When observed over a large panel, measures of risk (such as realized volatilities) usually exhibit a secular trend around which individual risks cluster. In this article we propose a vector Multiplicative Error Model achieving a decomposition of each risk measure into a common systematic and an...

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...

This work proposes novel network analysis techniques for multivariate time series. We define the network of a multivariate time series as a graph where vertices denote the components of the process and edges denote non zero long run partial correlations. We then introduce a two step LASSO...

This paper is concerned with the issues of modeling and projecting the dynamics of volatility when a group of potentially useful predetermined variables is available. We predict realized volatility and value at risk (VaR) with a nested set of multiplicative error models for realized volatility....

In this paper we address the issue of forecasting Value--at--Risk (VaR) using different volatility measures: realized volatility, bipower realized volatility, two-scales realized volatility, realized kernel, as well as the daily range. We propose a dynamic model with a flexible trend...

Within models for nonnegative time series, it is common to encounter deterministic components (trends, seasonalities) which can be specified in a flexible form. This work proposes the use of shrinkage type estimation for the parameters of such components. The amount of smoothing to be imposed on...

Within models for nonnegative time series, it is common to encounter deterministic components (trends, seasonalities) which can be specified in a flexible form. This work proposes the use of shrinkage type estimation for the parameters of such components. The amount of smoothing to be imposed on...

The explosion of algorithmic trading has been one of the most pro-minent recent trends in the financial industry. Algorithmic trading consists of automated trading strategies that attempt to minimize transaction costs by optimally placing orders. The key ingredient of many of these strategies...

The financial econometrics literature on Ultra High-Frequency Data (UHFD)has been growing steadily in recent years. However, it is not always straightforward to construct time series of interest from the raw data and the consequences of data handling procedures on the subsequent statistical...