Table of contents for all three volumes full details at andersen-piterbarg-book. From Preface For quantitative researchers working in an investment bank, the process of writing a fixed income model usually has two stages. First, a theoretical framework for yield curve dynamics is specified, using the language of mathematics especially stochastic calculus to ensure that the underlying model is well-specified and internally consistent. Second, in order to use the model in practice, the equations arising from the first step need to be turned into a working implementation on a computer. While specification of the theoretical model may be seen as the difficult part, in quantitative finance applications the second step is technically and intellectually often more challenging than the first. In the implementation phase, not only does one need to translate abstract ideas into computer code, one also needs to ensure that the resulting numbers being produced are meaningful to a trading desk, are stable and robust, are in line with market observations, and are produced in a timely manner.
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Please visit our information page. Princeton University Library Catalog. Start over. Andersen and Vladimir V. Students and academics interested in financial engineering and applied work will find the material particularly useful for its description of real-life model usage and for its expansive discussion of model calibration, approximation theory, and numerical methods.
Bibliographic references Includes bibliographical references and indexes. Contents v. Foundations and vanilla models: Introduction to arbitrage pricing theory ; Finite difference methods ; Monte Carlo methods ; Fundamentals of interest rate modeling ; Fixed income instruments ; Yield curve construction and risk management ; Vanilla models with local volatility ; Vanilla Models with stochastic volatility I ; Vanilla models with stochastic volatility II -- v.
Products and risk management: Single-rate vanilla derivatives ; Multi-rate vanilla derivatives ; Callable Libor exotics ; Bermudan swaptions ; TARNs, volatility swaps, and other derivatives ; Out-of-model adjustments ; Introduction to risk management ; Payoff smoothing and related methods ; Pathwise differentiation ; Importance sampling and control variates ; Vegas in Libor market models -- Appendix: Markovian projection.
ISBN v. Supplementary Information Other versions. Id SCSB
ISBN 13: 9780984422104
Dirichlet Boundary Conditions. Monte Carlo Error. A Appendix: Spline Theory. A Appendix: General Volatility Processes.
Interest Rate Modeling. Volume 1 : Foundations and Vanilla Models
Containing many results that are new or exist only in recent research articles, Interest Rate Modeling: Theory and Practice portrays the theory of interest rate modeling as a three-dimensional object of finance, mathematics, and computation. It introduces. The three volumes of Interest Rate Modeling present a comprehensive and up-to- date treatment of techniques and models used in the pricing and risk. Andersen; Vladimir V. Piterbarg and a great. Page 1 of 1 Start over Page 1 of 1. Amazon Renewed Refurbished products with a warranty.
Princeton University Library Catalog
Leif B. Andersen , Vladimir V. The three volumes of Interest Rate Modeling present a comprehensive and up-to-date treatment of techniques and models used in the pricing and risk management of fixed income securities. Written by two leading practitioners and seasoned industry veterans, this unique series combines finance theory, numerical methods, and approximation techniques to provide the reader with an integrated approach to the process of designing and implementing industrial-strength models for fixed income security valuation and hedging. Aiming to bridge the gap between advanced theoretical models and real-life trading applications, the pragmatic, yet rigorous, approach taken in this book will appeal to students, academics, and professionals working in quantitative finance. Volume I provides the theoretical and computational foundations for the series, emphasizing the construction of efficient grid- and simulation-based methods for contingent claims pricing.