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American Style Option Pricing And Improvement Of Regression Based Monte Carlo Methods By Machine Learning Techniques
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Book Synopsis American-style Option Pricing and Improvement of Regression-based Monte Carlo Methods by Machine Learning Techniques by : Songyin Tang
Download or read book American-style Option Pricing and Improvement of Regression-based Monte Carlo Methods by Machine Learning Techniques written by Songyin Tang and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Monte Carlo Methods for American Option Pricing by : Alberto Barola
Download or read book Monte Carlo Methods for American Option Pricing written by Alberto Barola and published by LAP Lambert Academic Publishing. This book was released on 2014-05-21 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Monte Carlo approach has proved to be a valuable and flexible computational tool in modern finance. A number of Monte Carlo simulation-based methods have been developed within the past years to address the American option pricing problem. The aim of this book is to present and analyze three famous simulation algorithms for pricing American style derivatives: the stochastic tree; the stochastic mesh and the least squares method (LSM). The author first presents the mathematical descriptions underlying these numerical methods. Then the selected algorithms are tested on a common set of problems in order to assess the strengths and weaknesses of each approach as a function of the problem characteristics. The results are compared and discussed on the basis of estimates precision and computation time. Overall the simulation framework seems to work considerably well in valuing American style derivative securities. When multi-dimensional problems are considered, simulation based methods seem to be the best solution to estimate prices since the general numerical procedures of finite difference and binomial trees become impractical in these specific situations.
Book Synopsis American Option Pricing by : Garrett G. Smith
Download or read book American Option Pricing written by Garrett G. Smith and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This study examines methods of pricing American style options, moving from the binomial model to the Black Scholes method and finishing with simulated method of option pricing. A simulated approached is based off the work established by Longstaff and Schwartz (2001) and extended by Rambharat and Brockwell (2010). Downfalls of these methods are discussed, as are ways to improve upon them. Using Monte Carlo methods and particle filtering will lead to a platform where options are priced with greater detail. Also, these simulated methods lead to faster computing time allowing for a more efficient use of resources and a theoretical framework of pricing.
Book Synopsis A Monte Carlo Method for Pricing American Options by : Diego Garcia
Download or read book A Monte Carlo Method for Pricing American Options written by Diego Garcia and published by . This book was released on 1999 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Valuation of American Options by : David Animante
Download or read book Valuation of American Options written by David Animante and published by . This book was released on 2016 with total page 55 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of American style equity options as hedging instrument has gained currency in recent times. This phenomenon devolves from the ever-expanding need by individuals, corporations and governments to hedge away their financial risks and the clarion call for derivative securities that give the holder increased flexibility in exercise. Nevertheless, pricing American options is complex and there exists no analytic solution to the problem except a profusion of approximation and finite difference techniques. Indeed, many researchers have shown that these methods cannot handle multifactor situations where the underlying asset follows a jump-diffusion process and where the derivative security depends on multiple sources of uncertainty such as stochastic volatility, stochastic interest rate among others. Monte-Carlo simulation techniques therefore developed out of the search for a pricing formula that has the capacity to accommodate all forms of uncertainty and at the same time able to produce speedy and accurate results. Some scholars at first rejected these techniques as yielding inaccurate results but in recent times, many researchers have demonstrated the efficacy of Monte-Carlo simulation in option pricing. The aim of this study is to assess the effectiveness of Monte-Carlo simulation methods in comparison with other option pricing techniques. To achieve this objective, the research builds an algorithm to compute Call and Put prices based on a wide range of input parameters. It also develops a model where volatility or interest rate is stochastic and a deterministic function of time. The results indicate that Monte-Carlo simulation techniques produce option values and exercise boundaries that are very similar to the Binomial, Barone-Adesi and Whaley as well as the Explicit Finite Difference methods. The results also show that the stochastic volatility and stochastic interest rate models yield slightly different but more accurate results. Consequently, the study recommends simulation techniques that incorporate multiple sources of uncertainty simultaneously for fast, efficient and more accurate option pricing.
Book Synopsis Handbook in Monte Carlo Simulation by : Paolo Brandimarte
Download or read book Handbook in Monte Carlo Simulation written by Paolo Brandimarte and published by John Wiley & Sons. This book was released on 2014-06-20 with total page 620 pages. Available in PDF, EPUB and Kindle. Book excerpt: An accessible treatment of Monte Carlo methods, techniques, and applications in the field of finance and economics Providing readers with an in-depth and comprehensive guide, the Handbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics presents a timely account of the applicationsof Monte Carlo methods in financial engineering and economics. Written by an international leading expert in thefield, the handbook illustrates the challenges confronting present-day financial practitioners and provides various applicationsof Monte Carlo techniques to answer these issues. The book is organized into five parts: introduction andmotivation; input analysis, modeling, and estimation; random variate and sample path generation; output analysisand variance reduction; and applications ranging from option pricing and risk management to optimization. The Handbook in Monte Carlo Simulation features: An introductory section for basic material on stochastic modeling and estimation aimed at readers who may need a summary or review of the essentials Carefully crafted examples in order to spot potential pitfalls and drawbacks of each approach An accessible treatment of advanced topics such as low-discrepancy sequences, stochastic optimization, dynamic programming, risk measures, and Markov chain Monte Carlo methods Numerous pieces of R code used to illustrate fundamental ideas in concrete terms and encourage experimentation The Handbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics is a complete reference for practitioners in the fields of finance, business, applied statistics, econometrics, and engineering, as well as a supplement for MBA and graduate-level courses on Monte Carlo methods and simulation.
Book Synopsis Monte Carlo Simulation with Machine Learning for Pricing American Options and Convertible Bonds by : Bella Dubrov
Download or read book Monte Carlo Simulation with Machine Learning for Pricing American Options and Convertible Bonds written by Bella Dubrov and published by . This book was released on 2015 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt: Li, Szepesvari and Schuurmans (2009) show that reinforcement learning (RL) algorithms are superior to the classical methods (such as Longstaff and Schwartz (2001)) in pricing American options using Monte Carlo simulation. We extend their techniques to the problem of pricing convertible bonds and show that RL outperforms LS on this task. Additionally, we propose a new method, based on the random forest algorithm from machine learning [Breiman (2001)], that can be used for pricing both American options and convertible bonds with Monte Carlo simulation. We show that this algorithm outperforms LS and is also superior to RL in most cases. We demonstrate how to use Monte Carlo simulation with the methods described above for pricing a complex convertible bond trading at the Tel Aviv stock exchange. Like many Israeli convertibles, this bond exhibits the "gradually diminishing principal" feature, meaning that instead of one payment of the principal at maturity, there are multiple principal payments during the lifetime of the bond. This feature presents a challenge to existing models. We also model other exotic features of this bond, such as path-dependent conversion ratio and exchange rate indexation. The prices that we obtain using this model are close to the market prices of the bond.
Book Synopsis Monte Carlo Techniques for Pricing American Style Option [sic] by : Su Pan
Download or read book Monte Carlo Techniques for Pricing American Style Option [sic] written by Su Pan and published by . This book was released on 2005 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Pricing American-style Options by Monte Carlo Method by : Chi Yan Wong (Ph. D.)
Download or read book Pricing American-style Options by Monte Carlo Method written by Chi Yan Wong (Ph. D.) and published by . This book was released on 2002 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis American Put Option Pricing Using a Hybrid Evolutionary Computation and Monte-Carlo Simulation Method by : Anjan Kumar Swain
Download or read book American Put Option Pricing Using a Hybrid Evolutionary Computation and Monte-Carlo Simulation Method written by Anjan Kumar Swain and published by . This book was released on 2017 with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt: American put option pricing is a challenging, complex problem, and existing methods to address this problem are computationally intensive. In this paper, a self-adaptive evolutionary computation method is used for computing American put option price. The proposed method essentially transforms a discrete time exercisable American option to a continuous time exercisable option. The performance of the proposed method is compared with that of plain European Monte Carlo and Binomial Lattice option values. Further, in pricing American options this method exhibited better results with considerable improvements over that of conventional Monte-Carlo simulation method. It is argued that the proposed method effectively computes the upper bound on the American put options.
Book Synopsis Efficient Pricing of High Dimensional American Style Derivatives by : Christian Jonen
Download or read book Efficient Pricing of High Dimensional American Style Derivatives written by Christian Jonen and published by . This book was released on 2011 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Option Pricing Using Monte Carlo Methods by : Mengliu Lu
Download or read book Option Pricing Using Monte Carlo Methods written by Mengliu Lu and published by . This book was released on 2011 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: This paper aims to use Monte Carlo methods to price American call options on equities using the variance reduction technique of control variates and to price American put options using the binomial model. We use this information to form option positions. This project was done a part of the masters capstone course Math 573: Computational Methods of Financial Mathematics.
Book Synopsis Valuing American Style Option by Quasi-Monte Carlo Simulation by : To Wang Ng
Download or read book Valuing American Style Option by Quasi-Monte Carlo Simulation written by To Wang Ng and published by . This book was released on 2012 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis American-Asian Option Pricing Based on Monte Carlo Simulation Method by : Shiguang Han
Download or read book American-Asian Option Pricing Based on Monte Carlo Simulation Method written by Shiguang Han and published by . This book was released on 2012 with total page 65 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis On Improving the Least Squares Monte Carlo Option Valuation Method by : Nelson Areal
Download or read book On Improving the Least Squares Monte Carlo Option Valuation Method written by Nelson Areal and published by . This book was released on 2018 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Enhanced Monte Carlo Estimates for American Option Prices by : Mark Broadie
Download or read book Enhanced Monte Carlo Estimates for American Option Prices written by Mark Broadie and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Monte Carlo simulation has trouble with American options because the exercise decision at a given date must compare the option's immediate exercise value against its continuation value. The option value if it is not exercised is a function of its value along all possible future price paths from that point on, and each path will present further exercise decisions with the same difficulty in resolving them. The authors propose a hybrid valuation technique that bridges Monte Carlo simulation and lattice methods. Instead of simulating price paths, they simulate whole price trees. The tree emanating from each point is used to assess the option continuation value for that date and stock price. While the results are accurate, inevitably the procedure requires a large number of computations. The authors then offer a variety of techniques that substantially increase efficiency.
Book Synopsis Simulation-based Valuation and Counterparty Exposure Estimation of American Options by : Kin Hung Kan
Download or read book Simulation-based Valuation and Counterparty Exposure Estimation of American Options written by Kin Hung Kan and published by . This book was released on 2010 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: Valuing American options is a central problem in option pricing since the early-exercise feature is very common among financial or insurance derivatives products. For high-dimensional American options, Monte Carlo simulation is generally regarded as the only viable approach to price them, and this is the focus of our work. We propose a new regression-based Monte Carlo algorithm for pricing American options. This method typically generates an upper bound of the option value. It is computationally efficient and generates accurate price estimates. To improve the convergence rate, we apply a bias reduction technique to the least-squares Monte Carlo estimators of American option value. It works by subtracting a bias approximation from the original option value estimators at each exercise opportunity. The bias approximation is derived using large sample properties of the least-squares regression estimators. The resulting expression is easy to evaluate, and is applicable to any payoff structures and underlying processes. Numerical results show that this technique can significantly reduce the bias. However, it introduces non-negligible computational costs, thus careful treatment is required when it is adopted in practice. Finally, we extend the least-squares Monte Carlo algorithm to estimate the counterparty exposures of American options. The new algorithm is termed optimized least-squares Monte Carlo (OLSM), which is combined with variance reduction techniques, initial state dispersion and multiple bucketing to enhance its performance. The biggest advantage of OLSM is that it avoids nested simulations, allowing for the computation of risk measures on various time horizons under a reasonable computational budget.
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