An Empirical Study on the Volatility of Public Opinion on Coal Mine Safety Accidents

Songhua LI

Abstract


This paper empirically studies the volatility of public opinion evolution on coal mine safety accidents based on weekly average data of coal mine accidents from January 2011 to May 2014 in Baidu search index. The findings are as follows: The volatility of public opinion evolution of coal mine safety accidents shows some characteristics such as aggregation, ARCH effect. And, the estimation of a GARCH model shows that public opinion evolution of coal mine safety accidents has conditional heteroscedasticity character, and this GARCH model successfully portrays the volatility of the public opinion on coal mine safety accidents.


Keywords


Coal mine safety; Public opinion; Volatility; GARCH model

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References


Basel, M. A., & Valentina Corradi, V. (2005). Predicting the volatility of the S&P -500 stock index via GARCH Models: The role of asymmetries. International Journal of Forecasting, (1), 167-183.

Chen, Q. L., & Shao, F. C. (2002). Study on the returns volatility of Shanghai stock index. The Journal of Quantitative & Technical Economics, (6), 122-125.

Gu, F. J., & Cen, Z. D. (2011). Study on the volatility of Chinese Shanghai and Shenzhen stock markets with GARCH and SV Models. Journal of Mathematics in Practice and Theory, (1), 4-22.

Liu, H.-C., & Hung, J.-C. (2010). Forecasting S&P-100 stock index volatility: The role of volatility asymmetry and distributional assumption in GARCH Models. Expert Systems with Applications, (7), 4928-4934.

Liu, J. Q., & Chang, C. (2002). An empirical study on the returns and volatility of Chinese Shanghai and Shenzhen stock markets. The Quarterly Journal of Economics, (1), 885-898.

Sabiruzza-Man, M. D., et al. (2010). Modeling and forecasting trading volume index: GARCH versus TGARCH approach. The Quarterly Review of Economics and Finance, (2), 141-145.

Yang, C. H., & Liu, X. M. (2012). Study on the Influence of Macroeconomic informations on the volatility of Shanghai and Shenzhen 300 Index. Journal of Shanxi University of Finance and Economics, (3), 38-44.

Yue, C. L. (2002). An empirical study on the Shanghai Stock market yields with GARCH models. The Journal of Quantitative & Technical Economics, (4), 56-59.

Zakoian, J. M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, (18), 931-955.

Zhang, J. L., & He, Q. G. (2012). Study on the time-varying linkage and volatility spillover of China’s gem and main board markets. Journal of Zhongnan University of Economics and Law, (2), 100-106.




DOI: http://dx.doi.org/10.3968/n

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