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Lu, Xunfa; Lai, Kin Keung; Liang, Liang

Dependence between stock returns and investor sentiment in Chinese markets: a copula approach. (English) Zbl 1256.93099

J. Syst. Sci. Complex. 25, No. 3, 529-548 (2012).
Summary: Using data of newly opened stock trading accounts in China as a proxy of investor sentiment index, the authors employ the time-varying copula-GARCH model with Hansen’s skewed Student-t innovations to investigate the dynamic dependence between investor sentiment and stock returns. The empirical findings show that shifts in investor sentiment are asymptotically positively correlated to stock returns in extreme value situations in both A shares market and B shares market in China, that is to say, stock prices will increase (decrease) more when investors become more bullish (bearish). Also, results show that the dependence between investor sentiment and stock returns is time-varying, which means that the traditional Pearson’s correlation based on normal distribution is not enough to describe the relationship between stock market behavior and investor behavior.

MSC:

93E03 Stochastic systems in control theory (general)
91G10 Portfolio theory

Keywords:

behavioral finance; copula; GARCH; investor sentiment; newly opened stock trading accounts; traditional Pearsons correlation; Hansens skewed Student-t; stock market behavior; empirical findings; copula approach; investor behavior; stock prices

Software:

QRM
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Full Text: DOI

References:

[1] J. Treynor, Toward a theory of market value of risky assets. Unpublished manuscript. A final version was published in 1999, in Asset Pricing and Portfolio Performance: Models, Strategy and Performance Metrics, Robert A. Korajczyk (editor) London: Risk Books, 1962.
[2] W. Sharpe, Capital asset prices: A theory of market equilibrium under conditions of risk, Journal of Finance, 1964, 19(3): 425–442.
[3] J. Lintner, The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets, The Review of Economics and Statistics, 1965, 47(1): 13–37. · doi:10.2307/1924119
[4] J. Mossin, Equilibrium in a capital asset market, Econometrica, 1966, 34(4): 768–783. · doi:10.2307/1910098
[5] S. A. Ross, The arbitrage theory of capital asset pricing, Journal of Economic Theory, 1976, 13(3): 341–360. · doi:10.1016/0022-0531(76)90046-6
[6] E. Fama, Efficient capital markets: A review of theory and empirical work, Journal of Finance, 1970, 25(2): 383–417. · doi:10.2307/2325486
[7] D. Kahneman and A. Tversky, Prospect theory: An analysis of decision under risk, Econometrica, 1979, 47(2): 263–291. · Zbl 0411.90012 · doi:10.2307/1914185
[8] F. Black, Noise, Journal of Finance, 1986, 41(3): 529–543. · doi:10.1111/j.1540-6261.1986.tb04513.x
[9] J. De Long, A. Shleifer, L. G. Summers, and R. Waldmann, Noise trader risk in financial markets, Journal of Political Economy, 1990, 98(4): 703–738. · doi:10.1086/261703
[10] N. Barberis, A. Shleifer, and R. Vishny, A model of investor sentiment, Journal of Financial Economics, 1998, 49(3): 307–343. · doi:10.1016/S0304-405X(98)00027-0
[11] K. Daniel, D. Hirshleifer and A. Subrahmanyam, Investor psychology and security market underand overreactions, Journal of Finance, 1998, 53(6): 1839–1885. · doi:10.1111/0022-1082.00077
[12] H. Hong and J. Stein, A unified theory of underreaction, momentum trading, and overreaction in asset markets, Journal of Finance, 1999, 54(6): 2143–2184. · doi:10.1111/0022-1082.00184
[13] G. W. Brown and M. T. Cliff, Investor sentiment and the near-term stock market, Journal of Empirical Finance, 2004, 11(1): 1–27. · doi:10.1016/j.jempfin.2002.12.001
[14] P. Embrechts, A. McNeil, and D. Straumann, Correlation and dependency in risk management: Properties and pitfalls, Working Paper, 1999.
[15] U. Cherubini, E. Luciano and W. Vecchiato, Copula Methods in Finance, John Wiley & Sons, England, 2004. · Zbl 1163.62081
[16] A. Patton, Modelling asymmetric exchange rate dependence, International Economic Review, 2006, 47(2): 527–556. · doi:10.1111/j.1468-2354.2006.00387.x
[17] E. Jondeau and M. Rockinger, The copula-GARCH model for conditional dependencies: An international stock market application, Journal of International Money and Finance, 2006, 25(5): 827–853. · doi:10.1016/j.jimonfin.2006.04.007
[18] D. Fantazzini, Dynamic copula modeling for value at risk, Frontiers in Finance and Economics, 2008, 5(2): 72–108.
[19] J. Hu, Dependence structures in Chinese and US financial markets: A time-varying conditional copula approach, Applied Financial Economics, 2010, 20(7): 561–583. · doi:10.1080/09603100903459865
[20] A. McNeil, R. Frey, and P. Embrechts, Quantitative Risk Management: Concepts, Techniques and Tools, Princeton University Press, New Jersey, 2005. · Zbl 1089.91037
[21] J. Rank, Copulas: from Theory to Applications in Finance, Risk Books, London, 2006.
[22] C. Alexander, Market Risk Analysis, Wiley & Sons, London, 2008.
[23] T. Ane, L. Ureche-Rangau, and C. Labidi-Makni, Time-varying conditional dependence in Chinese stock markets, Applied Financial Economics, 2008, 18(11): 895–916. · doi:10.1080/09603100701259772
[24] M. Barker and J. Wurgler, Investor sentiment in stock market, Journal of Economic Perspectives, 2007, 21(2): 129–151. · doi:10.1257/jep.21.2.129
[25] R. G. Clarke and M. Statman, Bullish or Bearish, Financial Analysts Journal, 1998, 54(3): 63–72. · doi:10.2469/faj.v54.n3.2182
[26] K. L. Fisher and M. Statman, Investor sentiment and stock returns, Financial Analysts Journal, 2000, 56(2): 16–23. · doi:10.2469/faj.v56.n2.2340
[27] A. Sklar, Fonctions de repartition n dimensions et leurs marges, Publications de l’Institut de Statistique de l’Universit de Paris, 1959, 8: 229–231.
[28] P. M. Morilas, A method to obtain new copulas from a given one, Metrika, 2005, 61(2): 169–184. · Zbl 1079.62056 · doi:10.1007/s001840400330
[29] K. Aas, C. Czado, A. Frigessi, and H. Bakken, Pair copula constructions of multiple dependence, Insurance, Mathematics, and Economics, 2009, 44(2): 182–198. · Zbl 1165.60009 · doi:10.1016/j.insmatheco.2007.02.001
[30] R. B. Nelsen, An Introduction to Copula, Springer, New York, 1998.
[31] C. Genest and B. Rmillard, Validity of the parametric bootstrap for goodness-of-fit testing in semiparametric models, Annales de l’Institut Henri Poincar, Probabilits et Statistiques, 2008, 44(6): 1096–1127. · Zbl 1206.62044 · doi:10.1214/07-AIHP148
[32] J. H. Shih and T. A. Louis, Inference on the association parameter in copula models for bivariate survival data, Biometrics, 1995, 51(4): 1384–1399. · Zbl 0869.62083 · doi:10.2307/2533269
[33] H. Joe and J. J. Xu, The estimation method of inference functions for margins for multivariate models, Technical Report 166, Department of Statistics, University of British Columbia, 1996.
[34] H. Joe, Multivariate Models and Dependence Concepts, Chapman and Hall, London, 1997. · Zbl 0990.62517
[35] D. B. Nelson, Conditional heteroskedasticity in asset returns: A new approach, Econometrica, 1991 59(2): 347–370. · Zbl 0722.62069 · doi:10.2307/2938260
[36] T. Bollerslev and J. M. Wooldridge, Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances, Econometric Reviews, 1992, 11(2): 143–172. · Zbl 0850.62884 · doi:10.1080/07474939208800229
[37] B. E. Hansen, Autoregressive conditional density estimation, International Economic Review, 1994, 35(3): 705–730. · Zbl 0807.62090 · doi:10.2307/2527081
[38] China Securities Depository and Clearing Corporation Limited, http://www.chinaclear.cn/index.html .
[39] A. Ang and J. Chen, Asymmetric correlations of equity portfolios, Journal of Financial Economics, 2002, 63(3): 443–494. · doi:10.1016/S0304-405X(02)00068-5
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