Implied volatility is one of the key issues in modern quantitative finance, since plain vanilla option prices contain vital information for pricing and hedging of exotic and illiquid options. European plain vanilla options are nowadays widely traded, which results in a great amount of...

A primary goal in modelling the implied volatility surface (IVS) for pricing andhedging aims at reducing complexity. For this purpose one fits the IVS each dayand applies a principal component analysis using a functional norm. This approach, however, neglects the degenerated string structure of...

The pricing accuracy and pricing performance of local volatility models cruciallydepends on absence of arbitrage in the implied volatility surface: an input impliedvolatility surface that is not arbitrage-free invariably results in negative transitionprobabilities and/ or negative local...

In recent years, support vector regression (SVR), a novel neural network (NN) technique, has been successfully used for financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH method is proposed and is compared with a...

A great proportion of stock dynamics can be explained using publicly availableinformation. The relationship between dynamics and public information may be ofnonlinear character. In this paper we offer an approach to stock picking by employing so-called decision trees and applying them to XETRA...

Empirical studies have shown that a large number of financial asset returns exhibit fat tails and are often characterized by volatility clustering and asymmetry. Also revealed as a stylized fact is Long memory or long range dependence in market volatility, with significant impact on pricing and...

In the era of Basel II a powerful tool for bankruptcy prognosis isvital for banks. The tool must be precise but also easily adaptable tothe bank's objections regarding the relation of false acceptances (TypeI error) and false rejections (Type II error). We explore the suitabil-ity of Smooth...

Independent component analysis (ICA) is a modern factor analysis tool developed in the last two decades. Given p-dimensional data, we search for that linear combination of data which creates (almost) independent components. Here copulae are used to model the p-dimensional data and then...

We propose a new nonlinear classification method based on a Bayesian "sum-of-trees" model, the Bayesian Additive Classification Tree (BACT), whichextends the Bayesian Additive Regression Tree (BART) method into the classification context. Like BART, the BACT is a Bayesian nonparametric...

This paper offers a new method for estimation and forecasting of the linear and nonlinear time series when the stationarity assumption is violated. Our general local parametric approach particularly applies to general varying-coefficient parametric models, such as AR or GARCH, whose coefficients...