed to express whether the short-term efficiency patterns have interacted with each other over time as a coherent picture. It was confirmed that the network of 27 international markets was fully connected, strongly globalized and entangled. In addition, the complex network was composed of two modular structures grouped together with similar efficiency dynamics. As a result, although the informational efficiency of financial markets may be globalized to a high-efficiency state, it shows a collective dynamics pattern in which the global system may fall into risk due to the spread of systemic risk.

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