Hedging Industrial Metals With Stochastic Volatility Models

Abstract

The financialization of commodities documented in [Tang and Xiong (2012) Financial Analyst Journal, 68:54-74]has led commodity prices to exhibit not only time-varying volatility, but also price and volatility jumps. Using the class of stochastic volatility (SV) models, we incorporate such extreme price movements to generate out-of-sample hedge ratios. In-sample estimation on China's copper (CU) and aluminum (AL) spot and futures markets confirms the presence of price jumps and price-volatility jump correlations. Out-of-sample hedge ratios from the [Bates (1996) Review of Financial Studies, 9:69-107] SV with price jumps model deliver the greatest risk reduction on the unhedged positions at 59.55% for CU and 49.85% for AL. But it is the [Duffie, Pan, and Singleton (2000) Econometrica, 68:1343-1376] SV model with correlated price and volatility jumps that produces hedge ratios which yield the largest Sharpe Ratios of 0.644 for CU and 0.886 for AL.

Volatility forecasting in the Chinese commodity futures market with intraday data

“…Our data on aluminum, copper, and fuel oil consistently show that contracts with three months to delivery enjoy the best liquidity. We are not the first to note this pattern (see Liu et al 2014;Peck 2008), but we are the first to offer solid and detailed evidence. Using 5-min returns data over long sample periods, we compute three popular liquidity measures that capture different aspects of liquidity, namely the effective spread of Roll (1984), the proportion of zero returns of Lesmond et al (1999), and the Amihud (2002) illiquidity measure (Goyenko et al 2009).…”

“…6 The starting and ending dates of the four commodities are constrained by data availability. 7 Chortareas et al (2011) and Liu et al (2014) adopt similar sample period for the out-of-sample forecasting exercise with foreign exchange and commodity futures data, respectively.…”

“…The Chinese sugar futures contracts rank 3rd globally in terms of trading volume in the Agricultural Category, while copper ranks 4th in the Metals Category. 2 Our paper is related to Liu et al (2014) which examine hedging with metal futures in China using commodity futures contracts, and to Fung et al (2003) which adopt the bivariate GARCH framework to analyze the information flow between commodity futures traded both in the US and China. 3 See the document entitled White Sugar Futures (April 2009) on the Zhengzhou Commodity Exchange website http://www.czce.com.cn.…”

A Methodology to Forecast Commodity Prices in Vietnam

“…The SVCJ model of Duffie, Pan, and Singleton (2000) assumes that both the price and volatility jump simultaneously, which is motivated by the fact that price jumps are often accompanied volatility jumps. Liu, Chng and Xu (2014) analyse hedge ratios of commodity prices generated by these three models. Benth (2011) applies the Heston model as well as the Barndorff-Nielsen and Shephard model to commodity prices.…”

“…Benth (2011) applies the Heston model as well as the Barndorff-Nielsen and Shephard model to commodity prices. Brooks and Prokopczuk (2013) as well as Liu, Chng and Xu (2014) use the Markov Chain Monte Carlo (MCMC) to estimate the parameters of these models. Solibakke (2014) uses a multifactor stochastic volatility model for the carbon prices, also applying MCMC for parameter estimation and inference.…”