Statistics for Finance
 Responsibility
 by Erik Lindstrom, Henrik Madsen and Jan Nygaard Nielsen.
 Edition
 First edition.
 Publication
 Boca Raton, FL : Chapman and Hall/CRC, [2018].
 Copyright notice
 ©2015.
 Physical description
 1 online resource (384 pages) : 74 illustrations, text file, PDF.
 Series
 Chapman & Hall/CRC Texts in Statistical Science.
Online
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Description
Creators/Contributors
 Author/Creator
 Lindstrom, Erik, author.
 Contributor
 Madsen, Henrik, author.
 Nielsen, Jan Nygaard, author.
 Taylor and Francis.
Contents/Summary
 Contents

 IntroductionIntroduction to financial derivatives Financial derivativeswhats the big deal? Stylized factsOverview  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 functionAutocovariance functions Prediction in linear processes  NonLinear Time Series Models Introduction The aim of model buildingQualitative properties of the models Parameter estimationParametric 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 It 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 theoryModern 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 expansionConvergence Discretization schemes Multilevel Monte Carlo Simulation of SDEs  Parameter Estimation in Discretely Observed SDEsIntroduction High frequency methods Approximate methods for linear and nonlinear modelsState dependent diffusion term MLE for nonlinear diffusionsGeneralized method of moments (GMM) Model validation for discretely observed SDEs  Inference in Partially Observed Processes IntroductionThe model Exact filtering Conditional moment estimators Kalman filter Approximate filters State filtering and predictionThe 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.
 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
 Subjects
 Finance > Statistical methods.
 Statistics.
 Finance.
 MATHEMATICS / General.
 MATHEMATICS / Probability & Statistics / General.
 BlackScholes model.
 course in econometrics.
 course in financial mathematics.
 estimation theory.
 financial derivatives.
 financial engineering.
 financial reasoning.
 generalized methodofmoments.
 Kalman filter.
 option valuation.
 risk assessment and elimination.
 time series analysis.
 valueatrisk calculations.
Bibliographic information
 Publication date
 2018
 Copyright date
 2015
 Series
 Chapman & Hall/CRC Texts in Statistical Science
 Note
 Also available in print format.
 ISBN
 9781315372204 (ebook : PDF)