Erik Lindström, Henrik Madsen, Jan Nygaard Nielsen

Statistics for Finance

• Gebundenes Buch

Produktbeschreibung

This text develops students' professional skills in statistics with applications in finance. It 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 authors explain how various statistical and mathematical tools are used to price financial derivatives, identify interest rate models, and much more. The book includes examples, case studies, cross references, and end-of-chapter problems.

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Produktdetails

• Verlag: CRC Press
• Seitenzahl: 384
• Erscheinungstermin: 31. März 2015
• Englisch
• Abmessung: 236mm x 155mm x 25mm
• Gewicht: 658g
• ISBN-13: 9781482228991
• ISBN-10: 1482228998
• Artikelnr.: 40925750

Autorenporträt

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.

Inhaltsangabe

Introduction. Fundamentals. Discrete Time Finance. Linear Time Series
Models. Nonlinear Time Series Models. Kernel Estimators in Time Series
Analysis. Stochastic Calculus. Stochastic Differential Equations.
Continuous Time Security Markets. Stochastic Interest Rate Models. The Term
Structure of Interest Rates. Discrete Time Approximations. Parameter
Estimation in Discretely Observed SDEs. Inference in Partially Observed
Processes. Appendices. Bibliography.