Empirical Analysis of Constructing GARCH Model to Predict Stock Prices with Trading Volume

Yifan Li ()
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Yifan Li: Beijing Normal University-Hong Kong Baptist University United International College (UIC)

A chapter in Proceedings of the 8th International Conference on Financial Innovation and Economic Development (ICFIED 2023), 2023, pp 589-602 from Springer

Abstract: Abstract The immature stock capital market exhibits a kind of instability and immaturity, which can cause strong volatility in the Chinese stock market. Under such background conditions, how to describe as well as predict the price of the Chinese stock market has become a popular topic of concern for scholars in the financial sector. GARCH family model is a more popular model for studying financial time series in recent years, and with the development of academic research, more scholars have tried to incorporate external influence factors into the model to form an improved GARCH model to improve the fitting and forecasting ability of the GARCH model. Inspired by the above research, this paper will analyse the factors affecting stock price volatility and conduct an empirical study on stock prices and trading volumes in the Chinese stock market. Using daily data on the Shanghai Composite index and trading volumes in the Chinese stock market from 2 January 2020 to 1 December 2022, this paper selects elements that fit the daily data on stock prices to construct a GARCH family model. The GARCH (1,1), TGARCH, and EGARCH models with volume factors are used to estimate, analyse and forecast each time series. The final results show that the best fit and forecast results are obtained for the SSE index return series based on the EGARCH model with the introduction of volume factors.

Keywords: empirical analysis; GARCH models; Chinese stock index; prediction; time series; trading volume; stock price; leverage effect; data model diagnostic analysis; hypothesis testing (search for similar items in EconPapers)
Date: 2023
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DOI: 10.2991/978-94-6463-142-5_65

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