Book description
Statistics for Finance develops students’ professional skills in statistics with applications in finance. Developed from the authors’ courses at the Technical University of Denmark and Lund University, the text bridges the gap between classical, rigorous treatments of financial mathematics that rarely connect concepts to data and books on econometrics and time series analysis that do not cover specific problems related to option valuation.
The book discusses applications of financial derivatives pertaining to risk assessment and elimination. The authors cover various statistical and mathematical techniques, including linear and nonlinear time series analysis, stochastic calculus models, stochastic differential equations, Itō’s formula, the Black–Scholes model, the generalized method-of-moments, and the Kalman filter. They explain how these tools are used to price financial derivatives, identify interest rate models, value bonds, estimate parameters, and much more.
This textbook will help students understand and manage empirical research in financial engineering. It includes examples of how the statistical tools can be used to improve value-at-risk calculations and other issues. In addition, end-of-chapter exercises develop students’ financial reasoning skills.
Table of contents
- Preliminaries
- Preface
- Author biographies
- Chapter 1 Introduction
- Chapter 2 Fundamentals
- Chapter 3 Discrete time finance
- Chapter 4 Linear time series models
-
Chapter 5 Nonlinear time series models
- 5.1 Introduction
- 5.2 Aim of model building
- 5.3 Qualitative properties of the models
- 5.4 Parameter estimation
- 5.5 Parametric models
- 5.6 Model identification
- 5.7 Prediction in nonlinear models
- 5.8 Applications of nonlinear models
- 5.9 Problems
- Chapter 6 Kernel estimators in time series analysis
- Chapter 7 Stochastic calculus
- Chapter 8 Stochastic differential equations
- Chapter 9 Continuous-time security markets
- Chapter 10 Stochastic interest rate models
- Chapter 11 Term structure of interest rates
- Chapter 12 Discrete time approximations
- Chapter 13 Parameter estimation in discretely observed SDEs
-
Chapter 14 Inference in partially observed processes
- 14.1 Introduction
- 14.2 Model
- 14.3 Exact filtering
- 14.4 Conditional moment estimators
- 14.5 Kalman filter
- 14.6 Approximate filters
- 14.7 State filtering and prediction
- 14.8 Unscented Kalman Filter
- 14.9 A maximum likelihood method
- 14.10 Sequential Monte Carlo filters
- 14.11 Application of non-linear filters
- 14.12 Problems
- Appendix A Projections in Hilbert spaces
- Appendix B Probability theory
- Bibliography
Product information
- Title: Statistics for Finance
- Author(s):
- Release date: April 2015
- Publisher(s): CRC Press
- ISBN: 9781482229004
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