Abstract
Except for several pairs of utility functions and distribution functions, expected utility maximization problems do not have closed-form solutions so that these problems often require complex numerical optimizations. The paper proposes an approximated solution to this problem using higher moments of returns. Utility functions are approximated by polynomials by the Taylor expansion, and thus, expected utility functions are approximated by linear combinations of moments. With the GARCH effects, a simple approach to estimate conditional higher moments is given. In an empirical study, the strategy is compared to alternative strategies such as mean variance optimization and static optimization.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
