This paper proposes a nonparametric test of Granger causality in quantile. Zheng (1998,
Econometric Theory 14, 123–138) studied the idea to reduce the problem of testing a quantile …

… Gentle Wolfgang Karl Härdle Yuichi Mori … Wolfgang Karl Härdle Humboldt-Universität
zu Berlin Lv Bortkiewicz Chair of Statistics CASE Centre f. Appl. Stat. … Wolfgang Karl …

We propose a semiparametric factor model, which approximates the implied volatility
surface (IVS) in a finite dimensional function space. Unlike standard principal component …

Recently, support vector machine (SVM), a novel artificial neural network (ANN), has been
successfully used for financial forecasting. This paper deals with the application of SVM in …

It is common practice to identify the number and sources of shocks that move, eg, ATM implied
volatilities by principal components analysis. This approach, however, is likely to result in …

Predicting default risk is important for firms and banks to operate successfully. There are many
reasons to use nonlinear techniques for predicting bankruptcy from financial ratios. Here …

The gargantuan plethora of opinions, facts, and tweets on financial business offers the
opportunity to test and analyze the influence of such text sources on future directions of stocks. It …

We propose a new nonlinear classification method based on a Bayesian “sum-of-trees”
model, the Bayesian Additive Classification Tree (BACT), which extends the Bayesian Additive …

Functional data analysis (FDA) has become an important area of statistics research in the
recent decade, yet a smooth simultaneous confidence corridor (SCC) does not exist in the …

Principal component analysis (PCA) is a widely used dimension reduction tool in high-dimensional
data analysis. In risk quantification in finance, climatology and many other …