t he relationship between the price of oil and the level of economic activity is a fundamental empirical issue in macroeconomics. in this research, by using a multivariate garch-in-mean var, we try to investigate direct effects of uncertainty of oil price on macroeconomics of iran by using annually data from 1965 to 2013.results show that uncertainty about oil prices had a negative and signific...

The use of GARCH models to characterize crude oil price volatility is widely observed in the empirical literature. In this paper the efficiency of six univariate GARCH models and two methods of estimation the parameters for forecasting oil price volatility are examined and the best method for forecasting crude oil price volatility of Brent market is determined. All the examined models in this p...

â â â â â â â  this paper proposes a new forecasting model for investigating relationship between the price of crude oil, as an important energy source and gdp of the us, as the largest oil consumer, and the uk, as the oil producer. gmdh neural network and mlff neural network approaches, which are both non-linear models, are employed to forecast gdp responses to the oil price changes. the resul...

We use high-frequency intra-day realized volatility to evaluate the relative forecasting performance of several models for the volatility of crude oil daily spot returns. Our objective is to evaluate the predictive ability of time-invariant and Markov switching GARCH models over different horizons. Using Carasco, Hu and Ploberger (2014) test for regime switching in the mean and variance of the ...

We solve a Pareto risk-sharing problem with heterogeneous agents with recursive utility over multiple goods. We use this optimal consumption allocation to derive a pricing kernel and the price of oil and related futures contracts. This gives us insight into the dynamics of risk premia in commodity markets for oil. As an example, in a calibrated version of our model we show how rising oil prices...

this research studies the application of hybrid algorithms for predicting the prices of crude oil. brent crude oil price data and hybrid intelligent algorithm (time delay neural network, probabilistic neural network, and fuzzy logic) were used to build intelligent decision support systems for predicting crude oil prices. the proposed model was able to predict future crude oil prices from august...

The macroeconomic implications of oil price fluctuations vary according to their sources. Our estimated two-country DSGE model distinguishes between country-specific oil supply shocks, various domestic and foreign activity shocks, and oil efficiency shocks. Changes in foreign oil efficiency, modeled as factor-augmenting technology, were the key driver of fluctuations in oil prices between 1984 ...