Econometric estimation using simulation techniques, such as the efficient method of moments, may betime consuming. The use of ordinary matrix programming languages such as Gauss, Matlab, Ox or S-plus will very often cause extra delay. For the Efficient Method of Moments implemented to...

We propose a new estimator for the spot covariance matrix of a multi-dimensional continuous semi-martingale log asset price process which is subject to noise and non-synchronous observations. The estimator is constructed based on a local average of block-wise parametric spectral covariance...

We propose a new estimator for the spot covariance matrix of a multi-dimensional continuous semi-martingale log asset price process which is subject to noise and non-synchronous observations. The estimator is constructed based on a local average of block-wise parametric spectral covariance...

It is crucial to model, quantify and understand the variables and dynamics that underlie the well-known extreme behaviour of spot electricity prices in wholesale markets. We explicitly model the conditional volatility and skewness of electricity prices. A GARCH-type model allowing for...

We investigate a model in which we connect slowly time varying unconditional long-run volatility with short-run conditional volatility whose representation is given as a semi-strong GARCH (1,1) process with heavy tailed errors. We focus on robust estimation of both long-run and short-run...

Recent research has focused on modelling asset prices by Itocirc; semimartingales. In such a modelling framework, the quadratic variation consists of a continuous and a jump component. This paper is about inference on the jump part of the quadratic variation, which can be estimated by the...

We investigate price duration variance estimators that have long been neglected in the literature. We show i) how price duration estimators can be used for the estimation and forecasting of the integrated variance of an underlying semi-martingale price process and ii) how they are affected by a)...

We consider a nonparametric time series regression model. Our framework allows precise estimation of betas without the usual assumption of betas being piecewise constant. This property makes our framework particularly suitable to study individual stocks. We provide an inference framework for all...

We develop a volatility estimator that can be directly applied to tick-by-tick data. More specifically, we consider a model that allows for (i) irregular observation times that can be endogenous, (ii) dependent noise that can have diurnal features and be dependent on the latent price process,...

Semi-parametric estimators for non-Gaussian GARCH processes based on Feasible Weighted Least Squares (FWLS) are proposed. The estimators are consistent and do not require the specification of the innovations distribution family. The FWLS estimators incorporate information related to the skewness...