Commodity is one of the most volatile markets and forecasting its volatility is an issue of paramount importance. We … study the dynamics of the commodity markets volatility by employing fractional stochastic volatility and heterogeneous … autoregressive (HAR) models. Based on a high-frequency futures price dataset of 22 commodities, we confirm that the volatility of …

This paper extends the empirical literature on volatility risk premium (VRP) and future returns by analyzing the …

relevant driver of returns, especially during periods of high speculative demand volatility. These findings confirm significant …

using volatilities from strategically linked commodities; thus, no volatility spillovers are detected. Interestingly, when …

We analyse the dynamic behavior of conditional volatility in commodity markets using a novel, manually collected … dataset of daily price ranges over a time span of more than 140 years, which allows more precise daily volatility estimates … not adequate to capture the very distinct long-run and short-run dynamic volatility components. While the long memory …

metal price series is investigated, as well as time-varying volatility. The results demonstrate that allowing for jumps and … time-varying volatility provides statistically important improvements in the modelling or prices, relative to GBM. These …

vector autoregressions with Markov switching in heteroskedasticity. We decompose changes in futures price volatility into … and increases the hedging costs of producers and processors of oil when volatility is high. …

relevant driver of returns, especially during periods of high speculative demand volatility. These findings confirm significant …