Open Access Journal of Data Science and Artificial Intelligence (OAJDA)

ISSN: 2996-671X

Research Article

Study on the Efficiency of China's Convertible Bond Market Based on ARMA-GARCH Models

Authors: Bin Zhao* and Yi Wu

DOI: 10.23880/oajda-16000129


This study examines the prediction and analysis of China's convertible bond market using a combined ARMA and Generalized GARCH model. If this model can effectively achieve its predictive goals, it indirectly suggests that China's convertible bond market has not yet reached weak-form efficiency. The study focuses on the Shenzhen Investment Grade Convertible Bond Index, utilizing daily closing price data from January 2, 2019, to June 18, 2024, as the sample, providing a robust empirical foundation for the model. Empirical results indicate that the constructed model effectively captures the volatility patterns of current convertible bond yields, implying that historical price data holds certain predictive significance for current prices. Furthermore, through empirical analysis using the GARCH-M(1, 1) model, we find a relationship between yield volatility in the convertible bond market and historical risk levels. This suggests that market risk may not be fully priced rationally, as historical risk levels can help forecast current yield volatility, further confirming that China's convertible bond market has not yet achieved weak-form efficiency.

Keywords: ARMA-GARCH Model; Forecasting; Convertible Bonds; Weak-form Efficiency

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