Risk Measurement, Econometrics and Neural Networks

1. Front Matter

   Pages I-XI

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2. Nonparametric Smoothing and Quantile Estimation in Time Series

   • Klaus Abberger, Yuanhua Feng, Siegfried Heiler

   Pages 1-16

3. Development of a Credit-Standing-Indicator for Companies Based on Financial Statements and Business Information with Backpropagation-Networks

   • Jörg Baetge, Carsten Uthoff

   Pages 17-38

4. Data Warehousing and OLAP: Delivering Just-In-Time Information for Decision Support

   • Christoph A. Breitner

   Pages 39-52

5. Financial Calculations on the Net

   • Wolfgang Härdle, Stefan Sperlich

   Pages 53-56

6. The Durbin-Watson Test for Neural Regression Models

   • William Holt, Paul Refenes

   Pages 57-68

7. Neuro-Fuzzy Methods in Finance Applied to the German Stock Index DAX

   • Rudolf Kruse, Stefan Siekmann, Ralph Neuneier, Hans Georg Zimmermann

   Pages 69-82

8. Statistical Process Control and its Application in Finance

   • Thomas Severin, Wolfgang Schmid

   Pages 83-104

9. An Analysis of the Financing Behavior of German Stock Corporations Using Artificial Neural Networks

   • Manfred Steiner, Sebastian Schneider, J. Benedict Wolf

   Pages 105-146

10. Portfolio Analysis Based on the Shortfall Concept

    • Rainer Matthes, Michael Schröder

    Pages 147-160

11. Basics of Statistical VaR-Estimation

    • Thomas Ridder

    Pages 161-187

12. On the Accuracy of VaR Estimates Based on the Variance-Covariance Approach

    • Rakhal D. Davé, Gerhard Stahl

    Pages 189-232

13. Confidence Intervals for the Value-at-Risk

    • Stefan Huschens

    Pages 233-244

14. Regulatory Framework for the Risk Management of German Credit Institutions

    • Hermann Schulte-Mattler

    Pages 245-257

15. Measuring and Managing Credit Portfolio Risk

    • Thomas C. Wilson

    Pages 259-306

16. Back Matter

    Pages 307-307

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This book comprises the articles of the 6th Econometric Workshop in Karlsruhe, Germany. In the first part approaches from traditional econometrics and innovative methods from machine learning such as neural nets are applied to financial issues. Neural Networks are successfully applied to different areas such as debtor analysis, forecasting and corporate finance. In the second part various aspects from Value-at-Risk are discussed. The proceedings describe the legal framework, review the basics and discuss new approaches such as shortfall measures and credit risk.

• Credit Risk
• Econometrics
• Forecasting
• Neural Networks
• RM
• Risikomessung
• Risk Management
• Risk Measurement
• STATISTICA
• quantitative finance

• Institut für Statistik und Mathematische Wirtschaftstheorie, Universität Karlsruhe, Karlsruhe, Germany

  Georg Bol

• Daimler Benz AG, Ulm, Germany

  Gholamreza Nakhaeizadeh

• Südwestdeutsche Genossenschaftszentralbank AG, Karlsruhe, Germany

  Karl-Heinz Vollmer

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