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Item specifics
- Condition
- Book Title
- Statistical Methods for Financial Engineering
- ISBN
- 9781439856949
- Subject Area
- Mathematics, Business & Economics
- Publication Name
- Statistical Methods for Financial Engineering
- Publisher
- CRC Press LLC
- Item Length
- 9.5 in
- Subject
- Finance / Financial Engineering, Finance / General, Probability & Statistics / General, General
- Publication Year
- 2013
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Item Height
- 1.1 in
- Item Weight
- 28.9 Oz
- Item Width
- 6.5 in
- Number of Pages
- 496 Pages
Statistical Methods for Financial Engineering, Hardcover by Remillard, Bruno,…
About this product
Product Identifiers
Publisher
CRC Press LLC
ISBN-10
143985694X
ISBN-13
9781439856949
eBay Product ID (ePID)
7038488497
Product Key Features
Number of Pages
496 Pages
Publication Name
Statistical Methods for Financial Engineering
Language
English
Subject
Finance / Financial Engineering, Finance / General, Probability & Statistics / General, General
Publication Year
2013
Type
Textbook
Subject Area
Mathematics, Business & Economics
Format
Hardcover
Dimensions
Item Height
1.1 in
Item Weight
28.9 Oz
Item Length
9.5 in
Item Width
6.5 in
Additional Product Features
Intended Audience
College Audience
LCCN
2012-050917
Reviews
“… a successful attempt to cover the main statistical tools and methods used for practical purposes in financial engineering. In contrast to those few existing books on the implementation of stochastic models in financial markets, this monograph covers a vast number of topics from mathematical finance … can be used by practitioners as a reference book, but also it can serve as an excellent textbook for training quantitative analysts …” –Mathematical Reviews, September 2014, “… an interesting book with many features that are not easily found elsewhere. … libraries will certainly want to acquire a copy. … there are plenty of points at which even experts will pick up new ideas.” –J. Michael Steele, Journal of the American Statistical Association, September 2014, Vol. 109 “… a successful attempt to cover the main statistical tools and methods used for practical purposes in financial engineering. In contrast to those few existing books on the implementation of stochastic models in financial markets, this monograph covers a vast number of topics from mathematical finance … can be used by practitioners as a reference book, but also it can serve as an excellent textbook for training quantitative analysts …” –Mathematical Reviews, September 2014
Illustrated
Yes
Table Of Content
Black-Scholes Model The Black-Scholes Model Dynamic Model for an Asset Estimation of Parameters Estimation Errors Black-Scholes Formula Greeks Estimation of Greeks using the Broadie-Glasserman Methodologies Multivariate Black-Scholes Model Black-Scholes Model for Several Assets Estimation of Parameters Estimation Errors Evaluation of Options on Several Assets Greeks Discussion of the Black-Scholes Model Critiques of the Model Some Extensions of the Black-Scholes Model Discrete Time Hedging Optimal Quadratic Mean Hedging Measures of Risk and Performance Measures of Risk Estimation of Measures of Risk by Monte Carlo Methods Measures of Risk and the Delta-Gamma Approximation Performance Measures Modeling Interest Rates Introduction Vasicek Model Cox-Ingersoll-Ross (CIR) Model Other Models for the Spot Rates Lévy Models Complete Models Stochastic Processes with Jumps Lévy Processes Examples of Lévy Processes Change of Distribution Model Implementation and Estimation of Parameters Stochastic Volatility Models GARCH Models Estimation of Parameters Duan Methodology of Option Pricing Stochastic Volatility Model of Hull-White Stochastic Volatility Model of Heston Copulas and Applications Weak Replication of Hedge Funds Default Risk Modeling Dependence Bivariate Copulas Measures of Dependence Multivariate Copulas Families of Copulas Estimation of the Parameters of Copula Models Tests of Independence Tests of Goodness-of-Fit Example of Implementation of a Copula Model Filtering Description of the Filtering Problem Kalman Filter IMM Filter General Filtering Problem Computation of the Conditional Densities Particle Filters Applications of Filtering Estimation of ARMA Models Regime-Switching Markov Models Replication of Hedge Funds Appendix A: Probability Distributions Appendix B: Estimation of Parameters Index Suggested Reading, Exercises, Assignment Questions, Appendices, and References appear at the end of each chapter.
Synopsis
While many financial engineering books are available, the statistical aspects behind the implementation of stochastic models used in the field are often overlooked or restricted to a few well-known cases. Statistical Methods for Financial Engineering guides current and future practitioners on implementing the most useful stochastic models used in financial engineering. After introducing properties of univariate and multivariate models for asset dynamics as well as estimation techniques, the book discusses limits of the Black-Scholes model, statistical tests to verify some of its assumptions, and the challenges of dynamic hedging in discrete time. It then covers the estimation of risk and performance measures, the foundations of spot interest rate modeling, Lévy processes and their financial applications, the properties and parameter estimation of GARCH models, and the importance of dependence models in hedge fund replication and other applications. It concludes with the topic of filtering and its financial applications. This self-contained book offers a basic presentation of stochastic models and addresses issues related to their implementation in the financial industry. Each chapter introduces powerful and practical statistical tools necessary to implement the models. The author not only shows how to estimate parameters efficiently, but he also demonstrates, whenever possible, how to test the validity of the proposed models. Throughout the text, examples using MATLAB® illustrate the application of the techniques to solve real-world financial problems. MATLAB and R programs are available on the author’s website., While many financial engineering books are available, the statistical aspects behind the implementation of stochastic models used in the field are often overlooked or restricted to a few well-known cases. Statistical Methods for Financial Engineering guides current and future practitioners on implementing the most useful stochastic models used in financial engineering. After introducing properties of univariate and multivariate models for asset dynamics as well as estimation techniques, the book discusses limits of the Black-Scholes model, statistical tests to verify some of its assumptions, and the challenges of dynamic hedging in discrete time. It then covers the estimation of risk and performance measures, the foundations of spot interest rate modeling, Lévy processes and their financial applications, the properties and parameter estimation of GARCH models, and the importance of dependence models in hedge fund replication and other applications. It concludes with the topic of filtering and its financial applications. This self-contained book offers a basic presentation of stochastic models and addresses issues related to their implementation in the financial industry. Each chapter introduces powerful and practical statistical tools necessary to implement the models. The author not only shows how to estimate parameters efficiently, but he also demonstrates, whenever possible, how to test the validity of the proposed models. Throughout the text, examples using MATLAB(R) illustrate the application of the techniques to solve real-world financial problems. MATLAB and R programs are available on the author’s website., While many financial engineering books are available, the statistical aspects behind the implementation of stochastic models used in the field are often overlooked or restricted to a few well-known cases. Statistical Methods for Financial Engineering guides current and future practitioners on implementing the most useful stochastic models used in financial engineering. After introducing properties of univariate and multivariate models for asset dynamics as well as estimation techniques, the book discusses limits of the Black-Scholes model, statistical tests to verify some of its assumptions, and the challenges of dynamic hedging in discrete time. It then covers the estimation of risk and performance measures, the foundations of spot interest rate modeling, L vy processes and their financial applications, the properties and parameter estimation of GARCH models, and the importance of dependence models in hedge fund replication and other applications. It concludes with the topic of filtering and its financial applications. This self-contained book offers a basic presentation of stochastic models and addresses issues related to their implementation in the financial industry. Each chapter introduces powerful and practical statistical tools necessary to implement the models. The author not only shows how to estimate parameters efficiently, but he also demonstrates, whenever possible, how to test the validity of the proposed models. Throughout the text, examples using MATLAB(R) illustrate the application of the techniques to solve real-world financial problems. MATLAB and R programs are available on the author’s website.
LC Classification Number
HG176.7
Price : 174.41
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