Gea Carrasco, Cayetano and Isla Couso, Lorenzo (2010): A First Stochastic General Framework to Model the Project Finance Cash Flows under Monopolistic Situations.
Download (92kB) | Preview
The main aim of this work is to model the cash flows and cost dynamics for a Project Finance. Large scale capital-intensive projects usually require substantial investments up front and only generate revenues to cover their costs in the long term.
The abandonment flexibility affects each project independently.
This is the only one that we consider in this study and it is quite different from the idea to abandon due to a common (specific) catastrophic event.
This option is exercised under those situations of expected costs to completion higher than the expected cash flow, that is, during the investment period in the development phase. Including this flexibility in project finance is the same as valuing a project with an implicit American put option.
|Item Type:||MPRA Paper|
|Original Title:||A First Stochastic General Framework to Model the Project Finance Cash Flows under Monopolistic Situations|
|English Title:||A First Stochastic General Framework to Model the Project Finance Cash Flows under Monopolistic Situations|
|Keywords:||Project Finance, Cash Flows, Stochastic, Real Options|
|Subjects:||G - Financial Economics > G2 - Financial Institutions and Services|
|Depositing User:||Cayetano Gea|
|Date Deposited:||11. Dec 2010 02:46|
|Last Modified:||11. Feb 2013 20:31|
 Caperaa and Genest: A nonparametric estimation procedure for bivariate extreme value copulas. Biometrika, Sept. 1997, vol. 84.
 Chan Chiou: Multivariate Continuous Time Models through copulas(2004).
 Duffie, D. and K. Singleton (1999): “Simulating Correlated Defaults”, Working Paper, Graduate School of Business, Stanford University.
 Duffie, D. and K. Singleton (2000): “Simulating Correlated Defaults. A revision”, Working Paper, Graduate School of Business, Stanford University.
 Devroye, L.: Non uniform random variables generation.Mc Graw Hill. Chapter 10, pages 548-558.
 Embechts, McNeil and Straumann (1999): Correlation and dependence in risk management. Cambridge University Press.
 Embrechts, Frey and McNeil (2001): Quantitative Risk Management. Princeton University Press.
 Frees, E. W. and E. A. Valdez: Understanding relationship using copulas. North American Actuarial Journal. 2(1):1-25 (1998).
 Genest, Christian: Statistical inference procedure for Archimedean copula models. Journal of American Statistical Association. Vol. 88, September 1993.
 Kole, Eric: Testing copulas to model financial dependence. Erasmus University, Rotterdam.
 León and Piñero (2004): Valuation of a biotech company: A real option approach. Cemfi Working Paper 0420.
Marshall and Olkin (1988): Families of Multivariate Distributions. Journal of the American Statistical Association.
 Nelsen (1999): An introduction to copulas. Lectures Notes in Statistics. Risk Conference. New York.
 Nolan, J. Stable Distributions: Models for heavy tailed Data. American University Working Paper (1994).
 Roncalli. Copulas in Finance: A reading guide and some applications. Groupe de Recherche Opérationnelle. Crédit Lyonnais. France.
 Shang Chan Chiou: Multivariate Continuous Time models through copulas. Berkeley Working Paper.
 Schwartz (2004): “Patents and R&D as Real Options” Economic Notes 33, pp. 23-54.
 Schombücher (2004): Modelling Dynamic portfolio credit risk.Department of Mathematics. Imperial College, London.
 Schömbucher: Correlated defaults. Conference on credit risk at Salomon center, NY, May 2004.
 Whelan, Ian: Quantitative Finance. Sampling from Archimedean Copulas (2003).
 Yu, Fan: Default correlations in reduced form models. California University working paper.