This study investigates the price volatility of metals, using the GARCH and GJR models. First we examine the persistence of volatility and the leverage effect across metal markets taking into account the presence of outliers, and second we estimate the effects of oil price shocks on the price...

This paper evaluates how different types of speculation affect the volatility of commodities' futures prices. We adopt four indexes of speculation: Working's T, the market share of non-commercial traders, the percentage of net long speculators over total open interest in future markets, which...

We study the effects of crude oil price shocks on the stock market volatility of the G7 economies. We rely on a structural VAR model to identify the causes underlying the oil price shocks and gauge the differential impact that oil supply and oil demand innovations have on financial volatility....

This study investigates the effects of oil price shocks on volatility of selected agricultural and metal commodities. To achieve this goal, we decompose an oil price shock to its underlying components, including macroeconomics and oil specific shocks. The applied methodology is the structural...

We study the impact of oil price shocks on US stock market volatility. We derive three different structural oil shock variables (i.e. aggregate demand, oil-supply, and oil-demand shocks) and relate them to stock market volatility, using bivariate structural VAR models, one for each oil price...

We estimate dynamic conditional correlations between 10 commodities futures returns in energy, metals and agriculture markets over the period 1998-2014 with a DCC-GARCH model. We look at the factors influencing those correlations, adopting a pooled mean group (PMG) estimator. Macroeconomic...

This paper analyses futures prices for four energy commodities (light sweet crude oil, heating oil, gasoline and natural gas) and five agricultural commodities (corn, oats, soybean oil, soybeans and wheat), over the period 1986-2010. Using CCC and DCC multivariate GARCH models, we find that...

We present a weekly structural Vector Autoregressive (VAR) model of the US crude oil market. Exploiting weekly data we can explain short-run crude oil price dynamics, including those related with the COVID-19 pandemic and with the Russia's invasion of Ukraine. The model is set identified with a...

This paper investigates the forecasting performance of three popular variants of the non-linear GARCH models, namely VS-GARCH, GJR-GARCH and Q-GARCH, with the symmetric GARCH(1,1) model as a benchmark. The application involves ten European stock price indexes. Forecasts produced by each...

This paper estimates the dynamic conditional correlations in the returns on WTI oil one-month forward prices, and one-, three-, six-, and twelve-month futures prices, using recently developed multivariate conditional volatility models. The dynamic correlations enable a determination of whether...