Computational Statistics is an international journal that fosters the publication of applications and methodological research in the field of computational statistics. In this article, we will discuss the motivation, history, some specialties, and the future scope of this journal.

Die Rekordzahlen an Unternehmensinsolvenzen, die schlechte Ertragslage der deutschen Kreditinstitute in den vergangenen Jahren und der von Basel II ausgehende Druck zur Verwendung von realitätsnahen Ausfallwahrscheinlichkeiten haben es überdeutlich gemacht: Der Bedarf an leistungsfähigen...

Deep learning has substantially advanced the state of the art in computer vision, natural language processing, and other fields. The paper examines the potential of deep learning for exchange rate forecasting. We systematically compare long short-term memory networks and gated recurrent units to...

Modern econometrics requires implementation of highly specialized software. In contrast to mathematical arguments used in implementing new econometric techniques the corresponding software algorithms require specific platforms. The specialization of hardware and software, in fact, seriously...

Stochastic Volatility (SV) models are widely used in financial applications. To decide whether standard parametric restrictions are justified for a given dataset, a statistical test is required. In this paper, we develop such a test based on the linear state space representation. We provide a...

It is common practice to identify the number and sources of shocks that move implied volatilities across space and time by applying Principal Components Analysis (PCA) to pooled covariance matrices of changes in implied volatilities. This approach, however, is likely to result in a loss of...

We develop analysis of deviance tools for generalized partial linear models based on local polynomial fitting. Assuming a canonical link, we propose expressions for both local and global analysis of deviance, which admit an additivity property that reduces to ANOVA decompositions in the Gaussian...