New practice for investors in Chinese stock market: From perspective of fractionally integrated realized GARCH model

Xiao, Mei; Zai-ping, Tao; Gu, Zhouyi; Li, Zhengxin; Chen, Xihui

doi:10.1016/j.heliyon.2023.e14017

Cited by 1 publication

(2 citation statements)

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“…Zhang et al (2019) constructed a multidimensional economic spatial model by defining economic measures and gravity spatial weight matrix, effectively capturing the complex interactions among financial. Xiao et al (2023) proposed a new fractional integration to implement the GARCH model, aiming to improve the prediction of volatility in financial markets. By constructing weighted realized volatility and incorporating long memory parameters, the model can more accurately capture the long-term correlation of market volatility.…”

Section: Introductionmentioning

confidence: 99%

“…By constructing weighted realized volatility and incorporating long memory parameters, the model can more accurately capture the long-term correlation of market volatility. This provides technical support and risk control strategies for investors and offers a scientific basis for financial market risk management (Xiao et al, 2023). Kou et al (2022) comprehensively reviewed the major advances in network resilience and financial network literature, and discussed key elements and applications of financial network resilience in financial regulation, including financial network information, network resilience measurement, financial regulatory technology, and regulatory applications.…”

Section: Introductionmentioning

confidence: 99%

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Transmission Risk of Stock Price Fluctuations Among Industries in Complex Financial Networks

Wang,

Ding,

Zhou

2024

JAFAS

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In recent years, many researchers have studied some complex phenomena in the world from a new angle, and the subject of complexity science was born. In the field of complexity research, the study of social economics is very common. Because of the characteristics of financial market and its position in social economy, it is very important in complexity research. Based on complex network theory and vector autoregressive model (VAR) study of the stock market fluctuation and discusses the conduction effect between stock volatility in the industry on the influence of the correlation of share price volatility, aims to better understand the operating mechanism of the stock market, the more effective controlling the market development direction, to promote the healthy development of China's financial markets. In this paper, the rise and fall of stocks in 11 industries in the CSI 300 industry index are selected as variables, and the data from January 1, 2024, to March 1, 2024, are selected as research samples. Granger test and vector autoregressive model are used to calculate the conduction benefits between different industries. The complex network is constructed with industry as node and stock conduction direction as edge.Based on the analysis, we can find that in the CSI 300 industry index, there are several industries as the influence center of volatility, and their stock price fluctuations will affect the market of related industries, thus affecting the whole body. Based on the market situation of related industries in recent years, the correlation of fluctuations of different stocks in the stock market can be explained, and the influence of industry factors on stock price fluctuations can be deeply explored.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%