With many genetic traits discovered and many more in progress, it is imperative to the industry that firms (biotechnology companies) decide on the trait valuation and pricing. This includes more than one trait (also referred to as stacked traits) in a single variety of crop; the risk and uncertainty of expected returns associated with the development and release of a variety increases even more in case of stacked traits.
The purpose of this thesis is to develop a model that can be used for the valuing and pricing of genetically modified (GM) traits that are random, sporadic, and non-persistent (e.g. drought tolerance, heat/cold stress) using the real option approach. The efficiency gain in case of occurrence of random event and expression of GM traits will be measured and used as a decision factor in determining the value of GM trait(s) at different phases of development. Risk premiums representing the value of GM trait to growers is calculated across risk averse attitudes. The return to labor and management (RTLM) provided by a GM trait is used to calculate the risk premiums when variation in parameters is allowed to be same as that reflected in historical data and gains from GM traits are realized. Monte Carlo simulation and stochastic efficiency with respect to a function (SERF) are used to estimate the certainty equivalents that decision makers would place on a risky alternative relative to a no risk investment. Certainty equivalents are estimated across a range of risk aversion coefficients and used to rank alternatives and determine where preferences among alternatives change while estimating risk premiums for the base case (no trait), drought tolerance, cold tolerance, NUB, and All traits (all traits combined into one as a stacked trait). Premiums provide perspective on the magnitude of differences in relative preferences among choices. The range of ARAC utilized was from 0.00 to 0.15 for all three crops. The risk premiums are treated as a potential source of revenue in the model as a technology fee charged by a biotech company. This thesis uses the Real Option methodology to evaluate GM traits as Option values at various stages of development. This approach helps managers decide the best possible option in making a certain decision today. It is also helpful in comparing different pathways (series of decisions) and thus better exploits the potential cash return in the future from investments made today (Figure D.1, Figure D.2). Three possible options to "continue", "wait", and "abandon" were modeled in this thesis. Such modeling determines the possible option values of GM traits at different stages of development depending on the kind of choices made at different points of time. This thesis shows that various GM traits that are out-of-money (OTM) at initial stages have increased probability of being in-the-money (ITM) at later stages of development. Sensitivities show that a share of potential technology fees and acreage of GM crops play a significant role in option values being ITM. Stacked traits provide a better chance of being ITM, thus the option to continue will be exercised by management. The option to wait causes reduction in option value. Among
individual traits, drought tolerance has the greatest maximum option value in most cases. Therefore, if management has to choose the development of only one GM trait, it is most likely to choose to invest in the development of drought tolerance.
| Identifer | oai:union.ndltd.org:ndsu.edu/oai:library.ndsu.edu:10365/29713 |
| Date | January 2009 |
| Creators | Shakya, Sumadhur |
| Publisher | North Dakota State University |
| Source Sets | North Dakota State University |
| Detected Language | English |
| Type | text/thesis |
| Format | application/pdf |
| Rights | NDSU policy 190.6.2, https://www.ndsu.edu/fileadmin/policy/190.pdf |
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