![]() ![]() In the chapter 1, I suggest an extension of the RIF Decomposition for taking into account the average selectivity bias. This thesis studies an estimation technique applied for gender inequalities, an explaining factor for these gender inequality and their evolutions in the world as well. Finally, we discuss applications of this framework to robo-advisors. We establish a semi-analytical solution for the optimal trading strategy in our framework and provide numerical examples to illustrate its behavior. Our proposed asset allocation framework retains two attractive features of mean-variance optimization: an intuitive explanation of the investment objective and an easily computed optimal strategy. This naturally leads to a different characterization of possible investment outcomes below and above a target wealth as risk and potential. Crucially, our framework differs from mean-variance analysis in that we allow different treatment of upside and downside deviations from a target wealth level. The utility functions are motivated by the equivalence between the mean-variance objective and a quadratic utility function. ![]() We propose a novel dynamic asset allocation framework based on a family of mean-variance-induced utility functions that alleviate the non-monotonicity and time-inconsistency problems of mean-variance optimization. ![]()
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