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Would you like to tell us about a lower price? If you are a seller for this product, would you like to suggest updates through seller support? As today's financial products have become more complex, quantitative analysts, financial engineers, and others in the financial industry now require robust techniques for numerical analysis. Covering advanced quantitative techniques, Computational Methods in Finance explains how to solve complex functional equations through numerical methods.
The first part of the book describes pricing methods for numerous derivatives under a variety of models. The book reviews common processes for modeling assets in different markets. It then examines many computational approaches for pricing derivatives. These include transform techniques, such as the fast Fourier transform, the fractional fast Fourier transform, the Fourier-cosine method, and saddlepoint method; the finite difference method for solving PDEs in the diffusion framework and PIDEs in the pure jump framework; and Monte Carlo simulation.
The next part focuses on essential steps in real-world derivative pricing. The author discusses how to calibrate model parameters so that model prices are compatible with market prices.
He also covers various filtering techniques and their implementations and gives examples of filtering and parameter estimation. Developed from the author's courses at Columbia University and the Courant Institute of New York University, this self-contained text is designed for graduate students in financial engineering and mathematical finance as well as practitioners in the financial industry. It will help readers accurately price a vast array of derivatives.
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Mathematics for Machine Learning. Marc Peter Deisenroth. Applied Computational Economics and Finance. Next page. Customers who bought this item also bought. Advances in Financial Machine Learning. Marcos Lopez de Prado. Monte Carlo Methods in Financial Engineering: v. Interest Rate Modeling. Volume 1: Foundations and Vanilla Models. Volume 3: Products and Risk Management. Review "The depth and breadth of this stand-alone textbook on computational methods in finance is astonishing.
It brings together a full-spectrum of methods with many practical examples. This book provides plenty of exercises and realistic case studies. Those who work through them will gain a deep understanding of the modern computational methods in finance. This uniquely comprehensive and well-written book will undoubtedly prove invaluable to many researchers and practitioners. In addition, it seems to be an excellent teaching book. There is also extensive material on model calibration, including interest rate models and filtering approaches.
The book is a very comprehensive and useful reference for anyone, even with limited mathematical background, who wishes to quickly understand techniques from computational finance. The content reflects the author's vast experience teaching master's level courses at Columbia and NYU, while simultaneously researching and trading on quantitative finance in leading banks and hedge funds. No customer reviews. How does Amazon calculate star ratings?
The machine learned model takes into account factors including: the age of a review, helpfulness votes by customers and whether the reviews are from verified purchases. Review this product Share your thoughts with other customers. Write a customer review. Most helpful customer reviews on Amazon. Verified Purchase. Excellent book on pricing derivative securities via Fourier transform, finite difference methods, simulations, filtering and parameter estimation.
I loved the course that the author gave at NYU. This book brings together the course lecture notes and adds more to them. If the reader of this review is considering taking Dr. Hirsa's course then my advise is: definitely take it. It will be difficult, but you'll learn a lot! He's an amazing teacher, but also be aware that he's a tough grader.
The book covers many interesting and challenging topics like Fourier transformation methods, finite difference methods, Kalman filtering and Monte-Carlo simulation etc. If one understands theories presented in the book and puts these theories into practice by writing computer programs to solve problems at the end of each chapter, one is well prepared for a career in quantitative finance.
The book is well-written and easy to follow. The author usually breaks down a complex problem into steps with clear mathematical derivations. The author analyzes and breaks down the problem into sections with clear derivations for each section. As a result, most people with decent math background can understand these derivations and can write a computer program solving PIDE to get price of an American option. The number of times I reached out for this book at my desk made me feel sort of responsible to write down this review.
In a few words, I found the book extremely well-written and really useful for anyone who works with numerical and computational methods. Here is a few points which in my opinion distinguishes this book from many of its class: 1. The math is just at the right amount: the author has a very keen understanding of where a detailed derivation of an equation or an algorithm is necessary and instructive, and which mathematical details and technicalities should be left out in the references.
There is also a consistent level of math throughout the book, in the sense that the author knows the average audience of such a book and what mathematical background they come from and everywhere in the book there are technical details hovering around the same level.
The choice of topics is eclectic: Fourier methods, finite difference methods, Kalman filtering, monte-carlo simulation and many other topics are all gathered in one book, and in my opinion, with ample details and depth. There is no doubt that every chapter of this book could be a book by itself, but in my opinion the book has achieved to cover significant depth in each chapter. Just leafing through the book is a joyful experience as you see all these different methods and applications and how elegantly everything works in the examples.
The exposition is extremely clear, well-written and easy to follow: I have seen a number of the topics elsewhere, however things look much more clear and easier to follow in this book. The book is a result of lecture notes that the author had used for teaching for years and I guess the clarity in the exposition is a result of the feedback from the students over the years.
Many helpful and interesting examples: Applying a generic computational method to a specific stochastic process or financial product is not always straight forward and would sometimes need a significant amount of further mathematical derivation. As an example, take Kalman filter for Heston stochastic volatility model, or the finite difference methods for a PIDE for an American option.
The examples throughout the book are extremely instructive in showing how to apply the generic methods to actual financial products and popular stochastic processes in quantitative finance. This list could be longer, but I think it would suffice to say at this point that I would strongly recommend this book to anyone interested in the topics.
However no codes available though the Well structured book. However no codes available though the numerical schemes are described in depth. The book is a 5 star text book for studying at home, but it is a 3 star book if you want to use it as a quick reference at work. The book at clearly written and explained well, which leads to redundant details that most readers do not care.
Also, there are a number errata that are very annoying and confusing. Go to Amazon. Back to top. Get to Know Us. Shopbop Designer Fashion Brands. Alexa Actionable Analytics for the Web. DPReview Digital Photography.
Computational Methods in Finance
Covering advanced quantitative techniques, Computational Methods in Finance explains how to solve complex functional equations through numerical methods. The first part of the book describes pricing methods for numerous derivatives under a variety of models. The book reviews common processes for modeling assets in different markets. It then examines many computational approaches for pricing derivatives. These include transform techniques, such as the fast Fourier transform, the fractional fast Fourier transform, the Fourier-cosine method, and saddlepoint method; the finite difference method for solving PDEs in the diffusion framework and PIDEs in the pure jump framework; and Monte Carlo simulation. The next part focuses on essential steps in real-world derivative pricing.