The multivariate analysis of a panel of economic and financial time series with mixed frequencies is a challenging problem. The standard solution is to analyze the mix of monthly and quarterly time series jointly by means of a multivariate dynamic model with a monthly time index: artificial...

We demonstrate that the parameters controlling skewness and kurtosis in popular equity return models estimated at daily frequency can be obtained almost as precisely as if volatility is observable by simply incorporating the strong information content of realized volatility measures extracted...

In this paper we investigate whether accounting for non-pervasive shocks improves the forecast of a factor model. We compare four models on a large panel of US quarterly data: factor models, factor models estimated on selected variables, Bayesian shrinkage, and factor models together with...

The use of Markov processes (or Markov chains) has become widespread in dynamic stochastic modeling. For example, its use is ubiquitous in macroeconomics (dynamic stochastic general equilibrium), finance (dynamic asset pricing), and areas of microeconomics (dynamic programming). As we discuss...

Recent literature has focuses on realized volatility models to predict financial risk. This paper studies the benefit of explicitly modeling jumps in this class of models for value at risk (VaR) prediction. Several popular realized volatility models are compared in terms of their VaR forecasting...

The growing availability of financial and macroeconomic data sets including a large number of time series (hence the high dimensionality) calls for econometric methods providing a convenient and parsimonious representation of the covariance structure both in the time and the cross-sectional...

Rogers-Satchell (RS) measure is an efficient volatility measure. This paper proposes quantile RS (QRS) measure to ensure robustness and correct the downward bias of RS measure with an additive term. Moreover scaling factors are provided for different interquantile ranges to ensure unbiasedness....

Surveys of Professional Forecasters produce precise and timely point forecasts for key macroeconomic variables. However, the accompanying density forecasts are not as widely utilized, and there is no consensus about their quality. This is partly because such surveys are often conducted for...

Machine learning methods are becoming increasingly popular in economics, due to the increased availability of large datasets. In this paper I evaluate a recently proposed algorithm called Generalized Approximate Message Passing (GAMP), which has been popular in signal processing and compressive...

Forecasting volatility models typically rely on either daily or high frequency (HF) data and the choice between these two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer from many limitations. HF data feature microstructure problem,...