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Investors’ Risk Preference Characteristics and Conditional Skewness

DOI: 10.1155/2014/814965

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Perspective on behavioral finance, we take a new look at the characteristics of investors’ risk preference, building the D-GARCH-M model, DR-GARCH-M model, and GARCHC-M model to investigate their changes with states of gain and loss and values of return together with other time-varying characteristics of investors’ risk preference. Based on a full description of risk preference characteristic, we develop a GARCHCS-M model to study its effect on the return skewness. The top ten market value stock composite indexes from Global Stock Exchange in 2012 are adopted to make the empirical analysis. The results show that investors are risk aversion when they gain and risk seeking when they lose, which effectively explains the inconsistent risk-return relationship. Moreover, the degree of risk aversion rises with the increasing gain and that of risk seeking improves with the increasing losses. Meanwhile, we find that investors’ inherent risk preference in most countries displays risk seeking, and their current risk preference is influenced by last period’s risk preference and disturbances. At last, investors’ risk preferences affect the conditional skewness; specifically, their risk aversion makes return skewness reduce, while risk seeking makes the skewness increase. 1. Introduction Risk preference refers to the attitude people hold towards risks, which is a key factor in studies on investors’ decision-making behavior. Standard financial theory assumes that investors are rational and believes that when making investment decisions they tend to have invariant risk preferences-risk averse. However, as the research goes, people gradually find that the investors’ decision-making behavior in real life does not always comply with the assumption of rationality and their behaviors are usually limited by their own cognitive biases and external environment, leading to their risk preferences varying with different situations. With the development of behavioral finance, a multitude of research indicated that the result of investment in the financial market can affect their decisions, making them exhibit inconsistent risk preference. Prospect Theory proposed by Kahneman and Tversky [1] had described some prominent psychological traits of investors in their decision-making under uncertainty. Their experiments suggested that individuals tend to be risk averse with gain and risk seeking with loss, which have been confirmed by a variety of subsequent studies. For example, Laughhunn and Payne [2] found evidence that 20 managers in the process of their multiple risk choice


[1]  D. Kahneman and A. Tversky, “Prospect theory: an analysis of decision under risk,” Econometrica, vol. 47, no. 2, pp. 263–291, 1979.
[2]  D. Laughhunn and J. W. Payne, “The impact of sunk outcomes on risky choice behavior,” Canadian Journal of Operations Research and Information Processing, vol. 22, no. 2, pp. 155–181, 1984.
[3]  B. Fernandes and J. Luiz, “Risk Taking In Financial Markets: A Behavioral Perspective,” 2007.
[4]  M.-S. Haigh and J.-A. List, “Do professional traders exhibit myopic loss aversion? an experimental analysis,” Journal of Finance, vol. 60, no. 1, pp. 523–534, 2005.
[5]  M. Abdellaoui, H. Bleichrodt, and C. Paraschiv, “Loss aversion under prospect theory: a parameter-free measurement,” Management Science, vol. 53, no. 10, pp. 1659–1674, 2007.
[6]  H.-M. Anderson, K. Nam, and F. Vahid, “An asymmetric nonlinear smooth-transition GARCH models,” Nonlinear Time Series Analysis of Economic and Finance Data, vol. 1, pp. 191–207, 1999.
[7]  R.-H. Thaler and E.-J. Johnson, “Gambling with the house money and trying to break even: the effects of prior outcomes on risky choice,” Management Science, vol. 36, no. 6, pp. 643–660, 1990.
[8]  N. Barberis, M. Huang, and T. Santos, “Prospect theory and asset prices,” Quarterly Journal of Economics, vol. 116, no. 1, pp. 1–53, 2001.
[9]  N. Barberis and W. Xiong, “What drives the disposition effect? An analysis of a long-standing preference-based explanation,” Journal of Finance, vol. 64, no. 2, pp. 751–784, 2009.
[10]  M.-K. Brunnermeier and S. Nagel, “Do wealth fluctuations generate time-varying risk aversion? Micro-evidence on individuals' asset allocation,” American Economic Review, vol. 98, no. 3, pp. 713–736, 2008.
[11]  R. Chou, R.-F. Engle, and A. Kane, “Measuring risk aversion from excess returns on a stock index,” Journal of Econometrics, vol. 52, no. 1-2, pp. 201–224, 1992.
[12]  G. Li, “Time-varying risk aversion and asset prices,” Journal of Banking and Finance, vol. 31, no. 1, pp. 243–257, 2007.
[13]  J. Cotter and J. Hanly, “Time-varying risk aversion: an application to energy hedging,” Energy Economics, vol. 32, no. 2, pp. 432–441, 2010.
[14]  A.-A. Christie, “The stochastic behavior of common stock variances. Value, leverage and interest rate effects,” Journal of Financial Economics, vol. 10, no. 4, pp. 407–432, 1982.
[15]  K.-R. French, G.-W. Schwert, and R.-F. Stambaugh, “Expected stock returns and volatility,” Journal of Financial Economics, vol. 19, no. 1, pp. 3–29, 1987.
[16]  A. Ekholm and D. Pasternack, “The negative news threshold-an explanation for negative skewness in stock returns,” European Journal of Finance, vol. 11, no. 6, pp. 511–529, 2005.
[17]  K.-H. Bae, C. Lim, and K.-C.-J. John Wei, “Corporate governance and conditional skewness in the world's stock markets,” Journal of Business, vol. 79, no. 6, pp. 2999–3028, 2006.
[18]  C.-R. Harvey and A. Siddique, “Conditional skewness in asset pricing tests,” Journal of Finance, vol. 55, no. 3, pp. 1263–1295, 2000.
[19]  G. Bakshi, N. Kapadia, and D. Madan, “Stock return characteristics, Skew laws, and the differential pricing of individual equity options,” Review of Financial Studies, vol. 16, no. 1, pp. 101–143, 2003.
[20]  F. Wen, D. Huang, Q. Lan, and X. Yang, “Numerical simulation for influence of overconfidence and regret aversion on return distribution,” System Engineering Theory and Practice, vol. 27, no. 7, pp. 10–18, 2007.
[21]  T. Post, P.-V. Vliet, and H. Levy, “Risk aversion and skewness preference,” Journal of Banking and Finance, vol. 32, no. 7, pp. 1178–1187, 2008.
[22]  F. Wen and X. Yang, “Skewness of return distribution and coefficient of risk premium,” Journal of Systems Science and Complexity, vol. 22, no. 3, pp. 360–371, 2009.
[23]  E. Ghysels, P. Santa-Clara, and R. Valkanov, “There is a risk-return trade-off after all,” Journal of Financial Economics, vol. 76, no. 3, pp. 509–548, 2005.
[24]  H. Guo and R.-F. Whitelaw, “Uncovering the risk-return relation in the stock market,” Journal of Finance, vol. 61, no. 3, pp. 1433–1463, 2006.
[25]  S. Das and N. Sarkar, “Is the relative risk aversion parameter constant over time? A multi-country study,” Empirical Economics, vol. 38, no. 3, pp. 605–617, 2010.
[26]  M.-V. Brandt and Q. Kang, “On the relationship between the conditional mean and volatility of stock returns: a latent VAR approach,” Journal of Financial Economics, vol. 72, no. 2, pp. 217–257, 2004.
[27]  A. Ang, R. J. Hodrick, Y. Xing, and X. Zhang, “The cross-section of volatility and expected returns,” Journal of Finance, vol. 61, no. 1, pp. 259–299, 2006.
[28]  T.-G. Bali, K. O. Demirtas, and H. Levy, “Is there an intertemporal relation between downside risk and expected returns?” Journal of Financial and Quantitative Analysis, vol. 44, no. 4, pp. 883–909, 2009.
[29]  R.-T. Baillie and R.-P. DeGennaro, “Stock returns and volatility,” Journal of Financial and Quantitative Analysis, vol. 25, no. 2, pp. 203–214, 1990.
[30]  J.-Y. Campbell and L. Hentschel, “No news is good news. An asymmetric model of changing volatility in stock returns,” Journal of Financial Economics, vol. 31, no. 3, pp. 281–318, 1992.
[31]  A. Goyal and P. Santa-Clara, “Idiosyncratic Risk Matters!,” Journal of Finance, vol. 58, no. 3, pp. 975–1007, 2003.
[32]  M. Lanne and P. Saikkonen, “Why is it so difficult to uncover the risk-return tradeoff in stock returns?” Economics Letters, vol. 92, no. 1, pp. 118–125, 2006.
[33]  M. Lanne and J. Luoto, “Robustness of the risk-return relationship in the U.S. stock market,” Finance Research Letters, vol. 5, no. 2, pp. 118–127, 2008.
[34]  A. Kanas, “Modelling the risk-return relation for the S&P 100: the role of VIX,” Economic Modelling, vol. 29, no. 3, pp. 795–809, 2012.
[35]  B. J. Christensen, M. ?. Nielsen, and J. Zhu, “The impact of financial crises on the risk-return tradeoff and the leverage effect,” CREATES Research Paper, 2012.
[36]  J.-R. Anderson, “Verbatim and propositional representation of sentences in immediate and long-term memory,” Journal of Verbal Learning and Verbal Behavior, vol. 13, no. 2, pp. 149–162, 1974.
[37]  J.-M. Mandler and G.-H. Ritchey, “Long-term memory for pictures,” Journal of Experimental Psychology, vol. 3, no. 4, pp. 386–396, 1977.
[38]  B. Lev, “Industry averages as targets for financial ratios,” Journal of Accounting Research, vol. 7, no. 2, pp. 290–299, 1969.
[39]  T.-J. Frecka and C.-F. Lee, “Generalized financial ratio adjustment processes and their implications,” Journal of Ccounting Research, vol. 21, no. 1, pp. 308–316, 1983.
[40]  M. Grinblatt and B. Han, “Prospect theory, mental accounting, and momentum,” Journal of Financial Economics, vol. 78, no. 2, pp. 311–339, 2005.
[41]  D. Prelec and G. Loewenstein, “Decision making over time and under uncertainty: a common approach,” Management Science, vol. 37, no. 7, pp. 770–786, 1991.
[42]  A. Hopfensitz, “Previous outcomes and reference dependence: a meta study of repeated investment tasks with and without restricted feedback,” MPRA Working Paper 16096, 2009.
[43]  P. O'Connell and M. Teo, “Institutional investors, past performance, and dynamic loss aversion,” Journal of Financial and Quantitative Analysis, vol. 44, no. 1, pp. 155–188, 2009.
[44]  D. Genesove and C. Mayer, “Loss aversion and seller behavior: evidence from the housing market,” Quarterly Journal of Economics, vol. 116, no. 4, pp. 1233–1260, 2001.
[45]  á. León, G. Rubio, and G. Serna, “Autoregresive conditional volatility, skewness and kurtosis,” Quarterly Review of Economics and Finance, vol. 45, no. 4-5, pp. 599–618, 2005.
[46]  Q.-F. Xu, Financial Higher Order Moment of Risk Identification and Control, Tsinghua University Press, Beijing, China, 2007.


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