Investment-consumption-insurance optimisation problem with multiple habit formation and non-exponential discounting

by Yike Wang1, Jingzhen Liu2 and Tak Kuen Siu3
1Department of Insurance, School of Finance, Chongqing Technology and Business University, Chongqing 400067, China
2China Institute for Actuarial Science, Central University of Finance and Economics, Beijing 100081, China
(email: janejz.liu@hotmail.com)
3Department of Actuarial Studies and Business Analytics, Macquarie Business School, Macquarie University, Sydney, NSW 2109, Australia

Abstract

This paper is devoted to an investment-consumption and life insurance problem with habit formation and non-exponential utility discounting. General utility functions are employed to evaluate non-habitual consumption and bequest. Distinct from Liu et al. (Non-exponential discounting portfolio management with habit formation. Math. Control Relat. Fields, 10:761-783/2020) for consumption habit and feedback control, we assume that past consumption and bequest amounts have an interaction in formulating their endogenous reference levels, and seek open-loop control for each of the pre-commitment solutions and the time-consistent solution. Since the model coefficients are allowed to be random, we use the stochastic maximum principle arising from a perturbation argument to solve our problems. For each of the pre-commitment solutions and the time-consistent solution, the analytical expression is obtained via a flow of forward-backward stochastic differential equations. Additionally, when the model coefficients are Markovian, we show that our problem for open-loop control can also be reduced to solving a Hamilton-Jacobi-Bellman equation, and then we introduce a transformation method for solving the equation. In particular, we provide the semi-analytical solution with numerical results based on simulations for the constant relative risk aversion (CRRA) utility with hyperbolic


Key words:

Investment-consumption-insurance management, Habit formation, Non-exponential discounting, Stochastic maximum principle, Open-loop Nash equilibrium control
JEL Classification: D11, G11, C61, C73
Mathematics Subject Classification (2020):  93E20, 91G80, 91B08, 91B42, 49N90, 35A22