Multi-stage real option evaluation with double barrier under stochastic volatility and interest rate

Annals of Finance - This paper focuses on valuing R&D projects using a twofold compound real option by including two knock-out barriers. However, the valuation of R&D projects is...     

Bayesian assessment of dimensionality in reduced rank regression

We consider Bayesian inference about the dimensionality in the multivariate reduced rank regression framework, which encompasses several models such as MANOVA, factor analysis and cointegration models for multiple time series. The fractional Bayes approach is used to derive a closed form approximation to the posterior distribution of the dimensionality and some asymptotic properties of the approximation are proved. Finite sample properties are studied by simulation and the method is applied to growth curve data and cointegrated multivariate time series.     

Escrow accounts and the value of mortgage servicing contracts

This paper was funded by the Education Committee of the Mortgage Bankers Association. We thank Richard Peach and Sharon Caravan of the MBA, Mark Marple and John Wiseman of MGIC, and Edward Kane of Boston College for their helpful comments, and especially Susan Busch Analytics, Inc for her keen market insights and extensive computational assistance.     

Steady‐state priors for vector autoregressions

Bayesian priors are often used to restrain the otherwise highly over‐parametrized vector autoregressive (VAR) models. The currently available Bayesian VAR methodology does not allow the user to specify prior beliefs about the unconditional mean, or steady state, of the system. This is unfortunate as the steady state is something that economists usually claim to know relatively well. This paper develops easily implemented methods for analyzing both stationary and cointegrated VARs, in reduced or structural form, with an informative prior on the steady state. We document that prior information on the steady state leads to substantial gains in forecasting accuracy on Swedish macro data. A second example illustrates the use of informative steady‐state priors in a cointegration model of the consumption‐wealth relationship in the USA. Copyright © 2009 John Wiley & Sons, Ltd.     

Real Estate Settlement Pricing: A Theoretical Framework

This paper examines the pricing of real estate settlement services. Prices charged by real estate brokers, title insurers, private mortgage companies and other settlement service providers have been a public policy issue for a decade. The Real Estate Settlement Procedures Act of 1974 did little to alleviate public concerns or change pricing practices.
The paper provides both a conceptual framework and some observations on how prices for settlement services are determined. It concludes that the demand has little to do with the services themselves, but rather depends on the demand for housing. This creates an environment for unnecessary services to be performed and for prices to be far in excess of cost.     

Regression density estimation using smooth adaptive Gaussian mixtures

We model a regression density flexibly so that at each value of the covariates the density is a mixture of normals with the means, variances and mixture probabilities of the components changing smoothly as a function of the covariates. The model extends the existing models in two important ways. First, the components are allowed to be heteroscedastic regressions as the standard model with homoscedastic regressions can give a poor fit to heteroscedastic data, especially when the number of covariates is large. Furthermore, we typically need fewer components, which makes it easier to interpret the model and speeds up the computation. The second main extension is to introduce a novel variable selection prior into all the components of the model. The variable selection prior acts as a self-adjusting mechanism that prevents overfitting and makes it feasible to fit flexible high-dimensional surfaces. We use Bayesian inference and Markov Chain Monte Carlo methods to estimate the model. Simulated and real examples are used to show that the full generality of our model is required to fit a large class of densities, but also that special cases of the general model are interesting models for economic data.     

Bayesian estimation of an open economy DSGE model with incomplete pass-through

In this paper, we develop a dynamic stochastic general equilibrium (DSGE) model for an open economy, and estimate it on Euro area data using Bayesian estimation techniques. The model incorporates several open economy features, as well as a number of nominal and real frictions that have proven to be important for the empirical fit of closed economy models. The paper offers: i) a theoretical development of the standard DSGE model into an open economy setting, ii) Bayesian estimation of the model, including assessments of the relative importance of various shocks and frictions for explaining the dynamic development of an open economy, and iii) an evaluation of the model's empirical properties using standard validation methods.     

Rapid housing privatization in reforming economies: Pay the special dividend now

State ownership and operation of the housing stock in the previously centrally planned economies severely distorts housing markets, stifles labor mobility, and produces operating losses that exacerbate fiscal deficits. The conventional wisdom regarding structural reform is to gradually increase administered rents, thereby reducing deficits. Housing sales, where they occur, are primarily motivated to generate revenue to cover deficits in current operating budgets. We argue that the perceived benefits to a prolonged transition are illusory and the social costs are high.Housing will be affordable only if the population's current ownership of the existing housing stock is recognized and dividends on it are paid out. The payment can be a regular dividend (e.g., housing allowances) or a one-time special dividend (e.g., ownership vouchers). The latter is preferable.     

Bayesian point estimation of the cointegration space

A neglected aspect of the otherwise fairly well developed Bayesian analysis of cointegration is point estimation of the cointegration space. It is pointed out here that, due to the well known non-identification of the cointegration vectors, the parameter space is not Euclidean and the loss functions underlying the conventional Bayes estimators are therefore questionable. We present a Bayes estimator of the cointegration space which takes the curved geometry of the parameter space into account. This estimate has the interpretation of being the posterior mean cointegration space and is invariant to the order of the time series, a property not shared with many of the Bayes estimators in the cointegration literature. An overall measure of cointegration space uncertainty is also proposed. Australian interest rate data are used for illustration. A small simulation study shows that the new Bayes estimator compares favorably to the maximum likelihood estimator.     

Generalized smooth finite mixtures

We propose a general class of models and a unified Bayesian inference methodology for flexibly estimating the density of a response variable conditional on a possibly high-dimensional set of covariates. Our model is a finite mixture of component models with covariate-dependent mixing weights. The component densities can belong to any parametric family, with each model parameter being a deterministic function of covariates through a link function. Our MCMC methodology allows for Bayesian variable selection among the covariates in the mixture components and in the mixing weights. The model’s parameterization and variable selection prior are chosen to prevent overfitting. We use simulated and real data sets to illustrate the methodology.