International asset demands are functions of expected returns. Optimal portfolio theory tells us that the coetlicients in this relationship depend on the variance-covariance matrix of real returns. But previous estimates of the optimal portfolio (1) assume expected returns constant and (2) are not set up to test the hypothesis of mean-variance optimization. We use maximum likelihood estimation ...

In this paper, a maximum likelihood estimation and a minimum entropy estimation for the expected value and variance of normal fuzzy variable are discussed within the framework of credibility theory. As an application, a credibilistic portfolio selection model is proposed, which is an improvement over the traditional models as it only needs the predicted values on the security returns instead of...

In this paper, a new modified version of geometric distribution is proposed. The newly introduced model called transmuted record type (TRTG) distribution. TRTG good alternative to the negative binomial, Poisson and distributions in modeling real data encountered several applied fields. main statistical properties were obtained. We determined measures value at risk tail for These are important q...

A blind adaptive scheme is proposed for joint maximum likelihood (ML) channel estimation and data detection of single-input multiple-output (SIMO) systems. The joint ML optimization over channel and data is decomposed into an iterative optimization loop. An efficient global optimization algorithm called the repeated weighted boosting search is employed at the upper level to identify optimally t...

It is widely recognized that when classical optimal strategies are used with parameters estimated from data, the resulting portfolio weights are remarkably volatile and unstable over time. The predominant explanation for this is the di¢ culty to estimate expected returns accurately. We propose to parameterize an n stock Black-Scholes model as an n factor Arbitrage Pricing Theory model where eac...

We study the viability of different robust optimization approaches to multiperiod portfolio selection. Robust optimization models treat future asset returns as uncertain coefficients in an optimization problem, and map the level of risk aversion of the investor to the level of tolerance of the total error in asset return forecasts.We suggest robust optimization formulations of the multiperiod p...

Considering mean-variance portfolio problems with uncertain model parameters, we contrast the classical absolute robust optimization approach with the relative robust approach based on a maximum regret function. Although the latter problems are NP-hard in general, we show that tractable inner and outer approximations exist in several cases that are of central interest in asset management. AMS s...