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| Title: | The valuation of projects:a real-option approach |
| Authors: | 吳聰皓 |
| Contributors: | 顏錫銘
吳聰皓 |
| Keywords: | real options
patent valuation
copula functions
R&D projects |
| Date: | 2009 |
| Issue Date: | 2010-12-08 01:54:32 (UTC+8) |
| Abstract: | Valuation of R&D projects is quite complex due to the substantial uncertainties in a project`s life-cycle phases. The sequential nature of R&D projects continuously provides decision-makers with choices regarding whether and when to undertake future potential investment opportunities. This means that when valuing R&D projects decision-makers should take these factors into account. But R&D project usually takes long time to complete processes for commercialization. If the time to complete is longer, it is easier to trigger the crisis for capital shortage. So it seems very important modeling the capital shortage risk to induce the probability of failure in the pricing model. In this thesis we try to apply the analogy of financial securities subject to credit risk of Jarrow & Turnbull (1995) and attempt to value patents with capital shortage risk in an arbitrage free environment using the martingale measure technique. Furthermore, derive closed form formula for patents valuation which makes application easier than that of the theoretic option model. The major findings are: (1) when considering the effect of the failure frequency (capital shortage risk), the patent value will grow rapidly and then converge in the short run, no matter how other parameters incorporated into the robust analysis; (2) when increasing in the volatility of market revenues with synchronized higher volatility of investment cost, the volatility curve will be distorted to be U-shaped. Meanwhile, lower failure frequency could aggravate the decreasing in the option value.
Another issue is when the manager exercises the project with multiple underlying assets, where the assets returns are of non-linear correlation particularly in the non-Normal environment. Non-parametric dependence measures may better employed when explaining co-movement. We focus on the value of a (such as resources development) project in general depends on the price of the multiple products; these are usually correlated to some extent. So the project was treated as having a rainbow option, whose underlying asset prices correlate with each other, and also as having uncertainties that decrease according to the project stage. Based on Cherubini and Luciano’s framework (2002), the risk-neutral copula models are derived to figure decision flexibilities out easily. The main framework studies the valuation of a project (call on Max) by determining the joint risk-neutral distribution of the underlying assets (products) using copulas. Monte-Carlo simulations show that the higher default risk and association among the assets and the expected cost to completion contributes the higher risk premium in our model with dependence structure of Archimedean copula family than traditional Black-Scholes environment.
Valuation of R&D projects is quite complex due to the substantial uncertainties in a project`s life-cycle phases. The sequential nature of R&D projects continuously provides decision-makers with choices regarding whether and when to undertake future potential investment opportunities. This means that when valuing R&D projects decision-makers should take these factors into account. But R&D project usually takes long time to complete processes for commercialization. If the time to complete is longer, it is easier to trigger the crisis for capital shortage. So it seems very important modeling the capital shortage risk to induce the probability of failure in the pricing model. In this thesis we try to apply the analogy of financial securities subject to credit risk of Jarrow & Turnbull (1995) and attempt to value patents with capital shortage risk in an arbitrage free environment using the martingale measure technique. Furthermore, derive closed form formula for patents valuation which makes application easier than that of the theoretic option model. The major findings are: (1) when considering the effect of the failure frequency (capital shortage risk), the patent value will grow rapidly and then converge in the short run, no matter how other parameters incorporated into the robust analysis; (2) when increasing in the volatility of market revenues with synchronized higher volatility of investment cost, the volatility curve will be distorted to be U-shaped. Meanwhile, lower failure frequency could aggravate the decreasing in the option value.
Another issue is when the manager exercises the project with multiple underlying assets, where the assets returns are of non-linear correlation particularly in the non-Normal environment. Non-parametric dependence measures may better employed when explaining co-movement. We focus on the value of a (such as resources development) project in general depends on the price of the multiple products; these are usually correlated to some extent. So the project was treated as having a rainbow option, whose underlying asset prices correlate with each other, and also as having uncertainties that decrease according to the project stage. Based on Cherubini and Luciano’s framework (2002), the risk-neutral copula models are derived to figure decision flexibilities out easily. The main framework studies the valuation of a project (call on Max) by determining the joint risk-neutral distribution of the underlying assets (products) using copulas. Monte-Carlo simulations show that the higher default risk and association among the assets and the expected cost to completion contributes the higher risk premium in our model with dependence structure of Archimedean copula family than traditional Black-Scholes environment. |
| Reference: | Amin, K.I. and R.A. Jarrow, 1992, “Pricing Options on Risky Assets in a Stochastic Interest Rate Economy,” Mathematical Finance 2, 217-237.
Benninga, S., and E. Tolkowsky, 2002, “Real-Options: An Introduction and Application to R&D Valuation,” The Engineering Economist 47, 151-168.
Berk, J., R. Green and V. Naik, 2004, “Valuation and Return Dynamics of New Ventures,” Review of Financial Studies 17, 1-35.
Bhattacharya, S., and D. Mookherjee, 1986, “Portfolio Choice in Research and Development,” Rand Journal of Economics 17, 594-605.
Bikos, A., 2000, “Bivariate Fx Pdfs: A Sterling eri Application,” Bank of England ,” Working Paper.
Black, F., and M. Scholes, 1973, “The Pricing of Options and Corporate Liabilities,” Journal of Political Economy 81, 637-654.
Bloch, C. , 2005, "R&D investment and internal finance: The cash flow effect," Economics of Innovation and New Technology 14(3): 213-223.
Brennan, M., and E. Schwartz, 1985, “Evaluating natural resource investments,” The Journal of Business 58, 135-157.
Cassimon, D., P.J. Engelen, L. Thomassen, and M.V. Wouwe, 2004, “The Valuation of a NDA using a 6-fold Compound Option,” Research Policy 33, 41-51.
Cherubini, U., and E. Luciano, 2002, Bivariate Option Pricing with Copulas,” Applied Mathematical Finance 9, 69-85.
Childs, P., and A. Triantis, 1999, “Dynamic R&D Investment Policies,” Management Science 45, 1359-1377.
Copeland, T., and P. Keenan, 1998, “Making Real Options Real,” The McKinsey Quarterly 1.
Copeland, T., T. Koller, and J. Murrin, 2000. Valuation: Measuring and Managing the Value of Companies (Wiley).
Cortazar, G., and E. Schwartz, 1993, “A Compound Option Model of Production and Intermediate Inventories,” The Journal of Business 66, 517-540.
Cortazar, G., E. Schwartz, and M. Salinas, 1998, “Evaluating Environmental Investments: A Real Options Approach,” Management Science 44, 1059-1070.
Damodaran, A., 2000, “The Promise of Real Options,” Journal of Applied Corporate Finance 13, 29-44.
Das, S., D. Duffie, 2007, "Common failings: How corporate defaults are correlated," The Journal of Finance 62(1): 93-117.
Dasgupta, P., and E. Maskin, 1987, “The Simple Economics of Research Portfolios,” The Economic Journal 97, 581-595.
Dixit, A., R. Pindyck, and G. Davis, 1994, Investment under Uncertainty (Princeton University Press Princeton, NJ).
Duffie, D., and K. Singleton, 1999, “Modeling Term Structures of Defaultable bonds,” Review of Financial Studies 12, 687.
Dutta, P., 1997, Optimal Management of an R&D budget,” Journal of Economic Dynamics and Control 21, 575-602.
Ekern, S., 1988, “An Option Pricing Approach to Evaluating Petroleum Projects,” Energy Economics 10, 91-99.
Embrechts, P., A. McNeil, and D. Straumann, 2002, Correlation and Dependence Properties in Risk Management: Properties and Pitfalls, in M. Dempster, ed., Risk Management: Value at Risk and Beyond, Cambridge University Press.
Embrechts, P., C. Kluppelberg, Modelling Extremal Events for Insurance and Finance. 1997, Springer-Verlag: Heidelberg.
Galambos, J., 1978, The Asymptotic Theory of Extreme Order Statistics, Wiley, New York.
Geske, R., 1979, “The Valuation of Compound Options,” The Journal of Financial Economics 7, 63-81.
Giannetti, C. and C. San Micheletto, 2009, "Relationship Lending and Firm Innovativeness," SSRN Working Paper.
Gompers, P., 1995, “Optimal Investment, Monitoring, and the Staging of Venture Capital,” The journal of finance 50, 1461-1489.
Gouriéroux, G. and J. Jasiak, 2004, “Stochastic Volatility Durations,” Journal of Econometrics 119, 413-435.
Grossman, G., and C. Shapiro, 1986, “Optimal Dynamic R&D Programs,” The Rand Journal of Economics 17, 581-593.
Gukhal, C., 2004, “The Compound Option Approach to American Options on Jump-Diffusions,” Journal of Economic Dynamics and Control 28, 2055-2074.
He, H., and R. Pindyck, 1992, “Investments in Flexible Production Capacity,” Journal of Economic Dynamics and Control 16, 575-99.
Herath, H., and C. Park, 2002, “Multi-Stage Capital Investment Opportunities as Compound Real Options,” The Engineering Economist 47, 1-27.
Ho, T., and S. Lee, 1986, “Term Structure Movements and Pricing Interest Rate Contingent Claims,” The journal of finance 41, 1011-1029.
Hsu, J., and E. Schwartz, 2008, “A Model of R&D Valuation and the Design of Research Incentives,” Insurance Mathematics and Economics 43, 350-367.
Jarrow, R., and S. Turnbull, 1995, “Pricing Derivatives on Financial Securities Subject to Credit Risk,” Journal of finance 53-85.
Jarrow, R., D. Lando, and S. Turnbull, 1997, "A Markov model for the term structure of credit risk spreads," Review of financial studies 10(2): 481-523.
Jarrow, R., D. Lando, and S. Turnbull, 1997, “A Markov Model for the Term Structure of Credit Risk Spreads,” The Review of Financial Studies 10, 481-523.
Joe, H., 1997, “Multivariate Models and Dependence Concepts,” Monographs in Statistics and Probability 73, Chapman and Hall, London.
Johnson, H., and R. Stulz, 1987, “The Pricing of Options with Default Risk,” Journal of finance 267-280.
Kellogg, D., and J. Charnes, 2000, “Real-Options Valuation for a Biotechnology Company,” Financial Analysts Journal 56, 76-84.
Kester, W., 1984, “Today’s Options for Tomorrow’s Growth,” Harvard Business Review 62, 153-160.
Klein, P., 1996, “Pricing Black-Scholes Options with Correlated Credit risk,” Journal of Banking & Finance 20, 1211-1229.
Longstaff, F., and E. Schwartz, 1995, “A Simple Approach to Valuing Risky Fixed and Floating Rate Debt,” The journal of finance 50, 789-819.
Longstaff, F., and E. Schwartz, 2001, “Valuing American Options by Simulation: A Simple Least-Squares Approach,” Review of Financial Studies 14, 113.
Luehrman, T., 2004, “Strategy as a Portfolio of Real Options,” Real Options and Investment Under Uncertainty: Classical Readings and Recent Contributions 385.
Mairesse, J., B. Hall, 1999, "Does cash flow cause investment and R&D: An exploration using panel data for French, Japanese, and United States scientific firms," Innovation, industry evolution, and employment: 129-156.
Majd, S., and R. Pindyck, 1989, “The Learning Curve and Optimal Production under Uncertainty,” RAND Journal of Economics 20, 331-343.
Margrabe, W.,1978,” The Value of an Option to Exchange One Asset for Another,” The Journal of Finance 33:1, 177-186.
Martzoukos, S., and L. Trigeorgis, 2002, “Real (investment) Options with Multiple Sources of Rare Events,” European journal of operational research 136, 696-706.
McDonald, R., and D. Siegel, 1986, “The Value of Waiting to Invest,” The Quarterly Journal of Economics 101, 707-727.
Merton, R., 1974, “On the Pricing of Corporate Debt: The Risk Structure of Interest Rates,” The Journal of Finance 29, 449-470.
Mikosch, T., 2006, “Copulas: Tales and Facts, with Discussion and Rejoinder,” Extremes 9, 3-62.
Nelsen, R., 2006, An Introduction to Copulas, Second Edition, Springer, U.S.A.
Paddock, J., D. Siegel, and J. Smith, 1988, “Option Valuation of Claims on Real assets: The Case of Offshore Petroleum Leases,” The Quarterly Journal of Economics 103, 479-508.
Panayi, S., and L. Trigeorgis, 1998, “Multi-Stage Real Options: The Cases of Information Technology Infrastructure and International Bank Expansion,” Quarterly Review of Economics and Finance 38, 675-692.
Patton, A., 2006a, Modelling Asymmetric Exchange Rate Dependence, International Economic Review 47, 527-556.
Patton, A., 2006b, “Estimation of Multivariate Models for Time Series of Possibly Different Lengths,” Journal of Applied Econometrics 21, 147-173.
Pitkethly, R., 1997, The Valuation of Patents: A Review of Patent Valuation Methods with Consideration of Option based Methods and the Potential for Further Research (University of Cambridge, Judge Institute of Management Studies).
Rosenberg, J., “Nonparametric Pricing of Multivariate Contingent Claims,” Stern School of Business Working Paper, S-99-35.
Ross, S., 1995, “Uses, Abuses, and Alternatives to the Net-Present-Value rule,” Financial Management 24, 96-102.
Schwartz, E., 2004, “Patents and R&D as Real Options,” Economic Notes 33, 23-54.
Schwartz, E., and M. Moon, 2001, “Rational Pricing of Internet Companies Revisited,” The Financial Reviews 36, 7-26.
Silvennoinen, A. and T. Teräsvirta, 2009, “Modeling Multivariate Autoregressive Conditional Heteroskedasticity with the Double Smooth Transition Conditional Correlation GARCH Model,” Journal of Financial Econometrics 7, 373-411.
Sklar, A., 1959, Fonctions de répartition à n dimensions et leurs marges, Publications de l’ Institut Statistique de l’Universite’ de Paris, 8, 229-231.
Smith, G.V., and R.L. Parr, 2004. Valuation of Intellectual Property and Intangible Assets (John Wiley & Sons Inc).
Trigeorgis, L., 1993, “Real Options and Interactions with Financial Flexibility,” Financial Management 22, 202-224.
Trigeorgis, L., 1996, Real options: Managerial flexibility and strategy in resource allocation, the MIT Press.
Trigeorgis, L., and S. Mason, 1987, “Valuing Managerial Flexibility,” Midland Corporate Finance Journal 5, 14-21.
Van den Goorbergh, R., C. Genest, 2005, "Bivariate option pricing using dynamic copula models," Insurance: Mathematics and Economics 37(1): 101-114.
Zhang, J. and D. Guegan, 2008, "Pricing bivariate option under GARCH processes with time-varying copula," Insurance: Mathematics and Economics 42(3): 1095-1103. |
| Description: | 博士
國立政治大學
財務管理研究所
88357504
98 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0883575041 |
| Data Type: | thesis |
| Appears in Collections: | [財務管理學系] 學位論文
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