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eBook Introduction to Stochastic Calculus Applied to Finance (Chapman and Hall/CRC Financial Mathematics Series) ePub

eBook Introduction to Stochastic Calculus Applied to Finance (Chapman and Hall/CRC Financial Mathematics Series) ePub

by Bernard Lapeyre,Damien Lamberton

  • ISBN: 1584886269
  • Category: Mathematics
  • Subcategory: Math Science
  • Author: Bernard Lapeyre,Damien Lamberton
  • Language: English
  • Publisher: Chapman and Hall/CRC; 2 edition (November 30, 2007)
  • Pages: 254
  • ePub book: 1900 kb
  • Fb2 book: 1691 kb
  • Other: lit mbr azw mbr
  • Rating: 4.6
  • Votes: 602

Description

Series: Chapman and Hall/CRC Financial Mathematics Series. In short, the book is what it is: a short primer on a large area of mathematics in finance for those well-trained in a variety of engineering and applied mathematical subjects.

Series: Chapman and Hall/CRC Financial Mathematics Series. Hardcover: 254 pages. In other words, this book is for the French, because all the best French students are always Engineers first and something else afterwards.

Chapter 3 is an introduction to the main results in stochastic calculus that we will use in Chapter 4 to study the Black-Scholes model.

In mathematical finance, the Doob decomposition theorem can be used to determine the largest optimal exercise time .

In mathematical finance, the Doob decomposition theorem can be used to determine the largest optimal exercise time of an American option. XN) denote the non-negative, discounted payoffs of an American option in a N-period financial market model, adapted to a filtration (F0, F1,. Lamberton, Damien; Lapeyre, Bernard (2008), Introduction to Stochastic Calculus Applied to Finance, Chapman & Hall/CRC financial mathematics series (2. e., Boca Raton, FL: Chapman & Hall/CRC, ISBN 978-1-58488-626-6, MR 2362458, Zbl 1167.

SIMULATION AND ALGORITHMS FOR FINANCIAL MODELS Simulation and financial models Introduction to variance reduction methods Computer experiments. APPENDIX Normal random variables Conditional expectation Separation of convex sets.

This book introduces the mathematical methods of financial modeling with clear explanations of the most useful models. Introduction to Stochastic Calculus begins with an elementary presentation of discrete models, including the Cox-Ross-Rubenstein model

This book introduces the mathematical methods of financial modeling with clear explanations of the most useful models. Introduction to Stochastic Calculus begins with an elementary presentation of discrete models, including the Cox-Ross-Rubenstein model.

Damien Lamberton, Bernard Lapeyre. Chapman and Hall/CRC Published November 30, 2007 Textbook - 254 Pages ISBN 9781584886266 - CAT C626 Series: Chapman and Hall/CRC Financial Mathematics Series. eBooks are subject to VAT, which is applied during the checkout process. What are VitalSource eBooks? Chapman and Hall/CRC Published December 14, 2011 Textbook - 254 Pages ISBN 9780429121081 - CAT KE79993. What are VitalSource eBooks? December 14, 2011 by Chapman and Hall/CRC Textbook - 254 Pages ISBN 9780429121081 - CAT KE79993. CRC Press, 30 нояб Providing all of the necessary stochastic calculus theory, the authors cover many key finance topics, including martingales, arbitrage. Providing all of the necessary stochastic calculus theory, the authors cover many key finance topics, including martingales, arbitrage, option pricing, American and European options, the Black-Scholes model, optimal hedging, and the computer simulation of financial models.

Stochastic Calculus and Financial Applications (Stochastic Modelling and Applied Probability).

ISBN 10: 1584886269 ISBN 13: 9781584886266. Publisher: Chapman and Hall/CRC, 2007.

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Since the publication of the first edition of this book, the area of mathematical finance has grown rapidly, with financial analysts using more sophisticated mathematical concepts, such as stochastic integration, to describe the behavior of markets and to derive computing methods. Maintaining the lucid style of its popular predecessor, Introduction to Stochastic Calculus Applied to Finance, Second Edition incorporates some of these new techniques and concepts to provide an accessible, up-to-date initiation to the field. New to the Second EditionComplements on discrete models, including Rogers' approach to the fundamental theorem of asset pricing and super-replication in incomplete marketsDiscussions on local volatility, Dupire's formula, the change of numéraire techniques, forward measures, and the forward Libor model A new chapter on credit risk modelingAn extension of the chapter on simulation with numerical experiments that illustrate variance reduction techniques and hedging strategiesAdditional exercises and problemsProviding all of the necessary stochastic calculus theory, the authors cover many key finance topics, including martingales, arbitrage, option pricing, American and European options, the Black-Scholes model, optimal hedging, and the computer simulation of financial models. They succeed in producing a solid introduction to stochastic approaches used in the financial world.

Comments

Llathidan Llathidan
Introduction to Stochastic Calculus Applied to Finance, translated from French, is a widely used classic graduate textbook on mathematical finance and is a standard required text in France for DEA and PhD programs in the field.

Most folks familiar with Steve Shreve's Stochastic Calculus Models for Finance will be surprised at its brevity, for this work is aimed at different audiences.

Whereas Shreve's work is aimed at mathematicians and physicists who are coming to finance, and building on the commonalities of understandings of time series and data sets and signals, Lamberton & Lapeyre's work is aimed at an audience of mathematically trained engineers, who look at data sets as information for solving problems. Shreve's work, is, therefore, to help people come up with mathematical proofs, and L&L's is to help people solve problems.

Both probabilistic and partial differential equation approaches are covered, so both those from electrical and telecommunication engineering and mechanical engineering will be satisfied and on familiar ground. Numerical and algorithmic methods are also covered for those with systems analysis and operations management backgrounds.

This book, however, is decidedly for those who have had significant mathematical training. Whereas with Hull, Wilmott, Neftci, or Joshi you can play around with their approaches almost instantly in Excel or other programming tools (VBA, C, etc.), Lamberton and Lapeyre's work is for those who think out loud with a white board and others do the dirty work of coding. This work lacks specific examples, data sets, etc. Which makes it difficult to place. Its clarity and brevity are welcome, and it expands the knowledge beyond Hull of those who are not trained in math and came up the practical coding grunt side of quantfin. But it also is not a complete theoretical treatment for the first string math and theory set.

In short, the book is what it is: a short primer on a large area of mathematics in finance for those well-trained in a variety of engineering and applied mathematical subjects. In other words, this book is for the French, because all the best French students are always Engineers first and something else afterwards. If you also happen to be trained as an engineer and find Hull, Wilmott, Joshi & Neftci too easy, and Shreve too hard, then this is the book for you. Or if you are like me, and you've banged your head against this stuff for years just through the happenstance of your career and want to see how a mathematician writes about your gritty world, this is a great book for shedding light in areas filled with cobwebs.
Very Old Chap Very Old Chap
This is a compact, informative, and good book. If you know what you are looking for, and have prior knowledge of stochastic calculus then this is a good (but expensive) book.
Early Waffle Early Waffle
As precisely mentioned in the title, this book is only an introduction; and it is not an introduction to finance, but to stochastic calculus applied to finance.
The buyer of this book should therefore be aware of three facts:
1. After having read this book you are not (yet) an expert on stochastic calculus applied to finance. You have to continue with other books mentioned in Lamberton/Lapeyre. But this book is an excellent framework that leads you to many important results, omiting proofs that are only technical.
2. Mathematics is used in many other areas of Finance too (Time Series Analysis for example). What is treated in this book is only a very small part of Finance Mathematics, but an important one.
3. One should read another book with more economic background at the same time.
The authors begin with discrete-time models to present many important ideas in a (mathematically) simple environment before treating the contiuous models. Introduction to stochastic integration and stochastic differential equations is brief. Stochastic integration is only with respect to the standard browning motion. After having reached the Black-Scholes model and american options, the approach via partial differential equations is treated, followed by interest rate models, models with jumps and, a good idea: a chapter on simulations.
The book has very few mistakes, no important ones, only a strange layout failure on pages 6 to 7.
So I highly recommend this book as an INTRODUCTION to ONE important part of finance mathematics if read in combination with another book with more economic background. It can especially be used for upper graduate student seminars or as a basis for lecture courses.
Sennnel Sennnel
This book, translated from French, is by now a classic graduate textbook on mathematical finance, and provides a clear and concise introduction to the basic and important aspects of the theory. Although one of the first textbooks on the subject, it still remains in my opinion one of the best.
The book has been written for engineering students not mathematicians and avoids the theorem/proof format, going straight to essentials.
Also, while most textbooks on mathematical finance exclusively adopt either a probabilistic (like Baxter & Rennie) or a PDE approach to the theory (Wilmott et al, Wilmott), this book maintains the balance between the two aspects. Moreover, it does not neglect numerical methods and gives details on several algorithms for option pricing ( trees, Finite Difference, Monte Carlo) Finally, and perhaps this point is very important, the book maintains a reasonable volume while treating all these topics AND maintaining a high level of scientific rigor: all statements and notations are precise and oversimplification is avoided. Advanced topics such as variational inequalities for American options and HJM theory of interest rates are also included.
Some drawbacks of the book are: - a complete absence of empirical data/ real life figures - no description of various kinds of derivative products, why they are used,... But then, what can you ask for in such a small volume?
If you are an engineering/maths student and you want to discover what mathematical finance is about, I recommend you this book instead of John Hull's book.