American Put Option Pricing Using a Hybrid Evolutionary Computation and Monte-Carlo Simulation Method PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download American Put Option Pricing Using a Hybrid Evolutionary Computation and Monte-Carlo Simulation Method PDF full book. Access full book title American Put Option Pricing Using a Hybrid Evolutionary Computation and Monte-Carlo Simulation Method by Anjan Kumar Swain. Download full books in PDF and EPUB format.
Author: Anjan Kumar Swain
Publisher:
ISBN:
Category :
Languages : en
Pages : 17
Get Book Here
Book Description
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.
Author: Anjan Kumar Swain
Publisher:
ISBN:
Category :
Languages : en
Pages : 17
Get Book Here
Book Description
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.
Author: Alberto Barola
Publisher: LAP Lambert Academic Publishing
ISBN: 9783659352607
Category :
Languages : en
Pages : 160
Get Book Here
Book Description
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.
Author: Victoria Zhanna Averbukh
Publisher:
ISBN:
Category : Finansielle instrumenter
Languages : en
Pages : 138
Get Book Here
Book Description
Author: Mark Broadie
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
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.
Author: David Animante
Publisher:
ISBN:
Category :
Languages : en
Pages : 55
Get Book Here
Book Description
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.
Author: Diego Garcia
Publisher:
ISBN:
Category :
Languages : en
Pages : 132
Get Book Here
Book Description
Author: Mengliu Lu
Publisher:
ISBN:
Category :
Languages : en
Pages : 22
Get Book Here
Book Description
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.
Author: N.K. Chidambaran
Publisher:
ISBN:
Category :
Languages : en
Pages : 48
Get Book Here
Book Description
We propose a methodology of Genetic Programming to approximate the relationship between the option price, its contract terms and the properties of the underlying stock price. An important advantage of the Genetic Programming approach is that we can incorporate currently known formulas, such as the Black-Scholes model, in the search for the best approximation to the true pricing formula. Using Monte Carlo simulations, we show that the Genetic Programming model approximates the true solution better than the Black-Scholes model when stock prices folow a jump-diffusion process. We also show that the Genetic Programming model outperforms various other models in many different settings. Other advantages of the Genetic Programming approach include its robustness to changing environment, its low demand for data, and its computational speed. Since genetic programs are flexible, self-learning and sefl-improving, they are an ideal tool for practitioners.
Author: Junxiong Wang
Publisher:
ISBN:
Category :
Languages : en
Pages : 40
Get Book Here
Book Description
Abstract: This project is devoted primarily to the use of Monte Carlo methods to simulate stock prices in order to price European call options using control variates, and to the use of the binominal model to price American put options. At the end, we can use the information to form a portfolio position using an Interactive Brokers paper trading account. This project was done as a part of the masters capstone course Math 573: Computational Methods of Financial Mathematics.
Author: Lishang Jiang
Publisher: World Scientific Publishing Company
ISBN: 9813106557
Category : Business & Economics
Languages : en
Pages : 343
Get Book Here
Book Description
From the unique perspective of partial differential equations (PDE), this self-contained book presents a systematic, advanced introduction to the Black-Scholes-Merton's option pricing theory.A unified approach is used to model various types of option pricing as PDE problems, to derive pricing formulas as their solutions, and to design efficient algorithms from the numerical calculation of PDEs. In particular, the qualitative and quantitative analysis of American option pricing is treated based on free boundary problems, and the implied volatility as an inverse problem is solved in the optimal control framework of parabolic equations.
