BEHAVIORAL PORTFOLIO SELECTION: ASYMPTOTICS AND STABILITY ALONG A SEQUENCE OF MODELS
研究了在弱收敛的金融市场序列中，行为偏好函数的最大化问题，证明了非凹效用和扭曲期望的收敛性，并指出需要比凹效用更强的模型收敛条件，为行为投资组合的数值求解提供方法。
Abstract We consider a sequence of financial markets that converges weakly in a suitable sense and maximize a behavioral preference functional in each market. For expected concave utilities, it is well known that the maximal expected utilities and the corresponding final positions converge to the corresponding quantities in the limit model. We prove similar results for nonconcave utilities and distorted expectations as employed in behavioral finance, and we illustrate by a counterexample that these results require a stronger notion of convergence of the underlying models compared to the concave utility maximization. We use the results to analyze the stability of behavioral portfolio selection problems and to provide numerically tractable methods to solve such problems in complete continuous‐time models.