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Author : David Anderson
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (129 download)
Download or read book Accelerated American Option Pricing with Deep Neural Networks written by David Anderson and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Alan White
Publisher : Routledge
ISBN 13 : 113568782X
Total Pages : 225 pages
Book Rating : 4.1/5 (356 download)
Download or read book Pricing Options with Futures-Style Margining written by Alan White and published by Routledge. This book was released on 2014-02-04 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines the applicability of a relatively new and powerful tool, genetic adaptive neural networks, to the field of option valuation. A genetic adaptive neural network model is developed to price option contracts with futures-style margining. This model is capable of estimating complex, non-linear relationships without having prior knowledge of the specific nature of the relationships. Traditional option pricing models require that the researcher or practitioner specify the distribution of the underlying asset. In addition, the methodology is able to easily accommodate additional inputs(something that cannot be preformed with existing models. Since 1973, options on stock have been traded on organized exchanges in the United States. An option on a stock gives the option owner the right to buy or sell the stock for a pre-set price.. Since the introduction of stock options, the options market has experienced tremendous growth and has spawned even more exotic types of derivative securities. Obviously, valuing these securities is an issue of great importance to investors and hedgers in the financial marketplace. Existing pricing models produce systematic pricing errors and new models have to be developed for options with differing characteristics. The genetic adaptive neural network is found to provide more accurate valuation than a traditional option pricing model when applied to the 3-month Eurodollar futures-option contract traded on the London International Financial Futures and Options Exchange.
Author : Ming Liu
Publisher :
ISBN 13 :
Total Pages : 80 pages
Book Rating : 4.:/5 (345 download)
Download or read book Option Pricing with Neural Networks written by Ming Liu and published by . This book was released on 1995 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : ALAN. WHITE
Publisher :
ISBN 13 : 9781000526721
Total Pages : pages
Book Rating : 4.5/5 (267 download)
Download or read book PRICING OPTIONS WITH FUTURES-STYLE MARGINING written by ALAN. WHITE and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: First Published in 2000. In 1973, options on stock became available on an organized exchange when the Chicago Board of Trade created the Chicago Board Options Exchange (CBOE). Options existed prior to this time, but the contracts lacked standardization and a central exchange. Since that introduction, the options market has experienced tremendous growth and has spawned even more exotic types of derivative securities. Although a great deal of work has been done in the area of option pricing, there still exists a number of problems related to estimating or predicting option prices. The purpose of this study is to utilize Genetic Adaptive Neural Networks (GANNs) to develop a method of pricing futures options with futures-style margining.
Author : Carl Chiarella
Publisher : World Scientific
ISBN 13 : 9814452629
Total Pages : 223 pages
Book Rating : 4.8/5 (144 download)
Download or read book The Numerical Solution of the American Option Pricing Problem written by Carl Chiarella and published by World Scientific. This book was released on 2014-10-14 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: The early exercise opportunity of an American option makes it challenging to price and an array of approaches have been proposed in the vast literature on this topic. In The Numerical Solution of the American Option Pricing Problem, Carl Chiarella, Boda Kang and Gunter Meyer focus on two numerical approaches that have proved useful for finding all prices, hedge ratios and early exercise boundaries of an American option. One is a finite difference approach which is based on the numerical solution of the partial differential equations with the free boundary problem arising in American option pricing, including the method of lines, the component wise splitting and the finite difference with PSOR. The other approach is the integral transform approach which includes Fourier or Fourier Cosine transforms. Written in a concise and systematic manner, Chiarella, Kang and Meyer explain and demonstrate the advantages and limitations of each of them based on their and their co-workers'' experiences with these approaches over the years. Contents: Introduction; The Merton and Heston Model for a Call; American Call Options under Jump-Diffusion Processes; American Option Prices under Stochastic Volatility and Jump-Diffusion Dynamics OCo The Transform Approach; Representation and Numerical Approximation of American Option Prices under Heston; Fourier Cosine Expansion Approach; A Numerical Approach to Pricing American Call Options under SVJD; Conclusion; Bibliography; Index; About the Authors. Readership: Post-graduates/ Researchers in finance and applied mathematics with interest in numerical methods for American option pricing; mathematicians/physicists doing applied research in option pricing. Key Features: Complete discussion of different numerical methods for American options; Able to handle stochastic volatility and/or jump diffusion dynamics; Able to produce hedge ratios efficiently and accurately"
Author : Zaheer Ahmed Kindar
Publisher :
ISBN 13 :
Total Pages : 176 pages
Book Rating : 4.:/5 (775 download)
Download or read book Artificial Neural Networks Applied to Option Pricing written by Zaheer Ahmed Kindar and published by . This book was released on 2004 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Michael Curran
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (129 download)
Download or read book Accelerating American Option Pricing in Lattices written by Michael Curran and published by . This book was released on 1998 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This article describes a method of accelerating the pricing of American options in binomial lattices in a Black-Scholes environment. The standard backward induction method for solving an option valuation problem involves computations at every node of the binomial option price tree. We show that many of the intermediate calculations are actually unnecessary, and eliminating them leads to a dramatic increase in computational efficiency. Test cases demonstrate that valuing an American put option can be accelerated by at least an order of magnitude, while yielding the identical estimate given by the standard Cox, Ross, and Rubinstein binomial tree. In addition, we discuss how similar techniques may be applied to pricing American options on interest rate derivatives and options involving multiple assets.
Author : Joachim Tobias Hahn
Publisher :
ISBN 13 :
Total Pages : 178 pages
Book Rating : 4.:/5 (13 download)
Download or read book Option Pricing Using Artificial Neural Networks written by Joachim Tobias Hahn and published by . This book was released on 2013 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Mike Curran
Publisher :
ISBN 13 :
Total Pages : 11 pages
Book Rating : 4.:/5 (13 download)
Download or read book Accelerating American Option Pricing in Lattices written by Mike Curran and published by . This book was released on 2015 with total page 11 pages. Available in PDF, EPUB and Kindle. Book excerpt: This article describes a method of accelerating the pricing of American options in binomial lattices in a Black-Scholes environment. The standard backward induction method for solving an option valuation problem involves computations at every node of the binomial option price tree. We show that many of the intermediate calculations are actually unnecessary, and eliminating them leads to a dramatic increase in computational efficiency.Test cases demonstrate that valuing an American put option can be accelerated by at least an order of magnitude, while yielding the identical estimate given by the standard Cox, Ross, and Rubenstein binomial tree. In addition, we discuss how similar techniques may be applied to pricing American options on interest rate derivatives and options involving multiple assets.
Author : Larry Cao
Publisher : CFA Institute Research Foundation
ISBN 13 : 195292734X
Total Pages : 258 pages
Book Rating : 4.9/5 (529 download)
Download or read book Handbook of Artificial Intelligence and Big Data Applications in Investments written by Larry Cao and published by CFA Institute Research Foundation. This book was released on 2023-04-24 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) and big data have their thumbprints all over the modern asset management firm. Like detectives investigating a crime, the practitioner contributors to this book put the latest data science techniques under the microscope. And like any good detective story, much of what is unveiled is at the same time surprising and hiding in plain sight. Each chapter takes you on a well-guided tour of the development and application of specific AI and big data techniques and brings you up to the minute on how they are being used by asset managers. Given the diverse backgrounds and affiliations of our authors, this book is the perfect companion to start, refine, or plan the next phase of your data science journey.
Author : Raquel M. Gaspar
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)
Download or read book Neural Network Pricing of American Put Options written by Raquel M. Gaspar and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this study, we use Neural Networks (NNs) to price American put options. We propose two NN models--a simple one and a more complex one--and we discuss the performance of two NN models with the Least-Squares Monte Carlo (LSM) method. This study relies on American put option market prices, for four large U.S. companies--Procter and Gamble Company (PG), Coca-Cola Company (KO), General Motors (GM), and Bank of America Corp (BAC). Our dataset is composed of all options traded within the period December 2018 until March 2019. Although on average, both NN models perform better than LSM, the simpler model (NN Model 1) performs quite close to LSM.Moreover, the second NN model substantially outperforms the other models, having an RMSE ca. 40% lower than the presented by LSM. The lower RMSE is consistent across all companies, strike levels, and maturities. In summary, all methods present a good accuracy; however, after calibration, NNs produce better results in terms of both execution time and Root Mean Squared Error (RMSE).
Author : Ing-Chyuan Wu
Publisher :
ISBN 13 :
Total Pages : 346 pages
Book Rating : 4.:/5 (525 download)
Download or read book Neural Networks for Financial Markets Analyses and Options Valuation written by Ing-Chyuan Wu and published by . This book was released on 2002 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose a neural network option pricing model that can fit listed option prices accurately, and be used to recover the implied asset price distribution and asset price dynamics. The observable market option prices are noisy and insufficient. To overcome the problem, two option pricing models constructed using multilayer feedforward neural networks are investigated. The first one uses a neural network to learn the implied volatility function of Black-Scholes-Merton model. To price an option, this neural network must work together with Black-Scholes-Merton formulas. The other one uses a neural network to learn the function mapping between the option price and observable affecting factors. This neural network is a complete option pricing model and can function independently of any option pricing formula. Based on a theory derived by Breeden and Litzenberger, the implied risk-neutral probability density surface can be extracted from the second partial derivative of the option price function with respect to the strike price. While both neural network option pricing models fit observed option prices well, only the first model is suitable for extracting a risk-neutral probability density surface. Risk-neutral valuation method is used to perform in-sample and out-of-sample tests. Based on the Fokker-Plank equation, an implied Ito process can be derived from the first and second partial derivatives of the option price function with respect to the strike price and the maturity. Similarly, only the first neural network option pricing model is suitable for deriving an Ito process. Monte Carlo simulation is used to perform in-sample and out-of-sample tests. The pricing errors from the extracted risk-neutral probability density surface and Ito process are only slightly larger than that directly from the neural network option pricing model. The small difference indicates that little information has been lost in the extracted risk-neutral probability density surface and Ito process. As a result, exotic options can be priced with the extracted information.
Author : Fahed Mostafa
Publisher : Springer
ISBN 13 : 331951668X
Total Pages : 177 pages
Book Rating : 4.3/5 (195 download)
Download or read book Computational Intelligence Applications to Option Pricing, Volatility Forecasting and Value at Risk written by Fahed Mostafa and published by Springer. This book was released on 2017-02-28 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book demonstrates the power of neural networks in learning complex behavior from the underlying financial time series data. The results presented also show how neural networks can successfully be applied to volatility modeling, option pricing, and value-at-risk modeling. These features mean that they can be applied to market-risk problems to overcome classic problems associated with statistical models.
Author : Marvin Koch
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (134 download)
Download or read book Numerical Implementation of a Deep Learning Approach to Bermuda Option Pricing written by Marvin Koch and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Daniel Alexandre Bloch
Publisher :
ISBN 13 :
Total Pages : 49 pages
Book Rating : 4.:/5 (13 download)
Download or read book Option Pricing With Machine Learning written by Daniel Alexandre Bloch and published by . This book was released on 2019 with total page 49 pages. Available in PDF, EPUB and Kindle. Book excerpt: An option pricing model is tied to its ability of capturing the dynamics of the underlying spot price process. Its misspecification will lead to pricing and hedging errors. Parametric pricing formula depends on the particular form of the dynamics of the underlying asset. For tractability reasons, some assumptions are made which are not consistent with the multifractal properties of market returns. On the other hand, non-parametric models such as neural networks use market data to estimate the implicit stochastic process driving the spot price and its relationship with contingent claims. When pricing multidimensional contingent claims, or even vanilla options with complex models, one must rely on numerical methods such as partial differential equations, numerical integration methods such as Fourier methods, or Monte Carlo simulations. Further, when calibrating financial models on market prices, a large number of model prices must be generated to fit the model parameters. Thus, one requires highly efficient computation methods which are fast and accurate. Neural networks with multiple hidden layers are universal interpolators with the ability of representing any smooth multidimentional function. As such, supervised learning is concerned with solving function estimation problems. The networks are decomposed into two separate phases, a training phase where the model is optimised off-line, and a testing phase where the model approximates the solution on-line. As a result, these methods can be used in finance in a fast and robust way for pricing exotic options as well as calibrating option prices in view of interpolating/extrapolating the volatility surface. They can also be used in risk management to fit options prices at the portfolio level in view of performing some credit risk analysis. We review some of the existing methods using neural networks for pricing market and model prices, present calibration, and introduce exotic option pricing. We discuss the feasibility of these methods, highlight problems, and propose alternative solutions.
Author : Lishang Jiang
Publisher : World Scientific
ISBN 13 : 9812563695
Total Pages : 344 pages
Book Rating : 4.8/5 (125 download)
Download or read book Mathematical Modeling and Methods of Option Pricing written by Lishang Jiang and published by World Scientific. This book was released on 2005 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: From the perspective of partial differential equations (PDE), this book introduces 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.
Author : Robert A. Jarrow
Publisher : McGraw-Hill/Irwin
ISBN 13 :
Total Pages : 268 pages
Book Rating : 4.F/5 ( download)
Download or read book Option Pricing written by Robert A. Jarrow and published by McGraw-Hill/Irwin. This book was released on 1983 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt:
