Stochastic Processes for Finance (Jonathan Block)

 
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Stochastic Processes for Finance (Jonathan Block)

This book expands Probability for Finance to multi-period financial models that can be constructed using discrete or continuous time frames. It provides a pedagogical explanation of the most significant stochastic processes utilized in finance, such as Markov chains, Brownian motion, and martingales.

Additionally, it demonstrates how to interpret mathematical concepts like filtrations, Ito's lemma, and the Girsanov theorem within the context of financial models. Additionally, it offers a tonne of examples drawn from financial literature.

An excellent demonstration of how mathematical probability can be used to get a big number of conclusions from a small number of premises. In conclusion, this is a well-written text that uses an applied probability method to discuss the main classical financial models. Anyone learning the mathematics behind the conventional theory of finance should find it to be a superb introduction.

It has become more crucial in recent years to describe financial uncertainty using stochastic processes, yet this process is frequently thought to require a strong mathematical foundation. This book demonstrates that this isn't always the case. It provides an approachable presentation that finds a balance between the theoretical and the practical of the theory of discrete stochastic processes and their applications in finance. The author initially gives a basic overview of the pertinent fields of real analysis and probability using an approach that sees complex stochastic calculus as based on a simple class of discrete processes called "random walks." Then, he utilizes random walks to explain the Kolmogorov backward equation, the reflection principle, and the change of measure formula. The Black-Scholes formula is derived as a limit of the binomial model, and applications to the pricing of derivative securities are presented.

Another primary focus of the book is the pricing of corporate bonds and credit derivatives, which the author explains in terms of discrete default models. By presenting important results in discrete processes and showing how to transfer those results to their continuous counterparts, this book imparts an intuitive and practical understanding of the subject. This unique treatment is ideal both as a text for a graduate-level class and as a reference for researchers and practitioners in financial engineering, operations research, and mathematical and statistical finance.

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