Factor Structure in Commodity Futures Return and Volatility

Abstract

Using data on more than 750 million futures trades during [2004][2005][2006][2007][2008][2009][2010][2011][2012][2013], we analyze eight stylized facts of commodity price and volatility dynamics in the post financialization period.We pay particular attention to the factor structure in returns and volatility and to commodity market integration with the equity market. We find evidence of a factor structure in daily commodity futures returns. However, the factor structure in daily commodity futures volatility is even stronger than in returns. When computing model-free realized commodity betas with the stock market we find that they were high during 2008-2010 but have since returned to the pre-crisis level close to zero. The common factor in commodity volatility is nevertheless clearly related to stock market volatility. We conclude that, while commodity markets appear to again be segmented from the equity market when only returns are considered, commodity volatility indicates a nontrivial degree of market integration.

The volatility of returns from commodity futures: evidence from India

“…Using GARCH models, the study suggests that speculation affects the volatility of returns, and long-term speculation has a negative impact, whereas short term speculation has a positive effect. Christoffersen et al (2014) analyzed the stylized facts of volatility in the post-financialization period using data of 750 million futures observed between 2004 and 2013.…”

A Markov Chain Estimator of Multivariate Volatility from High Frequency Data

“…We apply the Markov chain estimator to high-frequency commodity prices, that have previously been used in Christoffersen et al (2014). We confine our empirical analysis to 2013 data and consider in our study high frequency data for 14 assets.…”

“…The 14 assets include the exchange traded fund, SPY, that tracks the S&P 500 index, and 13 commodity futures. We refer to Christoffersen et al (2014) for detailed information about the data, including the procedures used for cleaning the high frequency data for outliers and other anomalies. 11% 19% 31% 20% 32% 19% 12% 24% 27% 18% 21% 33% 25% 22% Table 4: Summary statistics for the 251 trading days in 2013.…”

A Markov Chain Estimator of Multivariate Volatility from High Frequency Data

“…Of the 15 commodities analyzed in Christoffersen et al (2014), we drop two of these series for computational reasons. Specifically, we dropped "Heating Oil" (HO) because it has an unusually large number of distinct second-to-second price increments and "Feeder Cattle" (FC) because it is substantially less liquid compared with the other commodities.…”