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Financial Econometrics

Financial Econometrics

From Basics to Advanced Modeling Techniques

Svetlozar T. Rachev, Stefan Mittnik, Frank J. Fabozzi, Sergio M. Focardi, Teo Jasic - Collection Wiley Finance

576 pages, parution le 30/01/2007

Résumé

A comprehensive guide to financial econometrics

Financial econometrics is a quest for models that describe financial time series such as prices, returns, interest rates, and exchange rates. In Financial Econometrics, readers will be introduced to this growing discipline and the concepts and theories associated with it, including background material on probability theory and statistics. The experienced author team uses real-world data where possible and brings in the results of published research provided by investment banking firms and journals. Financial Econometrics clearly explains the techniques presented and provides illustrative examples for the topics discussed.

L'auteur - Svetlozar T. Rachev

Svetlozar T. Rachev, PhD, DR. SCI, is currently Chair-Professor at the University of Karlsruhe in the School of Economics and Business Engineering and Professor Emeritus at the University of California. He is also the founder of Bravo Risk Management Group and Chief Scientist of FinAnalytica.

L'auteur - Stefan Mittnik

Stefan Mittnik studied at the Technical University Berlin, Germany, the University of Sussex, England, and at Washington University in St. Louis, where he received his doctorate degree in economics. He is now Professor of Financial Econometrics at the University of Munich, Germany, and research director at the Ifo Institute for Economic Research in Munich. Prior to joining the University of Munich he taught at SUNYStony Brook, New York, the University of Kiel, Germany, and held several visiting positions, including that of Fulbright Distinguished Chair at Washington University in St. Louis. His research focuses on financial econometrics, risk management, and portfolio optimization. In addition to purely academic interests, Professor Mittnik directs the risk management program at the Center for Financial Studies in Frankfurt, Germany, and is co-founder of the Institut für Quantitative Finanzanalyse (IQF) in Kiel, where he now chairs the scientific advisory board.

L'auteur - Frank J. Fabozzi

Frank J. Fabozzi, Ph.D., CFA, CPA, is the Frederick Frank Adjunct Professor of Finance at Yale University's School of Management. One of the world's foremost authorities on fixed income securities and derivative instruments, Dr. Fabozzi is editor of the Journal of Portfolio Management and the bestselling author of more than forty books, including the acclaimed The Handbook of Fixed Income Securities. Prior to joining the Yale faculty, he was on the faculty of MIT's Sloan School of Management.

L'auteur - Sergio M. Focardi

Sergio Focardi is a founding partner of The Intertek Group, a Paris-based firm providing consulting on advanced mathematical methods in banking and finance, and a cofounder of CINEF (Center for Interdisciplinary Research in Economics and Finance) at the University of Genoa, Italy. Focardi's research interests focus on statistical arbitrage, dynamic factor analysis, and financial modeling in a multiple heterogeneous interacting agents framework. He has published numerous articles and coauthored the books Modeling the Market: New Theories and Techniques and Risk Management: Framework, Methods, and Practice (both published by Wiley). Focardi holds a degree in electronic engineering from the University of Genoa.

L'auteur - Teo Jasic

Teo Jasic earned his doctorate (Dr.rer.pol.) in economics from the University of Karlsruhe in 2006. He also holds an MSc degree from the National University of Singapore and a Dipl.-Ing. degree from the University of Zagreb. Currently, he is a Postdoctoral Research Fellow at the Chair of Statistics, Econometrics and Mathematical Finance at the University of Karlsruhe in the School of Economics and Business Engineering. He is also a senior manager in Financial & Risk Management Group of a leading international management consultancy firm in Frankfurt, Germany. His current professional and research interests are in the areas of asset management, risk management, and financial forecasting. Dr. Jasic has published more than a dozen research papers in internationally refereed journals.

Sommaire

  • Financial Econometrics: Scope and Methods.
    • The Data Generating Process.
    • Financial Econometrics at Work.
    • Time Horizon of Models.
    • Applications.
    • Appendix: Investment Management Process.
    • Concepts Explained in this Chapter (in order of presentation).
  • Review of Probability and Statistics.
    • Concepts of Probability.
    • Principles of Estimation.
    • Bayesian Modeling.
    • Appendix A: Information Structures.
    • Appendix B: Filtration.
    • Concepts Explained in this Chapter (in order of presentation).
  • Regression Analysis: Theory and Estimation.
    • The Concept of Dependence.
    • Regressions and Linear Models.
    • Estimation of Linear Regressions.
    • Sampling Distributions of Regressions.
    • Determining the Explanatory Power of a Regression.
    • Using Regression Analysis in Finance.
    • Stepwise Regression.
    • Nonnormality and Autocorrelation of the Residuals.
    • Pitfalls of Regressions.
    • Concepts Explained in this Chapter (in order of presentation) .
  • Selected Topics in Regression Analysis.
    • Categorical and Dummy Variables in Regression Models.
    • Constrained Least Squares.
    • The Method of Moments and its Generalizations.
    • Concepts Explained in this Chapter (in order of presentation).
  • Regression Applications in Finance.
    • Applications to the Investment Management Process.
    • A Test of Strong-Form Pricing Efficiency.
    • Tests of the CAPM.
    • Using the CAPM to Evaluate Manager Performance: The Jensen Measure.
    • Evidence for Multifactor Models.
    • Benchmark Selection: Sharpe Benchmarks.
    • Return-Based Style Analysis for Hedge Funds.
    • Hedge Fund Survival.
    • Bond Portfolio Applications.
    • Concepts Explained in this Chapter (in order of presentation).
  • Modeling Univariate Time Series.
    • Difference Equations.
    • Terminology and Definitions.
    • Stationarity and Invertibility of ARMA Processes.
    • Linear Processes.
    • Identification Tools.
    • Concepts Explained in this Chapter (in order of presentation).
  • Approaches to ARIMA Modeling and Forecasting.
    • Overview of Box-Jenkins Procedure.
    • Identification of Degree of Differencing.
    • Identification of Lag Orders.
    • Model Estimation.
    • Diagnostic Checking.
    • Forecasting.
    • Concepts Explained in this Chapter (in order of presentation).
  • Autoregressive Conditional Heteroskedastic Models.
    • ARCH Process.
    • GARCH Process.
    • Estimation of the GARCH Models.
    • Stationary ARMA-GARCH Models.
    • Lagrange Multiplier Test.
    • Variants of the GARCH Model.
    • GARCH Model with Student's t-Distributed Innovations.
    • Multivariate GARCH Formulations.
    • Appendix: Analysis of the Properties of the GARCH(1,1) Model.
    • Concepts Explained in this Chapter (in order of presentation).
  • Vector Autoregressive Models I.
    • VAR Models Defined.
    • Stationary Autoregressive Distributed Lag Models.
    • Vector Autoregressive Moving Average Models.
    • Forecasting with VAR Models.
    • Appendix: Eigenvectors and Eigenvalues.
    • Concepts Explained in this Chapter (in order of presentation).
  • Vector Autoregressive Models II.
    • Estimation of Stable VAR Models.
    • Estimating the Number of Lags.
    • Autocorrelation and Distributional Properties of Residuals.
    • VAR Illustration.
    • Concepts Explained in this Chapter (in order of presentation).
  • Cointegration and State Space Models.
    • Cointegration.
    • Error Correction Models.
    • Theory and Methods of Estimation of Nonstationary VAR Models.
    • State-Space Models.
    • Concepts Explained in this Chapter (in order of presentation).
  • Robust Estimation.
    • Robust Statistics.
    • Robust Estimators of Regressions.
    • Illustration: Robustness of the Corporate Bond Yield Spread Model.
    • Concepts Explained in this Chapter (in order of presentation).
  • Principal Components Analysis and Factor Analysis.
    • Factor Models.
    • Principal Components Analysis.
    • Factor Analysis.
    • PCA and Factor Analysis Compared.
    • Concepts Explained in this Chapter (in order of presentation).
  • Heavy-Tailed and Stable Distributions in Financial Econometrics.
    • Basic Facts and Definitions of Stable Distributions.
    • Properties of Stable Distributions.
    • Estimation of the Parameters of the Stable Distribution.
    • Applications to German Stock Data.
    • Appendix: Comparing Probability Distributions.
    • Concepts Explained in this Chapter (in order of presentation).
  • ARMA and ARCH Models with Infinite-Variance Innovations.
    • Infinite Variance Autoregressive Processes.
    • Stable GARCH Models.
    • Estimation for the Stable GARCH Model.
    • Prediction of Conditional Densities.
    • Concepts Explained in this Chapter (in order of presentation).
Voir tout
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Caractéristiques techniques

  PAPIER
Éditeur(s) Wiley
Auteur(s) Svetlozar T. Rachev, Stefan Mittnik, Frank J. Fabozzi, Sergio M. Focardi, Teo Jasic
Collection Wiley Finance
Parution 30/01/2007
Nb. de pages 576
Format 15,5 x 23,5
Couverture Relié
Poids 793g
Intérieur Noir et Blanc
EAN13 9780471784500
ISBN13 978-0-471-78450-0

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