Hortensia Estrada Ramírez
2.2 Morfemas que afijan las raíces adjetivales
Regarding the specific applications of RO to the pharmaceutical industry, while many authors (both academics and practitioners) have highlighted the option nature of drug development and suggested various applications of RO, the present study will explore the various ways these different methods are implemented. In particular, the present study aims at identifying when the two main frameworks are implemented: either the more financially oriented or the more management science oriented. Here, the first refers to the no arbitrage approach. This approach assumes market completeness; allowing for replicating portfolios, risk neutral prob- abilities, and risk free discounting. The second method assumes a ‘real world probabilities’ setting, with a risk adjusted discount rate.
In an early article, Perlitz, Peske and Schrank (1999) used the Geske model (1979) which is an adaptation of the Black- Sholes formula for sequential option valuation. This model was used to evaluate a sequential compound R&D option project in the pharmaceutical industry. Sequential compound options find applications in various industries (i.e. resource extraction, construction, semiconductors, software development) but are particularly relevant in the phar-
maceutical industry. This is because the very nature of the R&D process, where the success in one phase is conditional to the success in the previous one, is more suitably valued as a se- quential option. The use of the Geske model implies a risk neutral valuation setting and the use of the risk free rate for discounting.
Micalizzi presents two case studies on Glaxo (Micalizzi 1999a) and Schering Plough (Mi- calizzi 1999b). In the Schering Plough case the value of an investment opportunity is calculated using a dynamic programming model (Dixit and Pindyck 1994) and a constant risk adjusted discount rate. The Glaxo case refers to the valuation of an abandonment and expansion option within the R&D decision making process. The options’ values are estimated with the assump- tions that the NPV of the project without flexibility evolves according to a binomial process and the risk neutral setting is applied. Micalizzi’s case studies show similarities to the MAD approach where the NPV of the project is considered as best proxy for the project market value if it were traded. The NPV of the project is then modeled as the underlying within a risk neutral valuation framework.
Kellog and Charnes (2000) evaluated the R&D pipeline of a biotech company by first ap- plying a decision tree that was implemented with two different discount rates for development and commercialization cash flows, before applying a binomial tree. The lattice is based on the risk neutral approach of Cox, Ross and Rubinstein (1979) with risk free discounting.
Another project valuation in the pharmaceutical industry was presented by Borissiouk and Peli (2001). Here, the compound option is evaluated using continuum and discrete mod- els, both of which are based on the assumption that markets are complete therefore applying risk neutral pricing. Loch and Bode-Gruel (2001) used decision trees as a means of identifying and evaluating a project growth option that may arise from an R&D program. Like Amram and Kulatilaka (1999), they recognized that R&D pharmaceutical projects are affected mainly by project specific risk for which a replicating portfolio cannot be found. Consequently, the authors propose the use of decision trees to capture risk and flexibility in the pharmaceutical business, where an individual project’s payoffs cannot be replicated in financial markets. Tri- antis and Borison (2001) highlight an example of real option applications to drug development under the risk neutral approach using the biotech company Genentech. Here, the use of risk neutral probabilities to risk adjust the payoffs is advocated for market risk, while subjective probabilities for project specific risk do not need to be risk adjusted because the risk can be diversified away by investors. Rogers, Gupta and Maranas (2002) employed a quadrinomial approach, under the assumption of replicating portfolio and risk neutrality, to evaluate an
R&D project. The reference for the use of a tracking portfolio for a drug is made to (Schwartz and Moon 2000). The same methodology (replicating portfolio and risk neutrality) is proposed in Gupta and Maranas (2004) and in Rogers, Maranas and Ding (2005). The article of Schwarz (2004) presents a model to evaluate a drug R&D project with abandonment option, under the conditions of risk neutrality using Monte Carlo simulations. The paper of Cassimon et al. (2004) presents an R&D valuation for a pharmaceutical company’s new drug applications (NDA); a 6 fold compound option obtained as generalization of the Geske‘s model (1979) is applied under the assumption of risk neutrality. Pennings and Sereno (2010) evaluate an R&D project by modeling the technical uncertainty by means of a Poisson jump and the market un- certainty by a standard diffusion process under risk neutral conditions. Like Copeland and Antikarov (2001) they consider a compound sequential option, but they further implement a model where information arrives continuously and discontinuously over time. This approach thus allows for the possibility of technical failure to occur at any R&D phase. Willigers and Hansen (2008) compare the valuation of R&D projects using 3 different methods: 1) LSM (least squares Monte Carlo) real option valuation based on the methodology proposed by Schwartz (2004) 2) Industry traditional eNPV 3) binomial real option using the MAD methodology of Copeland Antikarov (2000). Approach 1) and 3) share the assumption of risk neutrality.
Authors, mainly working in the area of consultancy, such as Copeland and Antikarov (2001), Mun (2004, 2006), and Kodukula and Papudesku (2006) present various examples and practical applications of the MAD approach in the pharmaceutical business. The MAD ap- proach adopts the same assumptions as the NPV so as to make it an applicable method for real options. This approach surpasses the need to find a replicating portfolio, while still relying on the neutral probabilities setting. This use of risk neutral probabilities, not justified in the phar- maceutical business where projects are not market-traded assets, is employed also by Brach (2003) and Shockley (2007). In their work on RO applications they make the assumption that “a twin security exists in the market that captures exactly the risk and payoffs of the project and allows to construct a risk free hedge” Brach (2003). Shokley (2007) used the replicating portfo- lio assumption in other case studies within the coal mining industry or the oil industry both of which, unlike the pharmaceutical industry, have market traded prices that make this method viable. In comparison, Villiger and Bogdan (2005) evaluated a pharmaceutical R&D project using, as underlying, the project peak sales. Consistent with the specific business, the authors employ real world probabilities versus risk neutral probabilities, as well as a risk-adjusted dis- count rate which is kept constant along the tree. Their assumptions is that when dealing with
real business cases, such as the R&D phases of a specific drug under development in the phar- maceutical industry, the replicating portfolio theory cannot be applied since the underlying, the drug under development, is a non-traded asset. This assumption is relevant to the present study which shares the view that “risk neutrality or change of probabilities measures like for financial options lack any justification” Bodgan and Villiger (2007).
The practical applications of RO to the pharmaceutical business show how the different approaches rely mainly on the risk neutral assumption, market completeness and the possibil- ity of finding a replicating portfolio that perfectly mimics the payoff of the underlying project. Here, it is worth noting that to value a RO under risk neutral probabilities is as unrealistic as valuing a hedged business opportunity. Instead, real business conditions must become a part of RO analysis considered as real business opportunities that require a strategic decision approach. Uncertainty characterizes business conditions. When it is resolved through the pas- sage of time, managers can make value creating decisions by choosing the optimal action that maximizes the expected net present value of payoffs (Dixit and Pindyck 2004).
The contribution of the present study is to valuate a compound sequential R&D option, an expansion option and an option to wait by considering how market and technical risk are in- terrelated. This is the same approach used by Shockley (2007) and Bodgan and Villiger (2007), but unlike Shockley we will not work under risk neutral probability, instead using real world probabilities. Real world probabilities assign to the project a likelihood of commercial evolu- tion and are driven by management experience and marketing research. Risk neutral proba- bilities based on the assumption that the underlying is a market-traded asset, are an elegant mathematical expedient used to simplify the valuation process, but they fail to represent any business opinion.
Contrary to Bodgan and Villiger (2007), who used real world probabilities and a constant risk adjusted discount rate, the present study will use a discount rate that changes to reflect the different risk profile of each node along a decision tree.