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.

Inhalt

K. Abberger, Y.H. Feng, S. Heiler: Nonparametric Smoothing and Quantile Estimation in Time Series.- J. Baetge, C. Uthoff: Development of a Credit-Standing-Indicator for Companies Based on Financial Statements and Business Information with Backpropagation-Networks.- C.A. Breitner: Data Warehousing and OLAP: Delivering Just-in-Time Information for Decision Support.- W. Härdle, S. Sperlich: Financial Calculations on the Net.- W. Holt, P. Refenes: The Durbin-Watson Test for Neural Regression Models.- R. Kruse, S. Siekmann, R. Neuneiner, H.-G. Zimmermann: Neuro-Fuzzy Methods in Finance Applied to the German Stock Index DAX.- T. Severin, W. Schmid: Statistical Process Control and its Applications in Finance.- M. Steiner, S. Schneider, J.B. Wolf: An Analysis of the Financing Behavior of German Stock Corporations Using Artificial Neural Networks.- R. Matthes, M. Schröder: Portfolio Anaylsis Based on the Shortfall Concept.- T. Ridder: Basics of Statistical VaR-Estimation.- R.D. Davé, G. Stahl: On the Accuracy of VaR Estimates Based on the Variance-Covariance Approach.- S. Huschens: Confidence Intervals for the Value-at-Risk.- H. Schulte-Mattler: Regulatory Framework for the Risk Management of German Credit Institutions.- T.C. Wilson: Measuring and Managing Credit Portfolio Risk.