First Version: 03/11/2015This Version: 04/01/2016We expand the literature of volatility and Value-at-Risk forecasting of oil price returns by comparing the recently proposed Mixture Memory GARCH (MMGARCH) model to other discrete volatility models (GARCH, FIGARCH, and HYGARCH). We incorporate an...

We augment the HAR model with additional information channels to forecast realized volatility of WTI futures prices. These channels include stock markets, sentiment indices, commodity and FX markets, and text-based Google indices. We then apply four differing machine learning techniques to...

This paper contributes to the large debate regarding the impact of oil price changes on U.S. GDP growth. Firstly, it replicates empirical findings of prominent studies and finds that the proposed oil price measures have a dissipating effect with recent data up to 2016Q4. Secondly, it re-examines...

This paper presents a thorough replication of Hamilton (2003) which in turn replicates and extends the findings of four seminal papers regarding the oil price-GDP growth relationship. Firstly, we replicate the empirical results obtained with the oil price measures of Hamilton (1983), Mork...

This paper investigates the dynamics of the co-movement of GCC stock market returns with global oil market uncertainty, using an ARMA-DCC-EGARCH and time varying Student-t copula models. Empirical results demonstrate that oil uncertainty has significant and time varying impacts on the GCC stock...