Baidu News and the return volatility of Chinese commodity futures: evidence for the sequential information arrival hypothesis

Abstract

Abstract This study uses Baidu News data and introduces a novel proxy for the rate of information flow to examine its relationship with return volatility in Chinese commodity futures and to test two competing hypotheses. We examine the contemporaneous relationships using correlation coefficient analysis, and find apparent differences between the information flow-return volatility relationship and the information flow-trading volume relationship. The empirical evidence contradicts the mixture of distribution hypothesis (MDH) and suggests that the rate of information flow distinctly affects trading volume and volatility. We conducted linear and nonlinear Granger causality tests to explore the sequential information arrival hypothesis (SIAH). The empirical results prove that a lead-lag linear and nonlinear causality exists between the information flow and return volatility of commodity futures, which is consistent with SIAH. In other words, a partial equilibrium exists before reaching the ultimate equilibrium when the new information arrives in the market. Finally, these findings are robust to alternative measurement of return volatility and subperiod analysis. Our findings reject the MDH and support the SIAH in the context of Chinese commodity futures.

Keywords

• Baidu News
• Chinese commodity futures
• Return volatility
• Sequential information arrival hypothesis
• Mixture of distribution hypothesis