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Enhancing Least Squares Monte Carlo With Diffusion Bridges
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Book Synopsis Enhancing Least Squares Monte Carlo with Diffusion Bridges by : Tommaso Pellegrino
Download or read book Enhancing Least Squares Monte Carlo with Diffusion Bridges written by Tommaso Pellegrino and published by . This book was released on 2015 with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this study is to present an efficient and easy framework for the application of the Least Squares Monte Carlo methodology to the pricing of gas or power facilities as detailed in Boogert and de Jong. As mentioned in the seminal paper by Longstaff and Schwartz, the convergence of the Least Squares Monte Carlo depends on the convergence of the optimization combined with the convergence of the pure Monte Carlo method. In the context of the energy facilities, the convergence of the algorithm is more challenging in particular for the computation of sensitivities and optimal dispatched quantities. To our knowledge, an extensive study of the convergence and hence, of the reliability of the algorithm has not been performed yet, in our opinion because of the apparent infeasibility and complexity to use a very high number of simulations. We present then an easy way to simulate random trajectories by means of diffusion bridges similar to the one proposed by Kutt and Welke that is equivalent to generate a time reversal Itō diffusion. Our approach permits to perform a backward dynamic programming strategy based on a huge number of simulations without storing the whole simulated trajectory.Generally, in the valuation of energy facilities one is also interested in the forward recursion. We then design the backward and forward recursions algorithm such that one can produce the same random trajectories by the use of multiple independent random streams without storing at intermediate time steps. Finally, we show the advantages of our methodology for the valuation of virtual hydro power plants and gas storages.
Book Synopsis Improving the Least-squares Monte-Carlo Approach by : Nicki Søndergaard Rasmussen
Download or read book Improving the Least-squares Monte-Carlo Approach written by Nicki Søndergaard Rasmussen and published by . This book was released on 2002 with total page 65 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Monte Carlo Methods by : Adrian Barbu
Download or read book Monte Carlo Methods written by Adrian Barbu and published by Springer Nature. This book was released on 2020-02-24 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book seeks to bridge the gap between statistics and computer science. It provides an overview of Monte Carlo methods, including Sequential Monte Carlo, Markov Chain Monte Carlo, Metropolis-Hastings, Gibbs Sampler, Cluster Sampling, Data Driven MCMC, Stochastic Gradient descent, Langevin Monte Carlo, Hamiltonian Monte Carlo, and energy landscape mapping. Due to its comprehensive nature, the book is suitable for developing and teaching graduate courses on Monte Carlo methods. To facilitate learning, each chapter includes several representative application examples from various fields. The book pursues two main goals: (1) It introduces researchers to applying Monte Carlo methods to broader problems in areas such as Computer Vision, Computer Graphics, Machine Learning, Robotics, Artificial Intelligence, etc.; and (2) it makes it easier for scientists and engineers working in these areas to employ Monte Carlo methods to enhance their research.
Book Synopsis Approving Least Squares Monte Carlo Approach for Valuing American Options by : Lei Zhang
Download or read book Approving Least Squares Monte Carlo Approach for Valuing American Options written by Lei Zhang and published by . This book was released on 2004 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Least Squares Monte Carlo for Backward Stochastic Differential Equations by : David Stahl
Download or read book Least Squares Monte Carlo for Backward Stochastic Differential Equations written by David Stahl and published by . This book was released on 2012 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Monte Carlo Study of Least Squares Parameter Estimation for the Learning Curve Equation by : Charles A. Thomas (CAPT, USAF.)
Download or read book A Monte Carlo Study of Least Squares Parameter Estimation for the Learning Curve Equation written by Charles A. Thomas (CAPT, USAF.) and published by . This book was released on 1975 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Least-squares Monte Carlo for Backward SDEs by : Christian Bender
Download or read book Least-squares Monte Carlo for Backward SDEs written by Christian Bender and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Apply the Least Squares Monte Carlo Approach to Extremely Complex Options: A Building Block Methodology by :
Download or read book Apply the Least Squares Monte Carlo Approach to Extremely Complex Options: A Building Block Methodology written by and published by . This book was released on 2006 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Practical Policy Iteration by : Christopher Beveridge
Download or read book Practical Policy Iteration written by Christopher Beveridge and published by . This book was released on 2013 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: We introduce a set of improvements which allow the calculation of very tight lower bounds for Bermudan derivatives using Monte Carlo simulation. These lower bounds can be computed quickly, and with minimal hand-crafting. Our focus is on accelerating policy iteration to the point where it can be used in similar computation times to the basic least-squares approach, but in doing so introduce a number of improvements which can be applied to both the least-squares approach and the calculation of upper bounds using the Andersen-Broadie method. The enhancements to the least-squares method improve both accuracy and efficiency.Results are provided for the displaced-diffusion LIBOR market model, demonstrating that our practical policy iteration algorithm can be used to obtain tight lower bounds for cancellable CMS steepener, snowball and vanilla swaps in similar times to the basic least-squares method.
Book Synopsis Comparison of Least Squares Monte Carlo Methods with Applications to Energy Real Options by : Selvaprabu Nadarajah
Download or read book Comparison of Least Squares Monte Carlo Methods with Applications to Energy Real Options written by Selvaprabu Nadarajah and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Least squares Monte Carlo (LSM) is a state-of-the-art approximate dynamic programming approach used in financial engineering and real options to value and manage options with early or multiple exercise opportunities. It is also applicable to capacity investment and inventory/production management problems with demand/supply forecast updates arising in operations and hydropower-reservoir management. LSM has two variants, referred to as regress-now/later (LSMN/L), which compute continuation/value function approximations (C/VFAs). We provide novel numerical evidence for the relative performance of these methods applied to energy swing and storage options, two typical real options, using a common price evolution model. LSMN/L estimate C/VFAs that yield equally accurate (near optimal) and precise lower and dual (upper) bounds on the value of these real options. Estimating the LSMN/L C/VFAs and their associated lower bounds takes similar computational effort. In contrast, the estimation of a dual bound using the LSML VFA instead of the LSMN CFA takes seconds rather than minutes or hours. This finding suggests the use of LSML in lieu of LSMN when estimating dual bounds on the value of early or multiple exercise options, as well as of related capacity investment and inventory/production policies.
Book Synopsis Least Squares Under Heteroskedasticity by : Samar Abbas Lotia
Download or read book Least Squares Under Heteroskedasticity written by Samar Abbas Lotia and published by . This book was released on 1996 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Accelerating the Least-squares Monte Carlo Methodwith Parallel Computing by : 陳鏡文
Download or read book Accelerating the Least-squares Monte Carlo Methodwith Parallel Computing written by 陳鏡文 and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Monte Carlo study of two robust alternatives to least squares regression estimation by :
Download or read book A Monte Carlo study of two robust alternatives to least squares regression estimation written by and published by . This book was released on 1974 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Monte Carlo Study of Orthogonal Least Squares Estimators by : Suat-Hun Go
Download or read book A Monte Carlo Study of Orthogonal Least Squares Estimators written by Suat-Hun Go and published by . This book was released on 1970 with total page 58 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Monte Carlo Simulation for Functionals of Diffusion Processes by : Basant Pangeni
Download or read book Monte Carlo Simulation for Functionals of Diffusion Processes written by Basant Pangeni and published by . This book was released on 2003 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Monte Carlo Comparison of Two-square Least Squares and Ridge Estimators in a Simultaneous Equation Model by : Michael Regan Quigley
Download or read book A Monte Carlo Comparison of Two-square Least Squares and Ridge Estimators in a Simultaneous Equation Model written by Michael Regan Quigley and published by . This book was released on 1988 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Least Squares Monte Carlo and Approximate Linear Programming by : Selvaprabu Nadarajah
Download or read book Least Squares Monte Carlo and Approximate Linear Programming written by Selvaprabu Nadarajah and published by . This book was released on 2018 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: Least squares Monte Carlo (LSM) is an approximate dynamic programming (ADP) technique commonly used for the valuation of high dimensional financial and real options, but has broader applicability. It is known that the regress-later version of this method is an approximate linear programming (ALP) relaxation that implicitly provides a potential solution to a familiar ALP deficiency. Focusing on a generic finite horizon Markov decision process, we provide both theoretical and numerical backing for the usefulness of this solution, respectively using a worst-case error bound analysis and a numerical study dealing with merchant ethanol production, an energy real option application, based on an ALP heuristic that we propose. When both methodologies are applicable, our research supports the use of regress-later LSM rather than this ALP technique to approximately solve intractable Markov decision processes. Our numerical findings motivate additional research to obtain even better methods than the regress-later version of LSM.
