Five pieces of theoretical prediction on the generation of higher order moment risk are refined and thus the corresponding hypotheses are formed after reviewing the existed literature. Employing the idea of modeling time variant higher-order-moment, these five pieces of hypothesis are involved in a unified econometric framework. An empirical analysis is conducted based on such model to find some dominated theoretical explanations. Sampling daily returns from Shanghai and Shenzhen stock market composite index, the results show that there are two hypotheses relevant to volatility feedback effect and bad news revelation effect are significantly supported but the other three are not. Further analysis indicates that these two effects can generate both skewness and kurtosis risk. Therefore, they are implied as the main generation mechanism of higher-order-moment risk. These results get out of the mess of opinions on the generation mechanism of higher-order-moment risk and thus can benefit further exploration of such topic under a unified theoretical framework.
FANG Li-bing, ZENG Yong
. Testing the Generation Mechanism of Higher-Order-Moment Risk in Stock Market Returns[J]. Chinese Journal of Management Science, 2016
, 24(4)
: 27
-36
.
DOI: 10.16381/j.cnki.issn1003-207x.2016.04.004
[1] Pindyck R S. Risk, inflation, and the stock market[J]. American Economic Review, 1984, 74(3): 335-351.
[2] French K R, Schwert G W, Stambaugh R F. Expected stock returns and volatility[J]. Journal of Financial Economics, 1987, 19(1): 3-29.
[3] Campbell J Y, Hentschel L. No news is good news : An asymmetric model of changing volatility in stock returns[J]. Journal of Financial Economics, 1992, 31(3): 281-318.
[4] Hong H, Stein J C. Differences ofopinion, short-sales constraints, and market crashes [J]. Review of Financial Studies, 2003, 16(2): 487-525.
[5] Xu Jianguo. Price convexity and skewness[J]. Journal of Finance, 2007, 62(5): 2521-2552.
[6] Engle R F. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation[J]. Econometrica, 1982, 50(4): 987-1007.
[7] Bollerslev T. Generalized autoregressive conditional heteroskedasticity[J]. Journal of Econometrics, 1986, 31(3): 307-327.
[8] Romer D. Rational asset-price movements without news[J]. American Economic Review, 1993, 83(5): 1112-1130.
[9] Abreu D, Brunnermeier M K. Bubbles and crashes[J]. Econometrica, 2003, 71(1): 173-204.
[10] Glosten L R, Jagannathan R, Runkle D E. On the relation between the expected value and the volatility of the nominal excess return on stocks [J]. Journal of Finance, 1993, 48(5): 1779-1801.
[11] 王春. 投资者情绪对股票市场收益和波动的影响-基于开放式股票型基金资金净流入的实证研究. 中国管理科学, 2014, 22(9): 49-56.
[12] Brandt M W, Wang Leping. Measuring the time-varying risk-return relation from the cross-section of equity returns. Working paper,Duke Universty, 2010.
[13] Yu Jianfeng, Yuan Yu. Investor sentiment and the mean-variance relation[J]. Journal of Financial Economics, 2011, 100(2): 367-381.
[14] Engle R, Mistry A. Priced risk and asymmetric volatility in the cross section of skewness[J]. Journal of Econometrics, 2014, 182(1): 135-144.
[15] Kinnunen J. Risk-return trade-off and serial correlation: Do volume and volatility matter?[J]. Journal of Financial Markets, 2014, 20: 1-19.
[16] Hueng C J, McDonald J B. Forecasting asymmetries in aggregate stock market returns:Evidence from conditional skewness[J]. Journal of Empirical Finance, 2005, 12(5): 666-685.
[17] Hueng C J. Short-sales constraints and stock return asymmetry: evidence from the Chinese stock markets[J]. Applied Financial Economics, 2006, 16(10): 707-716.
[18] 陈国进, 张贻军. 异质信念、卖空限制与我国股市的暴跌现象研究[J]. 金融研究, 2009, (4): 80-91.
[19] Hansen B E. Autoregressive conditional density estimation[J]. International Economic Review, 1994, 35(3): 705-730.
[20] Chen J, Hong H, Stein J C. Forecasting crashes: Trading volume, past returns, and conditional skewness in stock prices[J]. Journal of Financial Economics, 2001, 31(3): 345-381.
[21] Boehmer E, Wu Juan. Short selling and the price discovery process[J]. Review of Financial Studies, 2013, 26(2): 287-322.
