USING BAYESIAN BETAS TO ESTIMATE RISK RETURN PARAMETERS: AN EMPIRICAL INVESTIGATION

Since its original development by Sharpe (1964), the Capital Asset Pricing Model (CAPM) has been the focus of great interest, practical usage, modifications, testing, and controversy. The basic hypothesis of the CAPM is that the minimum expected return required by investors on any asset will equal the risk-free rate plus a premium for the asset's contribution to the variance risk of a diversified portfolio as measured by the asset's beta. The model is often utilized by investors to calculate the relevant risk and required return on an asset, while corporate officers widely employ the theory to compute the appropriate discount rate to use in estimating the net present value of capital budgeting projects when evaluating spending decisions (Gitman and Mercurio, 1982).     

A SUGGESTED SIMPLE PROCEDURE TO OBTAIN BLUE ESTIMATOR OF GLOBAL BETA

Common stocks are typically traded on numerous exchanges both domestically and internationally. The price behaviour of stocks traded in multimarket settings provides investors with additional information regarding the risk characteristics of those shares. In this paper we provide a simple procedure to obtain the best linear unbiased (BLUE) estimator of relevant systematic risk (beta) of a stock by incorporating as much information as is possibly available in the market place.     

USING PROJECT FINANCE TO FUND INFRASTRUCTURE INVESTMENTS

For much of the past century, there has been an increased tendency for large infrastructure projects to be funded and operated by governments. Since the early 1980s, however, private-sector financing and management of such projects have experienced a dramatic revival. In some cases, this revival has taken the form of the "privatization" of an entire industry. But another, increasingly common, form has been the use of project finance to fund instrastructure investments. Besides being widely used in infrastructure investments like telecommunications and power generation in developing countries, the use of project finance has recently been extended by the U.K.'s Private Finance Initiative to fund public enterprises as diverse as the construction and operation of prisons, hospitals, subway cars, and the National Insurance computer system.
In a project financing, the project is managed by a separate company that is owned by a project sponsor (or sponsors) who usually takes an active role in the management of the project. The project company enters into a complex series of contracts with multiple parties, including the host government, the project's customers and suppliers, and the banks that typically provide most of the debt financing.
This paper argues that the equity investment by the project's operators works together with high debt ratios and the web of contractual arrangements to reduce "agency" problems in the management of large projects. It also shows how the contracts shift the various project risks to those parties best able to appraise and control them. Finally, it discusses why most project financing takes the form of limited recourse bank loans to the project company rather than, say, public bonds with full recourse to the sponsors.     

A STATE-SPACE APPROACH TO ESTIMATE AND TEST MULTIFACTOR COX-INGERSOLL-ROSS MODELS OF THE TERM STRUCTURE

The objective of this paper is to estimate and test multifactor versions of the Cox-Ingersoll-Ross (CIR) model of the nominal term structure of interest rates. The proposed state-space approach integrates time series and cross-sectional aspects of the CIR model, is consistent with the underlying economic model, and can use information from all available points of the term structure. We recover estimates of the underlying factors that are consistent with the assumptions about the stochastic processes and compare them with factors obtained from standard factor analysis. We perform thorough diagnostic checking and thereby provide new evidence regarding conclusions about the adequacy of the CIR model. We present empirical results for U.S. Treasury market data. Although the specification of multifactor CIR models is sufficiently flexible for the shape of the term structure, we find strong evidence against the adequacy of the CIR model.