Classical estimation techniques for linear models either are inconsistent, or perform somewhat poorly under stable error densities; most of them are not even rate-optimal. In this paper, we develop an original R-estimation method and investigate its asymptotic performances under stable...

We study the impact of the arrival of macroeconomic news on the informational and noise-driven components in high-frequency quote processes and their conditional variances. We decompose bid and ask returns into a common ("efficient return") factor and two market-side-specific components...

We propose a quantile--based method to estimate the parameters (i.e. locations, dispersions, co--dispersions and the tail index) of an elliptical distribution, and a battery of tests for model adequacy. The method is suitable for vast dimensions since the estimators for the location vector and...

We establish the asymptotic normality of marginal sample quantiles for S-mixing vector stationary processes. S-mixing is a recently introduced and widely applicable notion of dependence. Results of some Monte Carlo simulations are given

We introduce an approximate dynamic factor model for modeling and forecasting large panels of realized volatilities. Since the model is estimated by means of principal components and low dimensional maximum likelihood, it does not suffer from the curse of dimensionality. We apply the model to a...

To measure the systemic risk in financial markets, and rank systemically important financial institutions (SIFIs), we propose a methodology based on the Google PageRank algorithm. We understand the economic system as interconnected risk shocks of firms in both the financial sector and the real...

Realized volatilities observed across several assets show a common secular trend and some idiosyncratic pattern which we accommodate by extending the class of Multiplicative Error Models (MEMs). In our model, the common trend is estimated nonparametrically, while the idiosyncratic dynamics are...