Volatility Forecasting of the CSI 300 Index Based on the ARIMA-GARCH Model

DOI

10.2991/978-94-6239-801-6_48How to use a DOI?

Keywords

Volatility Prediction; ARIMA Model; GARCH Model

Abstract

The price volatility of financial assets features rising complexity and strengthened nonlinearity, and accurate index volatility prediction carries growing importance for investors. This paper takes the CSI 300 Index as the research object and constructs an ARIMA-GARCH combined prediction model. The research determines the model order based on ACF/PACF graphs and residual white noise tests, constructs an ARIMA model to capture the dynamic characteristics of returns, and inputs the residual series into a GARCH model to characterize the evolution of conditional variance. Empirical results show the hybrid model has stronger in-sample explanatory power than the simple moving average model. In out-of-sample prediction, the model yields an RMSE of 0.0068, an 8.3% improvement over the benchmark model. The prediction-based trading strategy yields a 12.5% cumulative return and a 0.76 Sharpe ratio, significantly outperforming the 0.53 market benchmark and reflecting sound risk management. The verification inside and outside the sample confirms the applicability of the ARIMA-GARCH model in financial time series prediction, provides a reliable quantitative tool for index volatility prediction, and also provides theoretical support and practical reference for investors’ risk control and asset allocation decisions.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Tingcong Zhou
PY  - 2026
DA  - 2026/10/23
TI  - Volatility Forecasting of the CSI 300 Index Based on the ARIMA-GARCH Model
BT  - Proceedings of the 2026 2nd International Conference on Financial Innovation and Marketing Management (FIMM 2026)
PB  - Atlantis Press
SP  - 442
EP  - 450
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-801-6_48
DO  - 10.2991/978-94-6239-801-6_48
ID  - Zhou2026
ER  -