The Valuation of Real Options with the Least Squares Monte Carlo Simulation Method PDF Download
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Author: Artur Rodrigues
Publisher:
ISBN:
Category :
Languages : en
Pages : 49
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Book Description
This paper provides a detailed analysis of the Least Squares Monte Carlo Simulation Method (Longstaff and Schwartz, 2001) and of the extension of Gamba (2003) to value portfolios of real options. The accuracy of the method is assessed when valuing stylised real options as maximum, compound or mutually exclusive options. For the latter, we propose an improved algorithm that is faster, more accurate as well as more reliable. The analysis is carried out for a large number of call and put options. It is done comparing alternative polynomial families and simulation methods, including moment matching techniques and low-discrepancy sequences. Unlike previous analysis of the method, our results suggest that the use of weighted Laguerre polynomials, initially proposed by Longstaff and Schwartz (2001), produces more accurate estimates. We show also that the choice of the best simulation method is contingent on the problem in hand. Low-discrepancy sequences tend to produce more accurate estimates, using fewer paths than pseudo-random numbers. The accuracy of the method depends on the payoff function and seems to converge, increasing both the number of basis and the number of simulated paths.
Author: Artur Rodrigues
Publisher:
ISBN:
Category :
Languages : en
Pages : 49
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Book Description
This paper provides a detailed analysis of the Least Squares Monte Carlo Simulation Method (Longstaff and Schwartz, 2001) and of the extension of Gamba (2003) to value portfolios of real options. The accuracy of the method is assessed when valuing stylised real options as maximum, compound or mutually exclusive options. For the latter, we propose an improved algorithm that is faster, more accurate as well as more reliable. The analysis is carried out for a large number of call and put options. It is done comparing alternative polynomial families and simulation methods, including moment matching techniques and low-discrepancy sequences. Unlike previous analysis of the method, our results suggest that the use of weighted Laguerre polynomials, initially proposed by Longstaff and Schwartz (2001), produces more accurate estimates. We show also that the choice of the best simulation method is contingent on the problem in hand. Low-discrepancy sequences tend to produce more accurate estimates, using fewer paths than pseudo-random numbers. The accuracy of the method depends on the payoff function and seems to converge, increasing both the number of basis and the number of simulated paths.
Author:
Publisher:
ISBN:
Category :
Languages : pt-BR
Pages :
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Book Description
O presente trabalho tem como objetivo testar empiricamente a eficiência e a aplicabilidade do método dos mínimos quadrados de Monte Carlo (LSM) na avaliação de projetos envolvendo opções reais. Inicialmente, o método passoupor uma série de testes de sensibilidade para validação do mesmo. Em seguida, alguns exemplos de projetos de exploração e produção (E & P) de petróleo com opções reais foram elaborados, e seus valores determinados através do LSM. Estes resultados foram comparados aos resultados obtidos com o modelo binomial que, devido a sua simplicidade e ampla utilização, foi escolhido comobenchmark para analisar a eficiência do método LSM. Devido às semelhanças entre oportunidades de investimento em ativos financeiros e reais, muitos estudos são realizados no sentido de adaptar instrumentos financeiros para a avaliação econômica de projetos. Muitas pesquisas sobre opções reais foram desenvolvidas em exploração de recursosnaturais, em especial de E & P de petróleo. Isso ocorre devido ao porte dos investimentos que são realizados neste setor e as suas características peculiares: o mercado de petróleo é bem desenvolvido (presença de mercado futuro, instrumentos de proteção financeira, derivativos etc); os investimentos ocorrem num ambiente de incertezas econômicas e / ou técnicas; os projetos demandam uma série de flexibilidades gerenciais (prazos alternativos paraexecução dos investimentos, possibilidade de mudanças na escala do projeto, entre outras). Tais características fazem com que seja necessária uma avaliação mais cautelosa e criteriosa destes ativos reais. Uma nova ferramentadesenvolvida neste sentido é o método LSM, que consiste na avaliação de opções americanas através de simulações e de regressões simples.
Author: Andrea Gamba
Publisher:
ISBN:
Category :
Languages : en
Pages : 71
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Book Description
This paper provides a numerical approach based on a Monte Carlo simulation for valuing dynamic capital budgeting problems with many embedded real options dependent on numerous state variables. We propose a way of decomposing a complex capital budgeting problem with many options into a set of simple options, suitably accounting for interaction and interdependence among them. The decomposition approach is numerically implemented using an extension of the Least Squares Monte Carlo algorithm, presented by Longstaff and Schwartz (2001) applied to our multi-option setting. We also provide a number of applications of our approach to well-known real options models and real life capital budgeting problems. Moreover, we present a set of numerical experiments to provide evidence for the accuracy of the proposed methodology.
Author: Jason B. Legrand
Publisher:
ISBN: 9781613243305
Category : Real options (Finance)
Languages : en
Pages : 0
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Book Description
The real option methodology is the generalized approach in corporate financial decision-making, investing and valuation. Traditionally, real option models reflect stochastic underlying processes and flexibility. In this book, the authors present topical research in the study of real options analysis, including the valuation of oil and gas reserves using cointegrated prices and least-squares Monte Carlo; flexibility in sequential investment and catastrophic risk; the survey of fuzzy-stochastic real option models and their application and possibilities and optimal control and real options models..
Author: Johnathan Mun
Publisher: John Wiley & Sons
ISBN: 0471465348
Category : Business & Economics
Languages : en
Pages : 321
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Book Description
Praise for Real Options Analysis Course "Dr. Mun's latest book is a logical extension of the theory and application presented in Real Options Analysis. More specifically, the Real Options Analysis Course presents numerous real options examples and provides the reader with step-by-step problem-solving techniques. After having read the book, readers will better understand the underlying theory and the opportunities for applying real option theory in corporate decision-making." -Chris D. Treharne, President, Gibraltar Business Appraisals, Inc. "This text provides an excellent follow up to Dr. Mun's first book, Real Options Analysis. The cases in Real Options Analysis Course provide numerous examples of how the use of real options and the Real Options Analysis Toolkit software can assist in the valuation of strategic and managerial flexibility in a variety of arenas." -Charles T. Hardy, PhD, Chief Financial Officer & Director of Business Development, Panorama Research, Inc. "Most of us come to real options from the perspective of our own areas of expertise. Mun's great skill with this book is in making real options analysis understandable, relevant, and immediately applicable to the field within which you are working." -Robert Fourt, Partner, Gerald Eve (UK) "Mun provides a practical step-by-step guide to applying simulation and real options analysis-invaluable to those of us who are no longer satisfied with conventional valuation approaches alone." -Fred Kohli, Head of Portfolio Management, Syngenta Crop Protection Ltd. (Switzerland)
Author: Eduardo S. Schwartz
Publisher: MIT Press
ISBN: 9780262693189
Category : Business & Economics
Languages : en
Pages : 890
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Book Description
The study of investment under uncertainty was stagnant for several decades until developments in real options revitalized the field. The topics covered in this book include the reasons behind the under-investment programme.
Author: Nelson Areal
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Chen and Shen (2003) argue that it is possible to improve the Least Squares Monte Carlo Method (LSMC) of Longstaff and Schwartz (2001) to value American options by removing the least squares regression module. This would make not only faster but also more accurate. We demonstrate, using a large sample of 2500 put options that the proposed algorithm - the Perfect Foresight Method (PFM) - is, as argued by the authors, faster than the LSMC algorithm but, contrary to what they state, it is not more accurate than the LSMC. In fact, the PFM algorithm incorrectly prices American options, resulting in an upward biased value of the option. We therefore, do not recommend the use of the PFM.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Author: Nelson Areal
Publisher:
ISBN:
Category :
Languages : en
Pages : 36
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Book Description
This paper studies various possible approaches to improving the least squares Monte Carlo option valuation method. We test different regression algorithms and suggest a variation to estimating the option continuation value, which can reduce the execution time of the algorithm by one third. We test the choice of varying polynomial families with different number of basis functions. We compare several variance reduction techniques, and find that using low discrepancy sequences can improve the accuracy up to four times. We also extend our analysis to compound and mutually exclusive options. For the latter, we propose an improved algorithm which is faster and more accurate.
Author: Philipp N. Baecker
Publisher: Springer Science & Business Media
ISBN: 3540482644
Category : Business & Economics
Languages : en
Pages : 279
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Book Description
This book proposes an integrated approach to patent risk and capital budgeting in pharmaceutical research and development (R and D), developing an option-based view (OBV) of imperfect patent protection, which draws upon contingent-claims analysis, stochastic game theory, as well as novel numerical methods. The text re-initiates a discussion about the contribution of quantitative frameworks to value-based R and D management.
