Stochastic Analysis for Finance with Simulations
Springer International Publishing (Verlag)
978-3-319-25587-3 (ISBN)
The level of familiarity with computer programming is kept to a minimum. To make the book accessible to a wider audience, some background mathematical facts are included in the first part of the book and also in the appendices. This work attempts to bridge the gap between mathematics and finance by using diagrams, graphs and simulations in addition to rigorous theoretical exposition. Simulations are not only used as the computational method in quantitative finance, but they can also facilitate an intuitive and deeper understanding of theoretical concepts.
Stochastic Analysis for Finance with Simulations is designed for readers who want to have a deeper understanding of the delicate theory of quantitative finance by doing computer simulations in addition to theoretical study. It will particularly appeal to advanced undergraduate and graduate students in mathematics and business, but not excluding practitioners in finance industry.
The author's main interests are simulations of random phenomena in the areas of quantitative finance, random number generators, dynamical systems theory, and information theory. He has published a book titled "Computational Ergodic Theory".
Preface.- Acknowledgements.- List of Figures.- List of Tables.- List of Simulations.- Fundamental Concepts.- Financial Derivatives.- The Lebesgue Integral.- Basic Probability Theory.- Conditional Expectation.- Stochastic Processes.- Brownian Motion.- Girsanov's Theorem.- The Reflection Principle of Brownian Motion.- The Ito Integral.- The Ito Formula.- Stochastic Differential Equations.- The Feynmann-Kac Theorem.- The Binomial Tree Method for Option Pricing.- The Black-Scholes-Merton Differential Equation.- The Martingale Method.- Pricing of Vanilla Options.- Pricing of Exotic Options.- American Options.- The Capital Asset Pricing Model.- Dynamic Programming.- Bond Pricing.- Interest Rate Models.- Numeraires.- Numerical Estimation of Volatility.- Time Series.- Random Numbers.- The Monte Carlo Method for Option Pricing.- Numerical Solution of the Black-Scholes-Merton Equation.- Numerical Solution of Stochastic Differential Equations. Appendices.- Solutions for Selected Problems.- Glossary.- References.- Index.
"This book gives an introduction to financial mathematics. It presents also some background of mathematical facts necessary for understanding modern finance. ... For the reader convenience, the book contains a detailed contents, a list of figures, a list of tables, a list of simulations, a list of acronyms and a list of used symbols." (Jacek Jakubowski, zbMATH 1409.91002, 2019)
"This excellent textbook is addressed to undergraduate and graduate students in mathematics and finance who want to study the main tools of stochastic calculus and its application to quantitative finance. Also, it can be used as a reference book for practitioners and professionals from the financial industry who want a better understanding of the theoretical aspects of stochastic calculus, and how it can be used in the pricing of financial derivatives." (Carlos Vázquez Cendón, Mathematical Reviews, August, 2017)
| Erscheinungsdatum | 08.10.2016 |
|---|---|
| Reihe/Serie | Universitext |
| Zusatzinfo | XXXII, 657 p. 189 illus., 107 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Mathematik / Informatik ► Mathematik |
| Wirtschaft ► Betriebswirtschaft / Management | |
| Schlagworte | Binomial Tree Method • Black-Scholes-Merton Equation • Black–Scholes–Merton Equation • Brownian motion • Interest Rate Model • Martingale Method • mathematics and statistics • mathematics, general • Monte Carlo Method • Optimal Portfolio • Option pricing • Quantitative Finance • Stochastic Calculus • Stochastic Calculus • stochastic differential equation • Time Series |
| ISBN-10 | 3-319-25587-8 / 3319255878 |
| ISBN-13 | 978-3-319-25587-3 / 9783319255873 |
| Zustand | Neuware |
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