G2TT
来源类型Discussion paper
规范类型论文
来源IDDP7267
DP7267 Assessing agglomeration economies in a spatial framework with endogenous regressors
Michael Artis; Rosina Moreno; Ernest Miguelez
发表日期2009-04-19
出版年2009
语种英语
摘要Mean-variance criteria remain prevalent in multi-period problems, and yet not much is known about their dynamically optimal policies. We provide a fully analytical characterization of the optimal dynamic mean-variance portfolios within a general incomplete-market economy, and recover a simple structure that also inherits several conventional properties of static models. We also identify a probability measure that incorporates intertemporal hedging demands and facilitates much tractability in the explicit computation of portfolios. We solve the problem by explicitly recognizing the time-inconsistency of the mean-variance criterion and deriving a recursive representation for it, which makes dynamic programming applicable. We further show that our time-consistent solution is generically different from the pre-commitment solutions in the extant literature, which maximize the mean-variance criterion at an initial date and which the investor commits to follow despite incentives to deviate. We illustrate the usefulness of our analysis by explicitly computing dynamic mean-variance portfolios under various stochastic investment opportunities in a straightforward way, which does not involve solving a Hamilton-Jacobi-Bellman differential equation. A calibration exercise shows that the mean-variance hedging demands may comprise a significant fraction of the investor's total risky asset demand.
主题Financial Economics
关键词Dynamic programming Incomplete markets Mean-variance analysis Multi-period portfolio choice Stochastic investment opportunities Time-consistency
URLhttps://cepr.org/publications/dp7267
来源智库Centre for Economic Policy Research (United Kingdom)
资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/536104
推荐引用方式
GB/T 7714
Michael Artis,Rosina Moreno,Ernest Miguelez. DP7267 Assessing agglomeration economies in a spatial framework with endogenous regressors. 2009.
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