Risk Measurement, Econometrics and Neural Networks
Selected Articles of the 6th Econometric-Workshop in Karlsruhe, Germany

by Georg Bol (Editor), Gholamreza Nakhaeizadeh (Editor), Karl-Heinz Vollmer (Editor)



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.

Table of contents (14 chapters)

Nonparametric Smoothing and Quantile Estimation in Time Series

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

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

Financial Calculations on the Net

The Durbin-Watson Test for Neural Regression Models

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

Statistical Process Control and its Application in Finance

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

Portfolio Analysis Based on the Shortfall Concept

Basics of Statistical VaR-Estimation

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

Confidence Intervals for the Value-at-Risk

Regulatory Framework for the Risk Management of German Credit Institutions

Measuring and Managing Credit Portfolio Risk