考虑市场学习行为的贝叶斯决策模型构建及应用
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- 英文论文题名：Construction and Application of Bayesian Decision Model with Market Learning Behavior
- 论文作者：周丽莉 ; 丁东洋
- 英文论文作者：ZHOU Li-li ; DING Dong-yang ; School of Economics and Management ; Nanchang University ; School of Public Administration ; Nanchang University
- 年：2016
- 作者机构：南昌大学经济管理学院;南昌大学公共管理学院;
- 论文关键词：学习行为 ; 贝叶斯方法 ; 决策模型 ; 数值模拟
- 英文论文关键词：learning behavior ; Bayesian methods ; decision model ; numerical simulation
- 会议召开时间：2016-11-12
- 会议录名称：第十八届中国管理科学学术年会论文集
- 英文会议录名称：Proceedings of the 18th Chinese Annual Conference on Management Science
- 语种：中文
- 分类号：F224;F830
- 学会代码：ZHYJ
- 会议名称：第十八届中国管理科学学术年会
- 会议地点：中国陕西西安
- 主办单位：中国优选法统筹法与经济数学研究会、西安交通大学、中国科学院科技战略咨询研究院、《中国管理科学》编辑部
- 学会名称：中国优选法统筹法与经济数学研究会
- 页数：6
- 文件大小：455k
- 原文格式：O
- 会议级别：全国
摘要
贝叶斯统计模型对于描述复杂市场环境下的决策行为具有重要意义。当决策者具有根据信息不断更新信念的学习行为时,不能再采用传统的静态稳定参数推断方法进行统计决策。在贝叶斯框架下,可以结合决策主体的主观信念设定相关参数构建模型。基于储蓄-消费模型的研究发现市场学习行为对统计决策的影响显著,构建考虑学习行为的贝叶斯决策模型能够解释过于保守的谨慎行为。通过数值模拟,进一步说明具有学习过程的决策行为更加贴近现实。相关研究结论值得引起对传统风险度量方法的反思。
Bayesian statistical model is important to describe the decision behavior in complex market environment.When decision makers have learning behavior which can continuously update beliefs based on information,statistical decision should no longer use the traditional static stability parameters.In the Bayesian framework,model can be built up by setting parameters combined with subjective beliefs of decision makers.Market learning behavior has significant effect on statistical decision based on saving — consumption model,Bayesian decision model with learning behavior can explain the conservative consumption.Decision behavior with the learning process will be closer to reality by numerical simulation.Related findings deserve reflection of the traditional risk measurement methods.
Bayesian statistical model is important to describe the decision behavior in complex market environment.When decision makers have learning behavior which can continuously update beliefs based on information,statistical decision should no longer use the traditional static stability parameters.In the Bayesian framework,model can be built up by setting parameters combined with subjective beliefs of decision makers.Market learning behavior has significant effect on statistical decision based on saving — consumption model,Bayesian decision model with learning behavior can explain the conservative consumption.Decision behavior with the learning process will be closer to reality by numerical simulation.Related findings deserve reflection of the traditional risk measurement methods.
引文
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[2]Matthew G.Valuing new goods in a model with complementarily:Online newspapers[J].American Economic Review,2006,97(3):713-744.
[3]Daniel,K.A perspective on judgment and choice:Mapping bounded rationality[J].American Psychologist,2003,58(9):697-698.
[4]Pastor L,Veronesi P.Learning in financial markets[J].Annual Review of Financial Economics,2009,1(1):361-381.
[5]Hansen L,Sargent T.Fragile beliefs and the price of uncertainty[J].Quantitative Economics,2010,1(1):129-162.
[6]Bekaert G,Hoerova M,Duca L,et al.Risk,uncertainty,and monetary policy[J].Journal of Monetary Economics,2013,60(3):771-788.
[7]Blanchard O,Ariccia G,Mauro P.Rethinking macroeconomic policy[J].Journal of Money Credit and Banking,2010,42(1):199-215.
[8]何朝林.均值-方差模型具有一般不确定性下的最优资产组合选择[J].中国管理科学,2015,23(12):63-70.
[9]高金窑.奈特不确定性与非流动资产定价:理论与实证[J].经济研究,2013,(10):82-97.
[10]赵大萍,张超梁,房勇.基于贝叶斯理论的长、短数据资产组合选择[J].中国管理科学,2015,23(11):504.-509.
[11]Guy T,Karny M,Wolpert D.Decision making:uncertainty,Imperfection,Deliberation and Scalability[M].New York:Springer,2015.
[12]Machina M,Viscusi W.Handbook of the economics of risk and uncertainty[M].Oxford:North Holland,2014.
[13]Blanchard 0,Romer D.Spence M,et al.In the wake of the crisis;Leading economists reassess economic policy[M].Cambridge:MIT Press,2014.
[14]Galati CMoessner R.Macroprudential policy.A Literature Review[J].Journal of Economic Surveys,2013,27(5):846-878.
[15]Bloom N.The impact of uncertainty shocks[J].Econometrica,2009,77(2):623-685.
[16]Al-Najjar N,Weinstein J.A bayesian model of Knightian uncertainty[J]Theory and Decision,2015,78(1):1-22.