Volatility spillover effects and cross hedging in corn and crude oil futures

Using a volatility spillover model, we find evidence of significant spillovers from crude oil prices to corn cash and futures prices, and that these spillover effects are time‐varying. Results reveal that corn markets have become much more connected to crude oil markets after the introduction of the Energy Policy Act of 2005. Furthermore, when the ethanol–gasoline consumption ratio exceeds a critical level, crude oil prices transmit positive volatility spillovers into corn prices and movements in corn prices are more energy‐driven. Based on this strong volatility link between crude oil and corn prices, a new cross‐hedging strategy for managing corn price risk using oil futures is examined and its performance is studied. Results show that this cross‐hedging strategy provides only slightly better hedging performance compared with traditional hedging in corn futures markets alone. The implication is that hedging corn price risk in corn futures markets alone can still provide relatively satisfactory performance in the biofuel era. © 2010 Wiley Periodicals, Inc. Jrl Fut Mark     

A maximal affine stochastic volatility model of oil prices

This study develops and estimates a stochastic volatility model of commodity prices that nests many of the previous models in the literature. The model is an affine three‐factor model with one state variable driving the volatility and is maximal among all such models that are also identifiable. The model leads to quasi‐analytical formulas for futures and options prices. It allows for time‐varying correlation structures between the spot price and convenience yield, the spot price and its volatility, and the volatility and convenience yield. It allows for expected mean‐reversion in the short term and for an increasing expected long‐term price, and for time‐varying risk premia. Furthermore, the model allows for the situation in which options' prices depend on risk not fully spanned by futures prices. These properties are desirable and empirically important for modeling many commodities, especially crude oil. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:101–133, 2010     

Forecasting oil price movements: Exploiting the information in the futures market

Relying on the cost of carry model, the long‐run relationship between spot and futures prices is investigated and the information implied in these cointegrating relationships is used to forecast out of sample oil spot and futures price movements. To forecast oil price movements, a vector error correction model (VECM) is employed, where the deviations from the long‐run relationships between spot and futures prices constitute the equilibrium error. To evaluate forecasting performance, the random walk model (RWM) is used as a benchmark. It was found that (a) in‐sample, the information in the futures market can explain a sizable portion of oil price movements; and (b) out‐of‐sample, the VECM outperforms the RWM in forecasting price movements of 1‐month futures contracts. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:34–56, 2008     

Causality in the VIX futures market

This study examines the price‐discovery function and information efficiency of a fast growing volatility futures market: the Chicago Board of Option Exchange VIX futures market. A linear Engle–Granger cointegration test with an error correction mechanism (ECM) shows that during the full sample period, VIX futures prices lead spot VIX index, which implies that the VIX futures market has some price‐discovery function. But a modified Baek and Brock nonlinear Granger test detects bi‐directional causality between VIX and VIX futures prices, suggesting that both spot and futures prices react simultaneously to new information. Quarter‐by‐quarter investigations show that, on average, the estimated parameters are not significantly different from zero, thus providing further evidence supporting information efficiency in the VIX futures market. © 2011 Wiley Periodicals, Inc. Jrl Fut Mark     

The new market for volatility trading

This study analyses the new market for trading volatility; VIX futures. We first use market data to establish the relationship between VIX futures prices and the index itself. We observe that VIX futures and VIX are highly correlated; the term structure of average VIX futures prices is upward sloping, whereas the term structure of VIX futures volatility is downward sloping. To establish a theoretical relationship between VIX futures and VIX, we model the instantaneous variance using a simple square root mean‐reverting process with a stochastic long‐term mean level. Using daily calibrated long‐term mean and VIX, the model gives good predictions of VIX futures prices under normal market situation. These parameter estimates could be used to price VIX options. © 2010 Wiley Periodicals, Inc. Jrl Fut Mark 30:809–833, 2010     

Volatility and commodity price dynamics

Commodity prices are volatile, and volatility itself varies over time. Changes in volatility can affect market variables by directly affecting the marginal value of storage, and by affecting a component of the total marginal cost of production, the opportunity cost of producing the commodity now rather than waiting for more price information. I examine the role of volatility in short‐run commodity market dynamics and the determinants of volatility itself. I develop a structural model of inventories, spot, and futures prices that explicitly accounts for volatility, and estimate it using daily and weekly data for the petroleum complex: crude oil, heating oil, and gasoline. © 2004 Wiley Periodicals, Inc. Jrl Fut Mark 24:1029–1047, 2004     

Cointegration,unbiased expectations,and forecasting in the BIFFEX freight futures market

The relationship between freight cash and futures prices is investigated using cointegration econometrics. Results illustrate that the BIFFEX futures market is unbiased, and hence efficient for the current, one, two, and quarterly contract horizons. Since the futures contract is based on an index of various shipping routes, which has undergone several changes since its inception, stability in the relationship between the spot and futures rates is investigated using rolling cointegration techniques. Results indicate that the futures contract appears to have become more efficient over time in predicting the spot rate, and that the decrease in trading volume found in the BIFFEX market is not driven by a lack of efficiency in this market. Rather, the decrease in futures trading might be attributed to the growth rate of the freight forward market. This article incorporates the long‐run cointegrating relationships between cash and futures prices in a forecasting model and compares the forecasting performance of this model with several alternatives. It is found that while the futures price is the best predictor of future spot rates for the current‐month contract, time‐series models can outperform the futures contract at longer contract horizons. © 2000 John Wiley & Sons, Inc. Jrl Fut Mark 20:545–571, 2000.     

Do futures prices help forecast the spot price?

This study proposes a futures‐based unobserved components model for commodity spot prices. Prices quoted at the same time incorporate the same information, but are affected differently, resulting in the different shapes of futures curves. This model utilizes information from part of the futures curve to improve forecasting accuracy of the spot price. Applying this model to oil market data, I find that the model forecasts outperform the literature benchmark (the no‐change forecast) and futures prices forecasts in multiple dimensions, with smaller average error variation over the sample period and higher chance of smaller absolute error in each period.     

A cointegrated commodity pricing model

We propose a commodity pricing model that extends the Gibson–Schwartz two‐factor model to incorporate the effect of linear relations among commodity spot prices, and provide a condition under which such linear relations represent cointegration. We derive futures and call option prices for the proposed model, and indicate that, unlike in Duan and Pliska (2004), the linear relations among commodity prices should affect commodity derivative prices, even when the volatilities of commodity returns are constant. Using crude oil and heating oil market data, we estimate the model and apply the results to the hedging of long‐term futures using short‐term ones.     

A STRUCTURAL RISK‐NEUTRAL MODEL FOR PRICING AND HEDGING POWER DERIVATIVES

We develop a structural risk‐neutral model for energy market modifying along several directions the approach introduced in Aïd et al. In particular, a scarcity function is introduced to allow important deviations of the spot price from the marginal fuel price, producing price spikes. We focus on pricing and hedging electricity derivatives. The hedging instruments are forward contracts on fuels and electricity. The presence of production capacities and electricity demand makes such a market incomplete. We follow a local risk minimization approach to price and hedge energy derivatives. Despite the richness of information included in the spot model, we obtain closed‐form formulae for futures prices and semiexplicit formulae for spread options and European options on electricity forward contracts. An analysis of the electricity price risk premium is provided showing the contribution of demand and capacity to the futures prices. We show that when far from delivery, electricity futures behave like a basket of futures on fuels.     

Risk premiums on inventory assets: the case of crude oil and natural gas

This study tested for the presence of risk premiums on crude oil and natural gas. The econometric analysis followed from a stochastic model in which the equilibrium value of inventories depends on a convenience yield and an option value related to price uncertainty. The empirical findings provide rather strong support for the presence of risk premiums and also evidence for the existence of convenience yields. The risk premiums rose sharply with greater price volatility and help to explain why prices for immediate sales often exceed prices for future delivery. © 2001 John Wiley & Sons, Inc. Jrl Fut Mark 21:109–126, 2001     

Investor Sentiment and Return Predictability in Agricultural Futures Markets

This study examines the usefulness of trader‐position‐based sentiment index for forecasting future prices in six major agricultural futures markets. It has been found that large speculator sentiment forecasts price continuations. In contrast, large hedger sentiment predicts price reversals. Small trader sentiment hardly forecasts future market movements. An investigation was performed into various sentiment‐based timing strategies, and it was found that the combination of extreme large trader sentiments provides the strongest timing signal. These results are generally consistent with the hedging‐pressure theory, suggesting that hedgers pay risk premiums to transfer nonmarketable risks in futures markets. Moreover, it does not appear that large speculators in the futures markets possess any superior forecasting ability. © 2001 John Wiley & Sons, Inc. Jrl Fut Mark 21:929–952, 2001     

Drift matters: An analysis of commodity derivatives

This article presents a reduced‐form, two‐factor model to price commodity derivatives, which generalizes the model by Schwartz and Smith (2000). The model allows for two mean‐reverting stochastic factors and therefore implies that spot and futures prices can be stationary. An empirical study for the crude oil market tests the new model. Out‐of‐sample pricing and hedging results for futures and forwards show that the new model dominates the nonstationary model by Schwartz and Smith in the following sense: It works equally well for short‐term contracts but leads to major improvements for long‐term contracts. This finding is particularly relevant for typical applications like the valuation of commodity‐linked real assets with long maturities. © 2005 Wiley Periodicals, Inc. Jrl Fut Mark 25:211–241, 2005     

The impact of crude oil inventory announcements on prices: Evidence from derivatives markets

This study examines the impact of weekly crude oil storage announcements on oil futures and options prices. We document evidence of a strong announcement day effect on both markets, and find prices to move in anticipation of the inventory surprise. Futures returns significantly decrease with positive surprises and increase with negative surprises. There is no evidence of an asymmetric impact on futures prices. Near‐the‐money options exhibit the greatest price sensitivity, and the magnitude of the price response of both futures and options declines with maturity. The results remain robust even after controlling for various macroeconomic and other storage‐related news variables.     

An N‐factor Gaussian model of oil futures prices

This article studies the ability of an N‐factor Gaussian model to explain the stochastic behavior of oil futures prices when estimated with the use of all available price information, as opposed to traditional approaches of aggregating data for a set of maturities. A Kalman filter estimation procedure that allows for a time‐dependent number of daily observations is used to calibrate the model. When applied to all daily oil futures price transactions from 1992 to 2001, the model performs very well, requiring at least three factors to explain the term structure of futures prices, but four factors to fit the volatility term structure. The model also performs very well for daily copper futures transactions from 1992 to 2001 and for out‐of‐sample daily oil futures transactions from 2002 to 2004. © 2006 Wiley Periodicals, Inc. Jrl Fut Mark 26:243–268, 2006     

Rational expectations and market efficiency in the U.S. live cattle futures market: The role of proprietary information

The role of proprietary information in forecasting and market efficiency in the U.S. live cattle futures market is investigated. Using a unique proprietary data source collected by a private firm, we test whether the initial estimates in the USDA Cattle on Feed Report and the Knight‐Ridder pre‐release forecasts are unbiased and efficient forecasts of final revised USDA Cattle on Feed Report numbers. We then use these results to test whether futures price movements are predictable based on information in the proprietary data. We also test whether the initial estimates from the Cattle on Feed Report have new information that moves prices once the information contained in the proprietary data source has been taken into account. Results suggest that the information contained in the proprietary data source does have statistically significant explanatory power for forecasting final revised Cattle on Feed Report numbers and for predicting short‐term price movements of futures contracts. The results are inconsistent with strong‐form market efficiency in the live cattle futures market. We also find that the initial estimates in the Cattle on Feed Report still have new information that moves prices even after accounting for the unique information in both the Knight‐Ridder pre‐release forecasts and the proprietary data. © 2004 Wiley Periodicals, Inc. Jrl Fut Mark 24:429–451, 2004     

Modes of fluctuation in metal futures prices

This article examines the stochastic structure of metal futures prices. First, this article presents a stationary multi‐factor model of fluctuations in the futures price curve. Next, the model is extended to allow for time variation in the factors or “modes” of fluctuation. The model is estimated using futures price data for three very different metals: copper, which is an industrial metal; gold, which is a precious metal; and silver, which is in transition from a precious metal to an industrial metal. The estimation results show that the shapes and importance of the various modes of fluctuation for gold and silver are much different from those for copper. Gold and silver futures price curves can be adequately modeled as a time‐varying one‐factor model. Copper, however, has a more complicated structure and should be modeled as a time‐varying two‐ or three‐factor model. © 2000 John Wiley & Sons, Inc. Jrl Fut Mark 20:219–242, 2000     

Risk premiums and predictive ability of BAX futures

This study provides an in‐depth analysis of risk premiums in the Canadian Bankers' Acceptances futures (BAX) market. The predictive regressions for excess and holding‐period returns on BAX futures lend empirical support to the presence of time‐varying risk premiums especially at longer horizons. Despite the evidence of time variation in the risk premium, however, the unbiasedness of the basis as a predictor of spot returns in forecast efficiency regressions cannot be rejected. The out‐of‐sample forecasts of spot returns demonstrate the excellent predictive ability of models that exploit the restrictions implied by the unbiasedness hypothesis. Overall, our findings support the presence of a slowly moving risk premium and entail important practical implications for measuring monetary policy expectations and portfolio allocation. © 2010 Wiley Periodicals, Inc. Jrl Fut Mark     

最低收购价政策改革背景下小麦期货市场价格发现功能再检验

通过构建最低收购价政策影响下小麦期现货市场的价格传导机制的理论框架,并选取2015年我国小麦最低收购价政策改革前后两个时期各4年的周度数据,使用ADF单位根检验、Johansen协整检验、Granger因果关系检验和方差分解对最低收购价政策改革背景下小麦期货市场的价格发现功能进行实证研究。研究结果表明:无论是强麦还是普麦,最低收购价政策改革对于小麦期货价格与现货价格均衡关系的形成均具有促进作用;在最低收购价政策改革之前,强麦期货市场不具有价格发现功能,之后这种功能才得以形成,同时普麦期货市场的价格发现功能变得更为显著;小麦期货市场的影响力强于现货市场,在价格发现功能中占据主导作用。     

Price discovery in the foreign exchange futures market

Examination is made of the relative contributions to price discovery of the floor and electronically traded euro FX and Japanese yen futures markets and the corresponding retail on‐line foreign exchange spot markets. GLOBEX electronic futures contracts provide the most price discovery in the euro; the on‐line trading spot market provides the most in the Japanese yen. The floor‐traded futures markets contribute the least to price discovery in both the euro and the Japanese yen markets. The overall results show that electronic trading platforms facilitate price discovery more efficiently than floor trading. Futures traders may also extract information from on‐line spot prices. © 2006 Wiley Periodicals, Inc. Jrl Fut Mark 26:1131–1143, 2006