Download or read book Nonlinear Models in Mathematical Finance written by Matthias Ehrhardt and published by Nova Science Pub Incorporated. This book was released on 2008 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an overview on the current state-of-the-art research on non-linear option pricing. Non-linear models are becoming more and more important since they take into account many effects that are not included in the linear model. However, in practice (i.e. in banks) linear models are still used, giving rise to large errors in computing the fair price of options. Hence, there exists a noticeable need for non-linear modelling of financial products. This book will help to foster the usage of non-linear Black-Scholes models in practice.
Download or read book Non Linear Time Series Models in Empirical Finance written by Philip Hans Franses and published by Cambridge University Press. This book was released on 2000-07-27 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: This 2000 volume reviews non-linear time series models, and their applications to financial markets.
Download or read book Nonlinear Option Pricing written by Julien Guyon and published by CRC Press. This book was released on 2013-12-19 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: New Tools to Solve Your Option Pricing ProblemsFor nonlinear PDEs encountered in quantitative finance, advanced probabilistic methods are needed to address dimensionality issues. Written by two leaders in quantitative research-including Risk magazine's 2013 Quant of the Year-Nonlinear Option Pricing compares various numerical methods for solving hi
Download or read book Nonlinear Financial Econometrics Forecasting Models Computational and Bayesian Models written by G. Gregoriou and published by Palgrave Macmillan. This book was released on 2010-12-21 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book investigates several competing forecasting models for interest rates, financial returns, and realized volatility, addresses the usefulness of nonlinear models for hedging purposes, and proposes new computational techniques to estimate financial processes.
Download or read book Non Linear Models in Mathematical Finance written by Marlo Avellaneda and published by . This book was released on 1996-08 with total page 728 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonlinear Valuation and Non Gaussian Risks in Finance written by Dilip B. Madan and published by Cambridge University Press. This book was released on 2022-02-03 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore how market valuation must abandon linearity to deliver efficient resource allocation.
Download or read book Nonlinear Option Pricing written by Julien Guyon and published by CRC Press. This book was released on 2013-12-19 with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt: New Tools to Solve Your Option Pricing Problems For nonlinear PDEs encountered in quantitative finance, advanced probabilistic methods are needed to address dimensionality issues. Written by two leaders in quantitative research—including Risk magazine’s 2013 Quant of the Year—Nonlinear Option Pricing compares various numerical methods for solving high-dimensional nonlinear problems arising in option pricing. Designed for practitioners, it is the first authored book to discuss nonlinear Black-Scholes PDEs and compare the efficiency of many different methods. Real-World Solutions for Quantitative Analysts The book helps quants develop both their analytical and numerical expertise. It focuses on general mathematical tools rather than specific financial questions so that readers can easily use the tools to solve their own nonlinear problems. The authors build intuition through numerous real-world examples of numerical implementation. Although the focus is on ideas and numerical examples, the authors introduce relevant mathematical notions and important results and proofs. The book also covers several original approaches, including regression methods and dual methods for pricing chooser options, Monte Carlo approaches for pricing in the uncertain volatility model and the uncertain lapse and mortality model, the Markovian projection method and the particle method for calibrating local stochastic volatility models to market prices of vanilla options with/without stochastic interest rates, the a + bλ technique for building local correlation models that calibrate to market prices of vanilla options on a basket, and a new stochastic representation of nonlinear PDE solutions based on marked branching diffusions.
Download or read book Nonlinear Financial Econometrics Forecasting Models Computational and Bayesian Models written by G. Gregoriou and published by Springer. This book was released on 2010-12-21 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book investigates several competing forecasting models for interest rates, financial returns, and realized volatility, addresses the usefulness of nonlinear models for hedging purposes, and proposes new computational techniques to estimate financial processes.
Download or read book Recent Advances in Estimating Nonlinear Models written by Jun Ma and published by Springer. This book was released on 2017-04-30 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonlinear models have been used extensively in the areas of economics and finance. Recent literature on the topic has shown that a large number of series exhibit nonlinear dynamics as opposed to the alternative--linear dynamics. Incorporating these concepts involves deriving and estimating nonlinear time series models, and these have typically taken the form of Threshold Autoregression (TAR) models, Exponential Smooth Transition (ESTAR) models, and Markov Switching (MS) models, among several others. This edited volume provides a timely overview of nonlinear estimation techniques, offering new methods and insights into nonlinear time series analysis. It features cutting-edge research from leading academics in economics, finance, and business management, and will focus on such topics as Zero-Information-Limit-Conditions, using Markov Switching Models to analyze economics series, and how best to distinguish between competing nonlinear models. Principles and techniques in this book will appeal to econometricians, finance professors teaching quantitative finance, researchers, and graduate students interested in learning how to apply advances in nonlinear time series modeling to solve complex problems in economics and finance.
Download or read book Nonlinear Time Series Modeling with Application to Finance and Other Fields written by Shusong Jin and published by . This book was released on 2017-01-26 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "Nonlinear Time Series Modeling With Application to Finance and Other Fields" by Shusong, Jin, 金曙松, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled "NONLINEAR TIME SERIES MODELING WITH APPLICATION TO FINANCE AND OTHER FIELDS" Submitted by JIN Shusong for the degree of Doctor of Philosophy at The University of Hong Kong in May 2005 This thesis investigates the extension and application of nonlinear time series methodologies in both finance and ecology. The nonlinear time series structure consideredhastheflavourofamixturemodel. Themixingmechanismcanfollow the threshold approach or the classical mixture approach. A simple Wald test was developed to check the number of components in a mixture structure. The penalized likelihood was used for parameter estimation. The consistency of the estimates and the asymptotic distribution of the test statistic which was based on the estimates was derived. New models for the di- rectmodelingofvalue-of-risk(VaR)infinancewereconsideredbasedontheabove framework. It was shown that modeling VaR directly using a nonlinear frame- work resulted in more reliable estimates than traditional methods. A nonlinear bivariate time series was constructed whose relationship between the marginal processes was defined by a copula. This nonlinear model was then applied to the modeling of the exchange rates of Deutsch-Mark/U.S.-Dollar (DEM/USD)and Japanese-Yen/U.S. Dollar (JPY/USD). The above nonlinear framework was extended to the analysis of panel time series. Mixture autoregressive models with a common component among all member series was proposed. Estimation of the model was done via the Expectation-Maximization (EM) algorithm. The model was illustrated using the grey-sided voles data collected from Hokkaido, Japan. A partial linear model was proposed for panel data with contemporane- ous correlations. A semiparametric estimation procedure was proposed and some asymptotic results of the estimates were obtained. This extended the classical seemingly uncorrelated regression model to the panel time series context. The modelwasappliedtothemodernCanadianlynxdatasetandthegrey-sidedvoles data. It was found that the new model provided a better understanding of the underlying structure of these two time series. DOI: 10.5353/th_b3199605 Subjects: Linear models (Statistics) Time-series analysis Finance - Mathematical models Ecology - Mathematical models
Download or read book Optimization in Economics and Finance written by Bruce D. Craven and published by Springer Science & Business Media. This book was released on 2005-10-24 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: Some recent developments in the mathematics of optimization, including the concepts of invexity and quasimax, have not yet been applied to models of economic growth, and to finance and investment. Their applications to these areas are shown in this book.
Download or read book Nonlinear Models for Economic Decision Processes written by Ionut Purica and published by World Scientific. This book was released on 2010 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using models, developed in one branch of science, to describe similar behaviors encountered in a different one, is the essence of a synergetic approach. A wide range of topics has been developed including Agent-based models, econophysics, socio-economic networks, information, bounded rationality and learning in economics, markets as complex adaptive systems evolutionary economics, multiscale analysis and modeling, nonlinear dynamics and econometrics, physics of risk, statistical and probabilistic methods in economics and finance. Complexity. This publication concentrates on process behavior of economic systems and building models that stem from Haken's, Prigogine's, Taylor's work as well as from nuclear physics models.
Download or read book Modelling and Forecasting Financial Data written by Abdol S. Soofi and published by Springer Science & Business Media. This book was released on 2002-03-31 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last decade, dynamical systems theory and related nonlinear methods have had a major impact on the analysis of time series data from complex systems. Recent developments in mathematical methods of state-space reconstruction, time-delay embedding, and surrogate data analysis, coupled with readily accessible and powerful computational facilities used in gathering and processing massive quantities of high-frequency data, have provided theorists and practitioners unparalleled opportunities for exploratory data analysis, modelling, forecasting, and control. Until now, research exploring the application of nonlinear dynamics and associated algorithms to the study of economies and markets as complex systems is sparse and fragmentary at best. Modelling and Forecasting Financial Data brings together a coherent and accessible set of chapters on recent research results on this topic. To make such methods readily useful in practice, the contributors to this volume have agreed to make available to readers upon request all computer programs used to implement the methods discussed in their respective chapters. Modelling and Forecasting Financial Data is a valuable resource for researchers and graduate students studying complex systems in finance, biology, and physics, as well as those applying such methods to nonlinear time series analysis and signal processing.
Download or read book Nonlinear Time Series Analysis of Economic and Financial Data written by Philip Rothman and published by Springer Science & Business Media. This book was released on 1999-01-31 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonlinear Time Series Analysis of Economic and Financial Data provides an examination of the flourishing interest that has developed in this area over the past decade. The constant theme throughout this work is that standard linear time series tools leave unexamined and unexploited economically significant features in frequently used data sets. The book comprises original contributions written by specialists in the field, and offers a combination of both applied and methodological papers. It will be useful to both seasoned veterans of nonlinear time series analysis and those searching for an informative panoramic look at front-line developments in the area.
Download or read book Nonlinear Financial Econometrics Markov Switching Models Persistence and Nonlinear Cointegration written by Greg N. Gregoriou and published by Springer. This book was released on 2010-12-08 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book proposes new methods to value equity and model the Markowitz efficient frontier using Markov switching models and provide new evidence and solutions to capture the persistence observed in stock returns across developed and emerging markets.
Download or read book Mathematical Finance written by Christian Fries and published by John Wiley & Sons. This book was released on 2007-10-19 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: A balanced introduction to the theoretical foundations and real-world applications of mathematical finance The ever-growing use of derivative products makes it essential for financial industry practitioners to have a solid understanding of derivative pricing. To cope with the growing complexity, narrowing margins, and shortening life-cycle of the individual derivative product, an efficient, yet modular, implementation of the pricing algorithms is necessary. Mathematical Finance is the first book to harmonize the theory, modeling, and implementation of today's most prevalent pricing models under one convenient cover. Building a bridge from academia to practice, this self-contained text applies theoretical concepts to real-world examples and introduces state-of-the-art, object-oriented programming techniques that equip the reader with the conceptual and illustrative tools needed to understand and develop successful derivative pricing models. Utilizing almost twenty years of academic and industry experience, the author discusses the mathematical concepts that are the foundation of commonly used derivative pricing models, and insightful Motivation and Interpretation sections for each concept are presented to further illustrate the relationship between theory and practice. In-depth coverage of the common characteristics found amongst successful pricing models are provided in addition to key techniques and tips for the construction of these models. The opportunity to interactively explore the book's principal ideas and methodologies is made possible via a related Web site that features interactive Java experiments and exercises. While a high standard of mathematical precision is retained, Mathematical Finance emphasizes practical motivations, interpretations, and results and is an excellent textbook for students in mathematical finance, computational finance, and derivative pricing courses at the upper undergraduate or beginning graduate level. It also serves as a valuable reference for professionals in the banking, insurance, and asset management industries.
Download or read book Extracting Knowledge From Time Series written by Boris P. Bezruchko and published by Springer. This book was released on 2010-11-04 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical modelling is ubiquitous. Almost every book in exact science touches on mathematical models of a certain class of phenomena, on more or less speci?c approaches to construction and investigation of models, on their applications, etc. As many textbooks with similar titles, Part I of our book is devoted to general qu- tions of modelling. Part II re?ects our professional interests as physicists who spent much time to investigations in the ?eld of non-linear dynamics and mathematical modelling from discrete sequences of experimental measurements (time series). The latter direction of research is known for a long time as “system identi?cation” in the framework of mathematical statistics and automatic control theory. It has its roots in the problem of approximating experimental data points on a plane with a smooth curve. Currently, researchers aim at the description of complex behaviour (irregular, chaotic, non-stationary and noise-corrupted signals which are typical of real-world objects and phenomena) with relatively simple non-linear differential or difference model equations rather than with cumbersome explicit functions of time. In the second half of the twentieth century, it has become clear that such equations of a s- ?ciently low order can exhibit non-trivial solutions that promise suf?ciently simple modelling of complex processes; according to the concepts of non-linear dynamics, chaotic regimes can be demonstrated already by a third-order non-linear ordinary differential equation, while complex behaviour in a linear model can be induced either by random in?uence (noise) or by a very high order of equations.
