Energy Risk Modeling | E-Book | www.sack.de
E-Book

E-Book, Englisch, 247 Seiten, eBook

Reihe: Finance and Capital Markets Series

Energy Risk Modeling

Applied Modeling Methods for Risk Managers
2005
ISBN: 978-0-230-52378-4
Verlag: Palgrave Macmillan UK
Format: PDF
Kopierschutz: 1 - PDF Watermark

Applied Modeling Methods for Risk Managers

E-Book, Englisch, 247 Seiten, eBook

Reihe: Finance and Capital Markets Series

ISBN: 978-0-230-52378-4
Verlag: Palgrave Macmillan UK
Format: PDF
Kopierschutz: 1 - PDF Watermark



Energy Risk Modeling is a primer on statistical methods for managers, students and anybody interested in the field. Illustrated through elementary and more advanced statistical Methods, it is primarily aimed at those individuals who need a gentle introduction in how to go about using statistical methods for modeling energy price risk. Statistical ideas are presented by outlining the necessary concepts and illustrating how these ideas can be implemented. This is the first energy risk book on the market to focus specifically on the role of statistical methods. Its practical approach makes the book a very useful reference and an interesting read.

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Zielgruppe


Research


Autoren/Hrsg.


Weitere Infos & Material


The Statistical Nature of Energy Risk Modeling Introduction to Applied Probability for Energy Risk Management Descriptive Statistics of Energy Prices and Returns Inferential Statistics Fitting Price Distributions Non Parametric Density Estimation Applied Regression Multiple Regression Misspecification Testing Nonlinear and Limited Dependent Models Price Volatility Stochastic Differential Equations


NIGEL DA COSTA LEWIS is a Professional Writer who has many years work experience in quantitative, econometric and statistical methods. He has worked in the City of London, on Wall Street and in academia. Dr Lewis is the author of a number of books for risk managers, including Operational Risk with Excel and VBA (Wiley) and the classic text Market Risk Modelling (Risk Books). He also holds an award winning PhD from the University of Cambridge and four Master's degrees in Statistics, Economics, Finance, and Advanced Computer Science, all from the University of London, UK.



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