Analysis of Volatility Characteristics in Financial Markets: Evidence from the CSI 300 Index

Chiyue Wang

doi:10.54254/2754-1169/2025.BL29525

Advances in Economics, Management and Political SciencesOpen access

Analysis of Volatility Characteristics in Financial Markets: Evidence from the CSI 300 Index

Research Article

Open Access

Analysis of Volatility Characteristics in Financial Markets: Evidence from the CSI 300 Index

Chiyue Wang ^1*

¹ Southwestern University of Finance and Economics

^*Corresponding author: wangchiyue169@gmail.com

Published on 11 November 2025

AEMPS Vol.239

ISSN (Print): 2754-1177

ISSN (Online): 2754-1169

ISBN (Print): 978-1-80590-525-7

ISBN (Online): 978-1-80590-526-4

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Abstract

This paper conducts a systematic review around the characteristics, measurement methods and forecasting models of financial market volatility, and conducts an empirical verification with the sample data of the Shanghai and Shenzhen 300 Index from 2020 to 2024. First of all, at the theoretical and literature levels, this paper combs several stylistic facts of volatility, including the characteristics of fat tail and peak of return distribution, volatility aggregation, leverage effect and asymmetric response, as well as long memory and roughness, and reviews the latest progress of relevant domestic and foreign research. Secondly, at the methodological level, this paper compares the unconditional measurement model represented by historical volatility, the conditional heteroscedasticity model represented by the GARCH family, the HAR-RS model based on high-frequency interval volatility, and the rough volatility model emerging in recent years, and discusses their theoretical basis, applicable scenarios, advantages and disadvantages respectively. Finally, in the empirical analysis, this paper compares the rolling forecasts based on the daily data of the Shanghai and Shenzhen 300 Index. The results show that GARCH (1, 1) - t and HAR-RS models are competitive in short-term forecasting, while RSV Lite has more advantages in overall forecasting efficiency; However, in the Value-at-Risk (VaR) backtest, all models have the problem of tail risk underestimation under extreme market scenarios. The results of this study provide important model selection basis and decision-making reference for financial institutions to carry out risk management and portfolio optimization.

Keywords:

Financial Market, Volatility, GARCH Model, HAR Model, Risk Measurement

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