It is a well accepted fact that stock returns data are often contaminated by market microstructure effects, such as bid-ask spreads, liquidity ratios, turnover, and asymmetric information. This is particularly relevant when dealing with high frequency data, which are often used to compute model...

This paper proposes a new approach for detecting the number of structural breaks in a time series when estimation of the breaks is performed one at the time. We consider the case of shifts in the mean of a possibly nonlinear process, allowing for dependent and heterogeneous observations. This is...

In real time forecasting, the sample is usually split into an estimation period of R observations and a prediction period of P observations, where T=R+P. Parameters are often estimated in a recursive manner, initially using R observations, then R+1 observations and so on until T-1 observations...

We employ a battery of model evaluation tests for a broad-set of GARCH-MIDAS models and account for data snooping bias. We document that inferences based on standard tests for GM variance components can be misleading. Our data mining free results show that the gains of macro-variables in...

In this paper we use the DCC-MIDAS (Dynamic Conditional Correlation-Mixed Data Sampling) model to infer the association between oil and equities in five MENA countries between February 2006 and April 2017. The model indicates that higher oil returns tends to reduce the long-term risk of the...

This article exploits a new spillover directional measure proposed by Diebold and Yilmaz (2009, 2012) to investigate the dynamic spillover of return and volatility between oil and equities in the Gulf Cooperation Council Countries during the period 2004 to 2012. Our results indicate that return...