Stochastic Models Of Financial Mathematics

Książki Obcojęzyczne>Angielskie>Mathematics & science>Mathematics>Applied mathematics>Stochastics

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Stochastic Models of Financial Mathematics

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FINANCIAL MATHEMATICS Asset Pricing Models LAP LAMBERT Academic Publishing

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Jump diffusion processes have been used in modern finance to capture discontinuous behavior in asset pricing. Pricing of assets in jump diffusions is consistent with the volatility smile often observed in financial markets. Logistic Brownian motion derived from logistic equation for asset security prices shows that naturally asset security prices would not usually shoot indefinitely (exponentially) due to the regulating factor that may limit the asset prices. Merton who was involved in the process of developing the Black-Scholes model came up with Merton jump model superimposed on Geometric Brownian motion. Therefore in this book, we have derived the price of dividend yielding asset that follows logistic Brownian motion with jump diffusion process and analyze the behavior of the derived model. This study used the knowledge of Geometric Brownian Motion and logistic Brownian motion, using the Heave-sides Cover-up Method we developed a price dynamic model. Data collected from Nairobi Security Exchange was analyzed to check the reliability of the formed.

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Mastering Financial Modeling: A Professional's Guide To Building Financial Models In Excel

Książki Obcojęzyczne>Angielskie>Mathematics & science>Mathematics>Applied mathematics>Mathematical modellingKsiążki Obcojęzyczne>Ang...

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Fundamental Models In Financial Theory

Książki Obcojęzyczne>Angielskie>Mathematics & science>Mathematics>Applied mathematics>Mathematical modellingKsiążki Obcojęzyczne>Ang...

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Mathematics of Financial Derivatives Cambridge University Press

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Finance is one of the fastest growing areas in the modern banking and corporate world. This, together with the sophistication of modern financial products, provides a rapidly growing impetus for new mathematical models and modern mathematical methods; the area is an expanding source for novel and relevant 'real-world' mathematics. In this book the authors describe the modelling of financial derivative products from an applied mathematician's viewpoint, from modelling through analysis to elementary computation. A unified approach to modelling derivative products as partial differential equations is presented, using numerical solutions where appropriate. Some mathematics is assumed, but clear explanations are provided for material beyond elementary calculus, probability, and algebra. Over 140 exercises are included. This volume will become the standard introduction to this exciting new field for advanced undergraduate students.

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Financial Mathematics of Market Liquidity Taylor & Francis Inc

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This book is among the first to present the mathematical models most commonly used to solve optimal execution problems and market making problems in finance. The Financial Mathematics of Market Liquidity: From Optimal Execution to Market Making

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Introduction To Financial Models For Management And Planning, Second Edition

Książki Obcojęzyczne>Angielskie>Economics, finance, business & management>Finance & accounting>FinanceKsiążki Obcojęzyczne>Angielskie&g...

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Business And Financial Models

Książki Obcojęzyczne>Angielskie>Economics, finance, business & management>Business & management>Business mathematics & systems

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Engineering Mathematics I Springer International Publishing AG

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This book highlights the latest advances in engineering mathematics with a main focus on the mathematical models, structures, concepts, problems and computational methods and algorithms most relevant for applications in modern technologies and engineering. In particular, it features mathematical methods and models of applied analysis, probability theory, differential equations, tensor analysis and computational modelling used in applications to important problems concerning electromagnetics, antenna technologies, fluid dynamics, material and continuum physics and financial engineering. The individual chapters cover both theory and applications, and include a wealth of figures, schemes, algorithms, tables and results of data analysis and simulation. Presenting new methods and results, reviews of cutting-edge research, and open problems for future research, they equip readers to develop new mathematical methods and concepts of their own, and to further compare and analyse the methods and results discussed. The book consists of contributed chapters covering research developed as a result of a focused international seminar series on mathematics and applied mathematics and a series of three focused international research workshops on engineering mathematics organised by the Research Environment in Mathematics and Applied Mathematics at M?lardalen University from autumn 2014 to autumn 2015: the International Workshop on Engineering Mathematics for Electromagnetics and Health Technology; the International Workshop on Engineering Mathematics, Algebra, Analysis and Electromagnetics; and the 1st Swedish-Estonian International Workshop on Engineering Mathematics, Algebra, Analysis and Applications. It serves as a source of inspiration for a broad spectrum of researchers and research students in applied mathematics, as well as in the areas of applications of mathematics considered in the book. ?

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Financial Modelling with Python John Wiley & Sons Inc

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"Fletcher and Gardner have created a comprehensive resource that will be of interest not only to those working in the field of finance, but also to those using numerical methods in other fields such as engineering, physics, and actuarial mathematics. By showing how to combine the high-level elegance, accessibility, and flexibility of Python, with the low-level computational efficiency of C++, in the context of interesting financial modeling problems, they have provided an implementation template which will be useful to others seeking to jointly optimize the use of computational and human resources. They document all the necessary technical details required in order to make external numerical libraries available from within Python, and they contribute a useful library of their own, which will significantly reduce the start-up costs involved in building financial models. This book is a must read for all those with a need to apply numerical methods in the valuation of financial claims."- David Louton, Professor of Finance, Bryant University This book is directed at both industry practitioners and students interested in designing a pricing and risk management framework for financial derivatives using the Python programming language. It is a practical book complete with working, tested code that guides the reader through the process of building a flexible, extensible pricing framework in Python. The pricing frameworks' loosely coupled fundamental components have been designed to facilitate the quick development of new models. Concrete applications to real-world pricing problems are also provided. Topics are introduced gradually, each building on the last. They include basic mathematical algorithms, common algorithms from numerical analysis, trade, market and event data model representations, lattice and simulation based pricing, and model development. The mathematics presented is kept simple and to the point. The book also provides a host of information on practical technical topics such as C++/Python hybrid development (embedding and extending) and techniques for integrating Python based programs with Microsoft Excel.

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Time Series Models Springer, Berlin

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This textbook provides a self-contained presentation of the theory and models of time series analysis. Putting an emphasis on weakly stationary processes and linear dynamic models, it describes the basic concepts, ideas, methods and results in a mathematically well-founded form and includes numerous examples and exercises. The first part presents the theory of weakly stationary processes in time and frequency domain, including prediction and filtering. The second part deals with multivariate AR, ARMA and state space models, which are the most important model classes for stationary processes, and addresses the structure of AR, ARMA and state space systems, Yule-Walker equations, factorization of rational spectral densities and Kalman filtering. Finally, there is a discussion of Granger causality, linear dynamic factor models and (G)ARCH models. The book provides a solid basis for advanced mathematics students and researchers in fields such as data-driven modeling, forecasting and filtering, which are important in statistics, control engineering, financial mathematics, econometrics and signal processing, among other subjects.

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Market Risk Analysis - Value-at-Risk Models, Volume IV John Wiley & Sons Inc

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Written by leading market risk academic, Professor Carol Alexander, Value-at-Risk Models forms part four of the Market Risk Analysis four volume set. Building on the three previous volumes this book provides by far the most comprehensive, rigorous and detailed treatment of market VaR models. It rests on the basic knowledge of financial mathematics and statistics gained from Volume I, of factor models, principal component analysis, statistical models of volatility and correlation and copulas from Volume II and, from Volume III, knowledge of pricing and hedging financial instruments and of mapping portfolios of similar instruments to risk factors. A unifying characteristic of the series is the pedagogical approach to practical examples that are relevant to market risk analysis in practice. All together, the Market Risk Analysis four volume set illustrates virtually every concept or formula with a practical, numerical example or a longer, empirical case study.Across all four volumes there are approximately 300 numerical and empirical examples, 400 graphs and figures and 30 case studies many of which are contained in interactive Excel spreadsheets available from the the accompanying CD-ROM . Empirical examples and case studies specific to this volume include: Parametric linear value at risk (VaR)models: normal, Student t and normal mixture and their expected tail loss (ETL); New formulae for VaR based on autocorrelated returns; Historical simulation VaR models: how to scale historical VaR and volatility adjusted historical VaR; Monte Carlo simulation VaR models based on multivariate normal and Student t distributions, and based on copulas; Examples and case studies of numerous applications to interest rate sensitive, equity, commodity and international portfolios; Decomposition of systematic VaR of large portfolios into standard alone and marginal VaR components; Backtesting and the assessment of risk model risk; Hypothetical factor push and historical stress tests, and stress testing based on VaR and ETL.

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Financial Engineering and Computation Cambridge University Press

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Nowadays students and professionals intending to work in any area of finance must master not only advanced concepts and mathematical models but also learn how to implement these models computationally. This comprehensive text combines the theory and mathematics behind financial engineering with an emphasis on computation, in keeping with the way financial engineering is practised in today's capital markets. Unlike most books on investments, financial engineering, or derivative securities, the book starts from very basic ideas in finance and gradually builds up the theory. It offers a thorough grounding in the subject for MBAs in finance, students of engineering and sciences who are pursuing a career in finance, researchers in computational finance, system analysts, and financial engineers. Along with the theory, the author presents numerous algorithms for pricing, risk management, and portfolio management. The emphasis is on pricing financial and derivative securities: bonds, options, futures, forwards, interest rate derivatives, mortgage-backed securities, bonds with embedded options, and more. Each instrument is treated in a short, self-contained chapter for ready reference use. Many of these algorithms are coded in Java as programs for the Web, available from the book's home page.

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Models.Behaving.Badly. Simon & Schuster

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A former head of quantitative analysis at Goldman Sachs explains how a collision between economics and mathematics contributed to the recent financial crisis, sharing narrative insights into how the unpredictability of human nature caused leading experts to miss important contributing factors. 50,000 first printing.

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