A modified mean-variance-conditional value at risk model of multi-objective portfolio optimization with an application in finance

This research focuses on the development of a portfolio optimization model based on the classic optimization method and a meta-heuristic algorithm. The main goal of a portfolio optimization model is to achieve maximum return with minimum investment risk by allocating capital based on a set of existing assets. Recently, mean-variance models have been improved to mean-variance-CVaR (MVC) model as a multi-objective portfolio optimization (MPO) problem which is difficult to be solved directly and optimally. In this work, a modified MVC model of portfolio optimization is constructed using the weighted sum method (WSM). In this method, each objective function of MVC model is given a weight. The optimization problem is then minimized as a weighted sum of the objective functions. The implementation of WSM enables the MVC model to be transformed from a multi-objective function to one with a single objective function. The modified MVC model is then solved using ant colony optimization (ACO) algorithm. This algorithm solves the MVC model by the number of ant colonies and the number of pheromone, a chemical creating trails for others to follow. The modified MVC model can be used in managing diverse investment portfolio, including stocks on the stock market and currency exchange. The applicability and effectiveness of the proposed method are demonstrated by solving a benchmark problem and a practical investment problem as examples. The data of practical examples are collected from the foreign currency exchange of Bank Negara Malaysia for the years 2012 and 2013. In conclusion, this thesis presented a hybrid optimization algorithm which utilizes a classical approach, WSM and a meta-heuristic approach, ACO to solve an MVC model of portfolio optimization

A convex optimization method for joint mean and variance parameter estimation of large-margin CDHMM

[[abstract]]© 2008 Institute of Electrical and Electronics Engineers-In this paper, we develop a new class of parameter estimation techniques for the Gaussian continuous-density hidden Markov model (CDHMM), where the discriminative margin among a set of HMMs is used as the objective function for optimization. In addition to optimizing the mean parameters of the large-margin CDHMM, which was attempted in the past, our new technique is able to optimize the variance parameters as well. We show that the joint mean and variance estimation problem is a difficult optimization problem but can be approximated by a convex relaxation method. We provide some simulation results using synthetic data which possess key properties of speech signals to validate the effectiveness of the new method. In particular, we show that with joint optimization of the mean and variance parameters, the CDHMMs under model mismatch are much more discriminative than with only the mean parameters.[[fileno]]2030157030009[[department]]電機工程學