This article develops and implements a simulation approach to value patents and patent-protected R&D projects based on the Real Options approach. It takes into account uncertainty in the cost-to-completion of the project, uncertainty in the cash flows to be generated from the project, and the possibility of catastrophic events that could put an end to the effort before it is completed. It also allows for the possibility of abandoning the project when costs turn out to be larger than expected or when estimated cash flows turn out to be smaller than anticipated. This abandonment option represents a very substantial part of the project’s value when the project is marginal or/and when uncertainty is large. The model presented can be used to evaluate the effects of regulation on the cost of innovation and the amount on innovative output. The main focus of the article is the pharmaceutical industry. The framework, however, applies just as well to other researchintensive industries such as software or hardware development.

vThe consumption asset pricing framework implies that asset prices may be used to investigate the properties of consumption. An important property of consumption is its elasticity of intertemporal substitution which measures the willingness of individuals to move consumption between time periods in response to changes in interest rates and has important implications for the effectiveness of monetary policy and the behavior of the business cycle. However, consumption asset pricing models typically assume a power utility function in which the elasticity of intertemporal substitution cannot be disentangled from the coefficient of relative risk aversion. While the Epstein-Zin utility function breaks this link, extant empirical tests of this specification have not been able to disentangle these parameter. We argue that this failure arises because data previously used does not properly capture the time dimension needed to accurately estimate the elasticity of intertemporal substitution. We use term structure data, in particular “forward” portfolios which more accurately measure movements across points on the term structure. We find that the Epstein-Zin specification is consistent with out term structure data and we are able to clearly disentangle the elasticity of intertemporal substitution from the coefficient of relative risk aversion.

This paper examines the importance of the regular patterns in the behavior of electricity prices, and its implications for the purposes of derivative pricing. We analyze the Nordic Power Exchange’s spot, futures, and forward prices. We conclude that the seasonal systematic pattern throughout the year, in particular, is of crucial importance in explaining the shape of the futures/forward curve. Moreover, in the context of the one and two factor models analyzed in this paper, a simple sinusoidal function is adequate in order capture the seasonal pattern of the futures and forward curve directly implied by the seasonal behavior of spot electricity prices.

This article presents a simple yet powerful new approach for approximating the value of American options by simulation. The key to this approach is the use of least squares to estimate the conditional expected payoff to the optionholder from continuation. This makes this approach readily applicable in path-dependent and multifactor situations where traditional finite difference techniqes cannot be used. We illustrate this technique with several realistic examples including valuing an option when the underlying asset follows a jump-diffusion process and valuing an American swaption in a 20-factor string model of the term structure.

There are two issues that are of central importance in term structure analysis. One is the modeling and estimation of the current term structure of spot rates. The second is the modeling and estimation of the dynamics of the term structure. These two issues have been addressed independently in the literature. The methods that have been proposed assume a sufficiently complete price data set and are generally implemented separately. However, when the methods are applied to markets with sparse bond price, results are unsatisfactory. We develop a method for jointly estimating the current term structure and its dynamics for markets with low-frequency transactions. We propose solving both issues by using a dynamic term structure model estimated from incomplete panel data. To achieve this, we modify the standard Kalman filter approach to deal with the missing-observation problem. In this way, we can use historic price data in a dynamic model to estimate the current term structure. With this approach we are able to obtain an estimate of the current term structure even for days with an arbitrary low number of price observations. The proposed methodology can be applied to a broad class of continuous-time term-structure models with any number of stochastic factors. To show the implementation of the approach, we estimate a three-factor generalized-Vasicek model using Chilean government bond price data. The approach, however, may be used in any market with low-frequency transactions, a common characteristic of many emerging markets.

This paper discusses two optimal allocation problems. We consider different hypotheses of portfolio selection with stable distributed returns for each of them. In particular, we study the optimal allocation between a riskless return and risky stable distributed returns. Furthermore, we examine and compare the optimal allocation obtained with the Gaussian and the stable non-Gaussian distributional assumption for the risky return. KEY WORDS: optimal allocation, stochastic dominance, risk aversion, measure of risk, a stable distribution, domain of attraction, sub-Gaussian stable distributed, fund separation, normal distribution, mean variance analysis, safety-first analysis.

Since arbitrage involves trading, it is potentially impeded by market frictions and costs. We study whether stock market liquidity is related to the efficacy of arbitrage. Specifically, we examine the joint time-series of the NYSE Composite index futures basis and aggregate liquidity on the NYSE for a relatively long time-period, over 3000 trading days, and find that the basis and liquidity are jointly determined. Contemporaneous innovations in the absolute basis and in bid-ask spreads are positively correlated. There is also evidence of two-way Granger causality between short-term absolute bases and effective spreads. Impulse response functions indicate that shocks to the absolute basis are significantly informative in predicting spreads.

We develop a real options model of R&D valuation, which takes into account the uncertainty in the quality of the research output, the time and cost to completion, and the market demand for the R&D output. The model is then applied to study the problem of pharmaceutical under-investment in R&D for vaccines to treat diseases affecting the developing regions of the world. To address this issue, world organizations and private foundations are willing to sponsor vaccine R&D, but there is no consensus on how to administer the sponsorship effectively. Different research incentive contracts are examined using our valuation model. Their effectiveness is measured in the following four dimensions: cost to the sponsor, the probability of development success, the consumer surplus generated and the expected cost per person successfully vaccinated. We find that, in general, purchase commitment plans (pull subsidies) are more effective than cost subsidy plans (push subsidies), while extending patent protection is completely ineffective. Specifically, we find that a hybrid subsidy constructed from a purchase commitment combined with a sponsor co-payment feature produces the best results in all four dimensions of the effectiveness measure.

This article describes a methodology for evaluating information technology investments using the real options approach. IT investment projects are categorized into development and acquisition projects depending upon the time it takes to start benefiting from the IT asset once the decision to invest has been taken. A couple of models that account for uncertainty both in the costs and benefits associated with the investment opportunity are proposed for these project types. Our stochastic cost function for IT development projects incorporates the technical and input cost uncertainties of Pindyck’s model (1993) but also considers the fact that the investment costs of some IT projects might change even if no investment takes place. In contrast to other models in the real options literature in which benefits are summarized in the underlying asset value, our model for IT acquisition projects represents these benefits as a stream of stochastic cash flows.