含时变风险厌恶系数的异质信念模型及实证分析
|
摘要
根据前景理论的反射效应,在做市商调整机制下,对市场中的两类投资者(基本面分析者和趋势追随者)同时引入时变的风险厌恶系数,扩展了异质预期下风险厌恶固定不变的资产定价模型.通过蒙特卡洛模拟,对噪声项和根本确定性系统之间相互作用的分析得出,模型能产生真实的价格行为.最后的实证模拟,对比分析了本模型,原模型及上证指数的收益序列特性,发现本模型能更好的模拟中国股票市场的收益率特性.
Based on the reflection effect of prospect theory, this paper introduces the time-varying risk aversion coefficient for two types of investors(fundamentalists and trend followers) in the market under the market-maker adjustment mechanism at the same time,and expands the asset pricing model with fixed risk aversion under heterogeneous expectation. Through the Monte Carlo simulation, the analysis of the interaction between the noise term and the fundamental deterministic system shows that the model in this paper can produce real price behavior. At last, the empirical simulations compare the characteristics of this model, the He and Li(2016) model, and the returns of the Shangzheng Index. We find that this model can better simulate the returns characteristics of the Chinese stock market.
Based on the reflection effect of prospect theory, this paper introduces the time-varying risk aversion coefficient for two types of investors(fundamentalists and trend followers) in the market under the market-maker adjustment mechanism at the same time,and expands the asset pricing model with fixed risk aversion under heterogeneous expectation. Through the Monte Carlo simulation, the analysis of the interaction between the noise term and the fundamental deterministic system shows that the model in this paper can produce real price behavior. At last, the empirical simulations compare the characteristics of this model, the He and Li(2016) model, and the returns of the Shangzheng Index. We find that this model can better simulate the returns characteristics of the Chinese stock market.
引文
[1] Brock W, and Hommes C. A rational route to randomness[J]. Econometrica, 1997, 65:1059-1095.
[2] Brock W, and Hommes C. Heterogeneous beliefs and routes to chaos in a simple asset pricing model[J]. Journal of Economic Dynamics and Control, 1998, 22:1235-1274.
[3]Nelson D. Conditional heteroskedasticity in asset returns:a new approach[J]. Econometrica, 1991,59:347-370.
[4] Park B-J. On the quantile regression based tests for asymmetries in stock return volatility[J]. Asian Economic Journal, 2002, 16:175-191.
[5] Park B-J. Trading volume, volatility, and GARCH effects in the Korean won-U.S. dollar exchange market:some evidence from conditional quantile estimation[J]. Japanese Economic Review, 2007,58:382-399.
[6] Kahneman D, Tversky A. Prospect theory:an analysis of decision under risk[J]. Econometrica,1979, 47:263-291.
[7] Chiarella C, He X. Heterogeneous beliefs, risk and learning in a simple asset pricing model with a market maker[J]. Macroeconomic Dynamics, 2003, 7:503-536.
[8] Park B-J. Asymmetric herding as a source of asymmetric return volatility[J]. Journal of Banking and Finance, 2011, 35:2657-2665.
[9] Delong J, Shleifer A, Summers L, Waldmann R. Noise trader risk in financial markets[J]. Journal of Political Economy, 1990, 98:703-738.
[10] Brandt M, Wang K. Time-varying risk aversion and unexpected inflation[J]. J Monet Econ, 2003,50:1457-1498.
[11] He X, Li Y. Power law behaviour, heterogeneity, and trend chasing[J]. Journal of Economic Dynamics and Control, 2007, 31:3396-3426.
[12] Park B-J. Time-varying, heterogeneous risk aversion and dynamics of asset prices among boundedly rational agents[J]. Journal of Banking&Finance, 2014, 43:150-159.
[13] Berardi M. Endogenous time-varying risk aversion and asset returns[J]. J Evol Econ, 2016, 26:581-601.
[14] Kukacka J. prospect theory in the heterogeneous agent model[J]. Journal of Economic Interaction and Coordination, 2018:1-28.
[15] Yoon, S J. Time-varying risk aversion and return predictability[J]. International Review of Economics and Finance, 2017, 49:327-339.
[16] He X, Li Y. Volatility clustering:A nonlinear theoretical approach[J]. Journal of Economic Behavior&Organization, 2016, 130:274-237.
[17]周义仓,曹慧,肖燕妮.差分方程及其应用[M].科学出版社,2014.
[2] Brock W, and Hommes C. Heterogeneous beliefs and routes to chaos in a simple asset pricing model[J]. Journal of Economic Dynamics and Control, 1998, 22:1235-1274.
[3]Nelson D. Conditional heteroskedasticity in asset returns:a new approach[J]. Econometrica, 1991,59:347-370.
[4] Park B-J. On the quantile regression based tests for asymmetries in stock return volatility[J]. Asian Economic Journal, 2002, 16:175-191.
[5] Park B-J. Trading volume, volatility, and GARCH effects in the Korean won-U.S. dollar exchange market:some evidence from conditional quantile estimation[J]. Japanese Economic Review, 2007,58:382-399.
[6] Kahneman D, Tversky A. Prospect theory:an analysis of decision under risk[J]. Econometrica,1979, 47:263-291.
[7] Chiarella C, He X. Heterogeneous beliefs, risk and learning in a simple asset pricing model with a market maker[J]. Macroeconomic Dynamics, 2003, 7:503-536.
[8] Park B-J. Asymmetric herding as a source of asymmetric return volatility[J]. Journal of Banking and Finance, 2011, 35:2657-2665.
[9] Delong J, Shleifer A, Summers L, Waldmann R. Noise trader risk in financial markets[J]. Journal of Political Economy, 1990, 98:703-738.
[10] Brandt M, Wang K. Time-varying risk aversion and unexpected inflation[J]. J Monet Econ, 2003,50:1457-1498.
[11] He X, Li Y. Power law behaviour, heterogeneity, and trend chasing[J]. Journal of Economic Dynamics and Control, 2007, 31:3396-3426.
[12] Park B-J. Time-varying, heterogeneous risk aversion and dynamics of asset prices among boundedly rational agents[J]. Journal of Banking&Finance, 2014, 43:150-159.
[13] Berardi M. Endogenous time-varying risk aversion and asset returns[J]. J Evol Econ, 2016, 26:581-601.
[14] Kukacka J. prospect theory in the heterogeneous agent model[J]. Journal of Economic Interaction and Coordination, 2018:1-28.
[15] Yoon, S J. Time-varying risk aversion and return predictability[J]. International Review of Economics and Finance, 2017, 49:327-339.
[16] He X, Li Y. Volatility clustering:A nonlinear theoretical approach[J]. Journal of Economic Behavior&Organization, 2016, 130:274-237.
[17]周义仓,曹慧,肖燕妮.差分方程及其应用[M].科学出版社,2014.