Abstract
This paper examines the impact of public news sentiment on the volatility states of firm-level returns on the Japanese Stock market. We firstly adopt a novel Markov Regime Switching Long Memory GARCH (MRS-LMGARCH), which is employed to estimate the latent volatility states of intraday stock return. By using the RavenPack Dow Jones News Analytics database, we fit discrete choice models to investigate the impact of news sentiment on changes of volatility states of the constituent stocks in the TOPIX Core 30 Index. Our findings suggest that news occurrence and sentiment, especially those of macro-economic news, are a key factor that significantly drives the volatility state of Japanese stock returns. This provides essential information for traders of the Japanese stock market to optimize their trading strategies and risk management plans to combat volatility.
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