W. Keener Hughen

The Future of Drug Development: The Economics of Pharmacogenomics

This report models how the evolving field of pharmacogenomics, the science of using genomic markers to predict drug response, may impact drug development times, attrition rates and costs. While there still remains an abundance of uncertainty around how pharmacogenomics will impact the future landscape of pharmaceutical and biological R&D, we identify several likely outcomes. We conclude pharmacogenomics (as defined in this context) has the potential to significantly reduce both expected drug development costs via higher probabilities of technical success, shorter clinical development times and smaller clinical trials. Our conclusions are, of course, accompanied by numerous caveats.

Expected Oil Prices and Stock Returns

I extract three oil risk factors using oil futures prices and returns of oil related firms. The first factor accounts for news that uniformly affects expected oil prices at all horizons, the second factor accounts for news that affects near term expected oil prices, and the third factor accounts for news that affects expected distant oil prices. I show that all three factors are important for explaining returns of oil-related portfolios, and account for over 35% of the non-market variation in these portfolios. As a comparison, non-market Fama-French-Carhart factors explain less than 8%. For non-oil industries, the oil risk factors explain a much lower portion of the variation (about 4% on average), however more than half of non-oil industries load significantly on at least one oil factor, and one fourth load significantly on at least two oil factors.

The Use of Bayes Factors to Compare Interest Rate Term Structure Models

Studies of the term structure of interest rates try to explain the relationship between the yield to maturity on zero-coupon bonds and their time to maturity. Over the years, many theoretical models have been developed to explain the stylized facts of U.S. Treasury yields; however, model comparison, parameter estimation and hypothesis testing remain thorny issues. The purpose of this paper is to show that Bayesian methods and Markov Chain Monte Carlo (MCMC) methods, in particular, may help resolve a number of these problems, especially those related to model comparison. We use MCMC to compare the seminal models of Vasicek and Cox, Ingersoll and Ross (CIR). The most surprising result of our analysis is that one of these two models is almost 50 000 times more likely than the other. In contrast, results in the previous literature have been much more ambiguous because they are based on a variety of goodness-of-fit measures. A Monte Carlo study shows that these results are not spurious: the MCMC method is able to select the correct data generation model, whereas goodness-of-fit measures are virtually indistinguishable regardless of whether the data were generated from Vasicek or CIR.