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A01=Erik Lindstrom
A01=Henrik Madsen
A01=Jan Nygaard Nielsen
Affine Term Structure Models
Arbitrage Free Price
Arch Model
Author_Erik Lindstrom
Author_Henrik Madsen
Author_Jan Nygaard Nielsen
Black-Scholes model
Category=KF
Cir Process
Conditional Expectation
course in econometrics
course in financial mathematics
Cox Ingersoll Ross Model
Driving Wiener Processes
econometrics
eq_bestseller
eq_business-finance-law
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Equivalent Martingale Measure
estimation theory
European Call Option
Exchange Rates
financial derivatives
financial engineering
financial reasoning
Foreign Exchange Rates
GARCH Model
generalized method-of-moments
GMM Estimator
GMM Method
interest rate models
Kalman filter
Linear SDE
Martingale Measure
option valuation
Ordinary Differential Equation
risk assessment
risk assessment and elimination
statistical tools
Stochastic Differential Equations
Stochastic Integral
Stochastic Interest Rate Models
Stochastic Taylor Expansion
Stochastic Volatility Models
Strict White Noise
time series analysis
value-at-risk calculations
Vasicek Model
Wiener Process

Product details

  • ISBN 9780367738372
  • Weight: 710g
  • Dimensions: 156 x 234mm
  • Publication Date: 18 Dec 2020
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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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.

Erik Lindström is an associate professor in the Centre for Mathematical Sciences at Lund University. His research ranges from statistical methodology (primarily time series analysis in discrete and continuous time) to financial mathematics as well as problems related to energy markets. He earned a PhD in mathematical statistics from Lund Institute of Technology/Lund University.

Henrik Madsen is a professor and head of the Section for Dynamical Systems in the Department for Applied Mathematics and Computer Sciences at the Technical University of Denmark. An elected member of the ISI and IEEE, he has authored or co-authored 480 papers and 11 books in areas including mathematical statistics, time series analysis, and the integration of renewables in electricity markets. He earned a PhD in statistics from the Technical University of Denmark.

Jan Nygaard Nielsen is a principal architect at Netcompany, a Danish IT and business consulting firm. He earned a PhD from the Technical University of Denmark.