Volatility spillovers between investor sentiment and market returns: evidence from the Shanghai stock market

Using daily trading data from the Shanghai stock market from January 2016 to December 2024, this paper extracts a liquidity factor, a leverage factor, and an expectation factor through a dynamic factor model from spot market trading, margin buying and short selling, and derivatives trading to capture multidimensional investor sentiment. Based on the DCC-GARCH framework, this study integrates VIRF with GFEVD to explore the volatility spillovers between investor sentiment and market returns. The results indicate that the overall spillover intensity within the system is significantly time-varying. On average, 25.80% of the forecast error variance of market returns can be explained by shocks from investor sentiment. Sub-sample estimates demonstrate that volatility spillovers from investor sentiment to market returns were primarily caused by the leverage factor from 2018 to 2024, but the expectation factor and the liquidity factor contributed to excessive volatility in market returns during the periods from 2020 to 2022 and from 2023 to 2024, respectively. Market returns also transmitted volatility spillovers back to the expectation factor during the Sino-U.S. trade war. The structure of the volatility spillover networks is event-driven, shaped by the Sino-U.S. trade war, the COVID-19 pandemic, and the stock market reforms.