The problem of robust estimation and multivariate outlier detection of the term structure of default intensity is considered. Both the multivariate Vasicek and CIR models, embedding the Kalman filter algorithm in a forward search context, are used to estimate default intensity. The focus is not...

The aim is to introduce a new econometric methodology for multi-output production frontiers. In the context of a system of frontier equations, a flexible multivariate distribution for the inefficiency error term is used. This multivariate distribution is constructed through a copula function...

A wavelet-based approach to test whether contagion occurred during the US subprime crisis of 2007 is proposed. After separately identifying contagion and interdependence through wavelet decomposition of the original returns series, the presence of contagion is assessed using a simple graphical...

Time varying volatilities in financial time series are commonly modeled by GARCH or by stochastic volatility models. Models with piecewise constant volatilities have been proposed recently as nonparametric alternatives. Following the latter approach, a procedure for online approximation of the...

It has been shown that vine copulas constructed from bivariate t copulas can provide good fits to multivariate financial asset return data. However, there might be stronger tail dependence of returns in the joint lower tail of assets than the upper tail. To this end, vine copula models with...

A new heteroskedastic hedonic regression model is suggested which takes into account time-varying volatility and is applied to a blue chips art market. A nonparametric local likelihood estimator is proposed, and this is more precise than the often used dummy variables method. The empirical...

Procedures based on the Generalized Method of Moments (GMM) are basic tools in modern econometrics. In most cases, the theory available for making inference with these procedures is based on first order asymptotic theory. It is well-known that the (first order) asymptotic distribution does not...

A dynamic multivariate periodic regression model for hourly data is considered. The dependent hourly univariate time series is represented as a daily multivariate time series model with 24 regression equations. The regression coefficients differ across equations (or hours) and vary...

A break detection testing procedure for the well-known AR(p) linear panel data model with exogenous or pre-determined regressors is developed. The proposed method can accommodate a structural break in the slope parameters as well as in the fixed effects. Breaks in the latter are not constrained...

Bayesian inference is developed and applied for an extended Nelson–Siegel term structure model capturing interest rate risk. The so-called Stochastic Volatility Nelson–Siegel (SVNS) model allows for stochastic volatility in the underlying yield factors. A Markov chain Monte Carlo (MCMC)...