Statistics for finance
 Responsibility
 Erik Lindström, Henrik Madsen, Jan Nygaard Nielsen.
 Publication
 Boca Raton, FL : CRC Press, [2015]
 Copyright notice
 ©2015
 Physical description
 1 online resource (1 volume) : illustrations
 Series
 Texts in statistical science.
Online
More options
Description
Creators/Contributors
 Author/Creator
 Lindström, Erik, author.
 Contributor
 Madsen, Henrik, author.
 Nielsen, Jan Nygaard, author.
Contents/Summary
 Bibliography
 Includes bibliographical references (pages 345259).
 Contents

 Introduction Introduction to financial derivatives Financial derivativeswhat's the big deal? Stylized facts Overview
 Fundamentals Interest rates Cash flows Continuously compounded interest rates Interest rate options: caps and floors
 DiscreteTime Finance The binomial one period model The one period model The multi period model
 Linear Time Series Models Introduction Linear systems in the time domain Linear stochastic processes Linear processes with a rational transfer function Autocovariance functions Prediction in linear processes
 NonLinear Time Series Models Introduction The aim of model building Qualitative properties of the models Parameter estimation Parametric models Model identification Prediction in nonlinear models Applications of nonlinear models
 Kernel Estimators in Time Series Analysis Nonparametric estimation Kernel estimators for time series Kernel estimation for regression Applications of kernel estimators
 Stochastic Calculus Dynamical systems The Wiener process Stochastic Integrals Ito stochastic calculus Extensions to jump processes
 Stochastic Differential Equations Stochastic differential equations Analytical solution methods FeynmanKac representation Girsanov measure transformation
 ContinuousTime Security Markets From discrete to continuous time Classical arbitrage theory Modern approach using martingale measures Pricing Model extensions Computational methods
 Stochastic Interest Rate Models Gaussian onefactor models A general class of onefactor models Timedependent models Multifactor and stochastic volatility models
 The Term Structure of Interest Rates Basic concepts The classical approach The term structure for specific models HeathJarrowMorton framework Credit models Estimation of the term structurecurvefitting
 DiscreteTime Approximations Stochastic Taylor expansion Convergence Discretization schemes Multilevel Monte Carlo Simulation of SDEs
 Parameter Estimation in Discretely Observed SDEs Introduction High frequency methods Approximate methods for linear and nonlinear models State dependent diffusion term MLE for nonlinear diffusions Generalized method of moments (GMM) Model validation for discretely observed SDEs
 Inference in Partially Observed Processes Introduction The model Exact filtering Conditional moment estimators Kalman filter Approximate filters State filtering and prediction The unscented Kalman filter A maximum likelihood method Sequential Monte Carlo filters Application of nonlinear filters
 Appendix A: Projections in Hilbert Spaces Appendix B: Probability Theory
 Bibliography
 Problems appear at the end of each chapter.
 (source: Nielsen Book Data)
 Publisher's summary

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, Ito's formula, the BlackScholes model, the generalized methodofmoments, 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 valueatrisk calculations and other issues. In addition, endofchapter exercises develop students' financial reasoning skills.
(source: Nielsen Book Data)
Subjects
Bibliographic information
 Publication date
 2015
 Series
 Texts in Statistical Science
 Note
 "A Chapman & Hall book."
 ISBN
 9781482229004 (electronic bk.)
 1482229005 (electronic bk.)
 1482228998
 9781482228991
 1482229005
 9781482229028
 1482229021
 9781482229011
 1482229013
 1482228998
 9781482228991
 DOI
 10.1201/b18357