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...

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...

High-dimensional regression problems which reveal dynamic behavior are typicallyanalyzed by time propagation of a few number of factors. The inference on thewhole system is then based on the low-dimensional time series analysis. Such highdimensional problems occur frequently in many different...

Predicting default probabilities is important for firms and banks to operate successfully and to estimate their specific risks. There are many reasons to use nonlinear techniques for predicting bankruptcy from financial ratios. Here we propose the so called Support Vector Machine (SVM) to...

Measuring dependence in a multivariate time series is tantamount to modelling its dynamic structure in space and time. In the context of a multivariate normally distributed time series, the evolution of the covariance (or correlation) matrix over time describes this dynamic. A wide variety of...

Motivated by the recurrent Neural Networks, this paper proposes a recurrent Support Vector Regression (SVR) procedure to forecast nonlinear ARMA model based simulated data and real data of financial returns. The forecasting ability of the recurrent SVR is compared with three competing methods,...

Numerous studies have tried to provide a better understanding of firm-level investment behaviour using econometric models. The model specification of more recent studies has been based on two main approaches. The first, the real options approach, focuses on irreversibility and uncertainty in...

A small strand of recent literature is occupied with identifying simultaneity in multipleequation systems through autoregressive conditional heteroscedasticity. Since thisapproach assumes that the structural innovations are uncorrelated, any contemporaneousconnection of the endogenous variables...

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...

According to housing investment models, house prices and replacement costshould have an equilibrating relationship. Previous empirical work|mainlybased on aggregate-level data|has found only little evidence of such a relationship.By using a unique data set, covering transactions of...