Titlebook: Risk Measurement, Econometrics and Neural Networks; Selected Articles of Georg Bol,Gholamreza Nakhaeizadeh,Karl-Heinz Vollm Conference proc

掺假

Basics of Statistical VaR-Estimation,tics. This paper gives a survey of common procedures to analyze risk exposure by VaR and studies basic statistical properties of various VaR-estimators. An application to foreign exchange rate risk gives insight into the accuracy of those VaR-strategies.

G-spot

影响深远

1431-1933 etrics 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 pr

Irascible

Vsd168

https://doi.org/10.1007/978-3-642-58272-1Credit Risk; Econometrics; Forecasting; Neural Networks; RM; Risikomessung; Risk Management; Risk Measureme

Initiative

The Durbin-Watson Test for Neural Regression Models,The need to subject neural regression models to residual diagnostic tests is justified. A generalisation of the Durbin-Watson statistic is introduced for neural network regression models. The distribution of the statistic is estimated using approximation and exact methods (Imhof algorithm).

HAWK

Statistical Process Control and its Application in Finance,Analyzing a financial time series it is of great interest to monitor changes in its structure. In this paper it is shown how control charts can be used to detect such positions. Several control schemes for GARCH processes are introduced and compared with each other. Furthermore these methods are applied to stock market returns.

残忍

Confidence Intervals for the Value-at-Risk,Exact and asymptotic confidence intervals for the Value-at-Risk (VaR) are derived in a parametric context with linear portfolio structure and multinormal distributed returns..

affinity

Data Warehousing and OLAP: Delivering Just-In-Time Information for Decision Support,s an integrated view on all business-relevant data, and thus, provides the data foundation upon which an enterprise-wide information system can be based. OLAP is an analytical processing technology, which creates new business information from existing data, through a rich set of business transformations and numerical calculations.

失望未来

978-3-7908-1152-0Springer-Verlag Berlin Heidelberg 1998