Diversification gains in mean-variance efficiency derived from including real atace in financial assel portfolios are examined. Optimal financial and mixed-ass* portfolios were generated by selecting from an investment universe including sevnal distinct financial and rral estate media. Deficiencies of pmious studies were overcome by employing data with improved rrprcsmtatirmm and comparabtlity. The etlkient mixedportfolios dominated the efficient financial asset portfolios implying that purely financial asset diversification is inefficient. The optimal mixedasset portfolio pmcribed that approximately two-thirds of the investment wealth be allocated to real atate and one-third to the financial media.Subject A m Rmfodfo Analysis and R d &arc
A large body of literature has emerged concerning the non-stationarity of security and portfolio betas (e.g., [1], [2], [3], [5], [7], [8], [9], [11], [13]). Empirical research has generally confirmed that estimated betas are temporally non-stationary; with security betas being significantly more volatile than portfolio betas. A variety of techniques has been suggested for the purpose of adjusting the forecasted betas for this non-stationarity. Initially, the techniques (such as those by Blume [2], Vasicek [13], and Merrill-Lynch) involved direct adjustments to Ordinary Least Squares (DLS) estimated betas. Recently however, a more sophisticated group of techniques based on random coefficients and/or switching regression models have been suggested for estimating betas (e.g., [5] and [8], respectively). Although the latter group of techniques are more robust in dealing with nonstationarity, a technique from the former group is much more likely to be used by practicioners due to the general familiarity of the basic DLS-method. A review of the empirical literature on these DLS-based techniques reveals that a form of the Bayes' method suggested by Vasicek [13] has performed better than other DLSbased techniques in estimating and forecasting betas. (See [1] and [7]). Consequently, in selecting an DLS adjustment technique, the Bayesian method would be a logical choice.The purpose of this paper is to show that the observed forecasting accuracy of the Bayes' technique is inherent in the estimating equation and may yield deceptive measures of the techniques' ability to forecast the true parameter. I. The Bayesian MethodThe Bayesian method facilitates the estimation of a parameter which is a random variable by incorporating any prior information about the parameter with current sample information to obtain the final estimate. Specifically, all prior information about a parameter is represented in the prior probability distribution function (pdf), and all new information enters through the likelihood function. The product of the prior pdf and the likelihood function yields the posterior pdf. The mean and variance of the posterior pdf then serve as estimates of the parameter and its variance, respectively. One of the primary advantages of the Bayesian technique relative to traditional DLS is that it allows the true parameter, beta, to be a random variable, while the DLS method assumes it is a constant. Accordingly, the Bayesian technique minimizes the loss due to misestimation when the true parameter is a random variable whereas the DLS method minimizes the mean square error of the estimate when the parameter is considered a constant.The Bayesian formulation suggested by Vasicek [13] assumes both a normal prior and likelihood function in which the corresponding estimates of beta are generated by the market model [12]. The resulting estimates of the posterior mean, {3~, and variance, S~I/,
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.