an elementary introduction to mathematical finance options and other topics

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An Elementary Introduction To Mathematical Finance

Author : Sheldon M. Ross
ISBN : 9781139498036
Genre : Mathematics
File Size : 81. 2 MB
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This textbook on the basics of option pricing is accessible to readers with limited mathematical training. It is for both professional traders and undergraduates studying the basics of finance. Assuming no prior knowledge of probability, Sheldon M. Ross offers clear, simple explanations of arbitrage, the Black-Scholes option pricing formula, and other topics such as utility functions, optimal portfolio selections, and the capital assets pricing model. Among the many new features of this third edition are new chapters on Brownian motion and geometric Brownian motion, stochastic order relations and stochastic dynamic programming, along with expanded sets of exercises and references for all the chapters.

An Elementary Introduction To Mathematical Finance

Author : Sheldon M. Ross
ISBN : 0521814294
Genre : Business & Economics
File Size : 37. 54 MB
Format : PDF, ePub, Docs
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Contains a new chapter on optimization methods in finance, a new section on Value at Risk and Conditional Value at Risk, plus much more.

An Elementary Introduction To Mathematical Finance

Author : Sheldon M. Ross
ISBN : 1139069691
Genre : Investments
File Size : 39. 32 MB
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An Introduction To Mathematical Finance

Author : Sheldon M. Ross
ISBN : 0521770432
Genre : Mathematics
File Size : 79. 22 MB
Format : PDF, Mobi
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This mathematically elementary introduction to the theory of options pricing presents the Black-Scholes theory of options as well as introducing such topics in finance as the time value of money, mean variance analysis, optimal portfolio selection, and the capital assets pricing model. The author assumes no prior knowledge of probability and presents all the necessary preliminary material simply and clearly. He explains the concept of arbitrage with examples, and then uses the arbitrage theorem, along with an approximation of geometric Brownian motion, to obtain a simple derivation of the Black-Scholes formula. In the later chapters he presents real price data indicating that this model is not always appropriate and shows how the model can be generalized to deal with such situations. No other text presents such topics in a mathematically accurate but accessible way. It will appeal to professional traders as well as undergraduates studying the basics of finance.

Elementary Probability Theory With Stochastic Processes

Author : K. L. Chung
ISBN : 9781475751147
Genre : Mathematics
File Size : 21. 85 MB
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In the past half-century the theory of probability has grown from a minor isolated theme into a broad and intensive discipline interacting with many other branches of mathematics. At the same time it is playing a central role in the mathematization of various applied sciences such as statistics, opera tions research, biology, economics and psychology-to name a few to which the prefix "mathematical" has so far been firmly attached. The coming-of-age of probability has been reflected in the change of contents of textbooks on the subject. In the old days most of these books showed a visible split personality torn between the combinatorial games of chance and the so-called "theory of errors" centering in the normal distribution. This period ended with the appearance of Feller's classic treatise (see [Feller l]t) in 1950, from the manuscript of which I gave my first substantial course in probability. With the passage of time probability theory and its applications have won a place in the college curriculum as a mathematical discipline essential to many fields of study. The elements of the theory are now given at different levels, sometimes even before calculus. The present textbook is intended for a course at about the sophomore level. It presupposes no prior acquaintance with the subject and the first three chapters can be read largely without the benefit of calculus.

Mathematical Finance And Probability

Author : Pablo Koch Medina
ISBN : 9783034880411
Genre : Mathematics
File Size : 46. 49 MB
Format : PDF
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This self-contained book presents the theory underlying the valuation of derivative financial instruments, which is becoming a standard part of the professional toolbox in the financial industry. It provides great insight into the underlying economic ideas in a very readable form, putting the reader in an excellent position to proceed to the more general continuous-time theory.

An Elementary Introduction To Stochastic Interest Rate Modeling

Author : Nicolas Privault
ISBN : 9789814390859
Genre : Business & Economics
File Size : 65. 63 MB
Format : PDF, ePub
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Interest rate modeling and the pricing of related derivatives remain subjects of increasing importance in financial mathematics and risk management. This book provides an accessible introduction to these topics by a step-by-step presentation of concepts with a focus on explicit calculations. Each chapter is accompanied with exercises and their complete solutions, making the book suitable for advanced undergraduate and graduate level students. This second edition retains the main features of the first edition while incorporating a complete revision of the text as well as additional exercises with their solutions, and a new introductory chapter on credit risk. The stochastic interest rate models considered range from standard short rate to forward rate models, with a treatment of the pricing of related derivatives such as caps and swaptions under forward measures. Some more advanced topics including the BGM model and an approach to its calibration are also covered.

An Introduction To Financial Option Valuation

Author : Desmond Higham
ISBN : 9781139457897
Genre : Mathematics
File Size : 31. 94 MB
Format : PDF, Kindle
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This is a lively textbook providing a solid introduction to financial option valuation for undergraduate students armed with a working knowledge of a first year calculus. Written in a series of short chapters, its self-contained treatment gives equal weight to applied mathematics, stochastics and computational algorithms. No prior background in probability, statistics or numerical analysis is required. Detailed derivations of both the basic asset price model and the Black–Scholes equation are provided along with a presentation of appropriate computational techniques including binomial, finite differences and in particular, variance reduction techniques for the Monte Carlo method. Each chapter comes complete with accompanying stand-alone MATLAB code listing to illustrate a key idea. Furthermore, the author has made heavy use of figures and examples, and has included computations based on real stock market data.

Introduction To Mathematical Finance

Author : Stanley R. Pliska
ISBN : 1557869456
Genre : Business & Economics
File Size : 37. 41 MB
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The purpose of this book is to provide a rigorous yet accessible introduction to the modern financial theory of security markets. The main subjects are derivatives and portfolio management. The book is intended to be used as a text by advanced undergraduates and beginning graduate students. It is also likely to be useful to practicing financial engineers, portfolio manager, and actuaries who wish to acquire a fundamental understanding of financial theory. The book makes heavy use of mathematics, but not at an advanced level. Various mathematical concepts are developed as needed, and computational examples are emphasized.

An Elementary Introduction To Statistical Learning Theory

Author : Sanjeev Kulkarni
ISBN : 1118023463
Genre : Mathematics
File Size : 86. 32 MB
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A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning A joint endeavor from leading researchers in the fields of philosophy and electrical engineering, An Elementary Introduction to Statistical Learning Theory is a comprehensive and accessible primer on the rapidly evolving fields of statistical pattern recognition and statistical learning theory. Explaining these areas at a level and in a way that is not often found in other books on the topic, the authors present the basic theory behind contemporary machine learning and uniquely utilize its foundations as a framework for philosophical thinking about inductive inference. Promoting the fundamental goal of statistical learning, knowing what is achievable and what is not, this book demonstrates the value of a systematic methodology when used along with the needed techniques for evaluating the performance of a learning system. First, an introduction to machine learning is presented that includes brief discussions of applications such as image recognition, speech recognition, medical diagnostics, and statistical arbitrage. To enhance accessibility, two chapters on relevant aspects of probability theory are provided. Subsequent chapters feature coverage of topics such as the pattern recognition problem, optimal Bayes decision rule, the nearest neighbor rule, kernel rules, neural networks, support vector machines, and boosting. Appendices throughout the book explore the relationship between the discussed material and related topics from mathematics, philosophy, psychology, and statistics, drawing insightful connections between problems in these areas and statistical learning theory. All chapters conclude with a summary section, a set of practice questions, and a reference sections that supplies historical notes and additional resources for further study. An Elementary Introduction to Statistical Learning Theory is an excellent book for courses on statistical learning theory, pattern recognition, and machine learning at the upper-undergraduate and graduate levels. It also serves as an introductory reference for researchers and practitioners in the fields of engineering, computer science, philosophy, and cognitive science that would like to further their knowledge of the topic.

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