Food versus fuel: What do prices tell us?

Sorting out the impacts of biofuels on global agricultural commodity prices is impossible without turning to data and distinguishing between the short-run versus the long-run impacts. Using time-series prices on fuels and agricultural commodities, the aim is to investigate the long-run cointegration of these prices simultaneously with their multivariate short-run interactions. Results indicate no direct long-run price relations between fuel and agricultural commodity prices, and limited if any direct short-run relationships. In terms of short-run price movements, sugar prices are influencing all the other agricultural commodity prices except rice. With sugar the number one world input for ethanol, results indicate increased ethanol production is potentially influencing short-run agricultural commodity prices. Overall, results support the effect of agricultural commodity prices as market signals which restore commodity markets to their equilibria after a demand or supply event (shock).     

Production and spatial distribution of switchgrass and miscanthus in the United States under uncertainty and sunk cost

The U.S. cellulosic biofuel mandate has not been enforced in recent years. Uncertainty surrounding the enforcement of the mandate in addition to high production and harvest cost have contributed to a delay in the widespread planting of bioenergy crops such as switchgrass and miscanthus. Previous literature has shown that under uncertainty and sunk cost, an investment threshold is further increased due to the value associated from holding the investment option. In this paper, we extend the previous literature by applying a real option switching model to bioenergy crop production. First, we calculate the county-level break-even price which triggers a switching away from traditional field crops (corn, soybeans, and wheat) to bioenergy crops under various scenarios differing by commodity prices, production cost and biomass price expectations. We show that the resulting break-even prices at the county-level can be substantially higher than previously estimated due to the inclusion of the option value. In a second step, we identify counties that are most likely to grow switchgrass or miscanthus by simulating a stochastic biomass price over time. Our results highlight two issues: First, switchgrass or miscanthus are not grown in the Midwest under any scenario. Under low agricultural residue removal rates, biomass crops are mostly grown in the Southeast. Second, under the assumption of a high removal rates, bioenergy crops are not grown anywhere in the U.S. since the cellulosic biofuel mandate can be covered by agricultural residues.     

Electricity price modeling with stochastic time change

In this paper, we develop a novel approach to electricity price modeling, based on the powerful technique of stochastic time change. This technique allows us to incorporate the characteristic features of electricity prices (such as seasonal volatility, time varying mean reversion and seasonally occurring price spikes) into the model in an elegant and economically justifiable way. The stochastic time change introduces stochastic as well as deterministic (e.g., seasonal) features in the price process' volatility and in the jump component.We specify the base process as a mean reverting jump diffusion and the time change as an absolutely continuous stochastic process with seasonal component. The activity rate of the stochastic time change can be related to the factors that influence supply and demand. Here we use the temperature as a proxy for the demand and hence, as the driving factor of the stochastic time change, and show that this choice leads to realistic price paths. We derive properties of the resulting price process and develop the model calibration procedure. We calibrate the model to the historical EEX power prices and apply it to generating realistic price paths by Monte Carlo simulations. We show that the simulated price process matches the distributional characteristics of the observed electricity prices in periods of both high and low demand.     

Real option valuation of free destination in long-term liquefied natural gas supplies

This paper presents a real option model for the valuation of destination flexibility in long-term LNG supplies. Stochastic price dynamics in the different markets is modelled through geometric Brownian motion processes. Mean reversion is considered as well as correlation between markets, but instead of the usual correlation in return shocks, a price convergence term is introduced representing the arbitrage streams between markets. Model parameters are estimated from market data on LNG prices by maximum log-likelihood. The goodness of the fit for the proposed model is tested as well as for two alternative models. Confidence intervals for the parameters are given. Results for the model are calculated by Monte Carlo simulation. Frequency distributions for the main results are plotted. The effect of the main parameters of the model is studied (i.e. price volatilities, price convergence, initial prices in the markets, mean reversion, extra transportation costs, number of alternative markets). The value of destination flexibility is found to be an important share of the value of LNG.     

The stochastic seasonal behavior of energy commodity convenience yields

This paper contributes to the commodity pricing literature by consistently modeling the convenience yield with its empirically observed properties. Specifically, in this paper, we show how a four-factor model for the stochastic behavior of commodity prices, with two long- and short-term factors and two additional seasonal factors, may accommodate some of the most important empirically observed characteristics of commodity convenience yields, such as the mean reversion and stochastic seasonality. Based on this evidence, a theoretical model is presented and estimated to characterize the commodity convenience yield dynamics that are consistent with previous findings. We also show that commodity price seasonality is better estimated through convenience yields than through futures prices.     

Biofuel-related price transmission using Renewable Identification Number prices to signal mandate regime

This paper improves on existing methods of testing price links between biofuels, petroleum, petroleum products, and agricultural commodity feedstocks by incorporating information contained in the prices of Renewable Identification Numbers (RINs) that are used for biofuel mandate compliance. Theoretical equations are developed to show the role RIN prices can play in the relationships among energy, biofuel, and agricultural commodity prices. RIN price data are used (a) to infer values of fuels where market data are unavailable and (b) to define binding and non-binding mandate regimes. A specific empirical exercise exploits the RIN price data to conduct cointegration tests on high frequency data representing spot and futures prices relating to ethanol and biodiesel from February 2009 through March 2015, finding little evidence of short-term biofuel and petroleum product substitution during this period.     

Blending under uncertainty: Real options analysis of ethanol plants and biofuels mandates

The value of a representative ethanol producer, that benefits from both low and high gasoline prices in the short-run, is modeled. Ethanol producers make a modest competitive profit in the mandate-induced region of production. A low price of gasoline increases the demand for blend ethanol and consequently increases the profit of ethanol producers. On the other hand, when gasoline becomes costlier than ethanol, the capacity constraints of the biofuels sector bind and ethanol producers gain large quasi-monopoly margins. This is an interesting example of a market where two commodities are complement up to a point and then substitute after that. We postulate the value of an ethanol producer as a strangle option consisting of two real options: the option to substitute gasoline at times of expensive crude oil and the option to expand supply of blend at times of cheap gasoline. Using a dynamic model we show that the higher volatilities of crude oil and ethanol costs increase biofuels firms' value. We also find non-monotonic relationships between the value of an ethanol plant and several underlying variables, including gasoline price level. We estimate the value provided by a 10% blend mandate to be around $150,000,000 for a representative ethanol unit. Our results offer a novel view of oil and feedstock price risks in contrast to the common belief that considers those risks as a negative factor for the biofuels sector.     

The crucial relationship among energy commodity prices: Evidence from the Spanish electricity market

The main purpose of this article is twofold to analyze: (a) the long-term relation among the commodities prices and between spot electricity market price and commodity prices, and (b) the short-term dynamics among commodity prices and between electricity prices and commodity prices. Data between 2002 and 2005 from the Spanish electricity market was used. Econometric methods were used in the analysis of the commodity spot price, namely the vector autoregression model, the vector error correction model and the granger causality test. The co-integration approach was used to analyze the long-term relationship between the common stochastic trends of four fossil fuel prices. One of the findings in the long-term relation is that the prices of fuel and the prices of Brent are intertwined, though the prices of Brent ten to “move” to reestablish the price equilibrium. Another finding is that the price of electricity is explained by the evolution of the natural gas series.     

Are biofuels a feasible option?

Recently a number of objections have been raised against the use of ethanol produced from agricultural products such as maize, sugarcane, wheat or sugar beets as a replacement for gasoline, despite some of their advantages such as being cleaner and to some extent renewable. We address these objections in this paper. Topics discussed include the “corn connection” (which was theorized to be a cause of deforestation in the Amazonia), the rise of food prices due to ethanol production and the real possibilities of ethanol in reducing greenhouse gas emissions. It has been shown that such concerns are grossly exaggerated and that ethanol from sugarcane, as produced in Brazil, is the preferred option for the production of fuel not only in terms of cost but also as a favourable energy balance. Finally, the possibility of expanding ethanol production to other sugar-producing countries is also discussed.     

Impact of biofuel production and other supply and demand factors on food price increases in 2008

The prices of some grain commodities more than doubled from March 2007 to March 2008. Increased food prices coincided with increasing global biofuel production, leading to speculation that biofuel production was responsible for the increased food prices. However, over the six-month period after March 2008, grain prices declined by 50% while biofuel production continued to increase. It is not possible to reconcile claims that biofuel production was the major factor driving food price increases in 2007-2008 with the decrease in food prices and increase in biofuel production since mid-2008. The available data suggests that record grain prices in 2008 were not caused by increased biofuel production, but were actually the result of a speculative bubble related to high petroleum prices, a weak US dollar, and increased volatility due to commodity index fund investments.Many factors converged in 2007-2008 to increase food and related commodity prices including increased demand, decreased supply, and increased production costs driven by higher energy and fertilizer costs. Disentangling these factors and providing a precise quantification of their contributions is a difficult, perhaps impossible, task. In 2008, several reports were published by governmental and international agencies that speculated on the cause of increased food prices worldwide. Taken together, the available analyses suggest that biofuel production had a modest (3-30%) contribution to the increase in commodity food prices observed up to mid-2008. The development of second-generation biofuels (e.g., cellulosic ethanol) which use non-food residual biomass or non-food crops should mitigate any future impact of biofuel production on food prices.     

Impact of US biofuel policy on US corn and gasoline price variability

Despite a large number of studies that examine the influence of biofuels and biofuel policy on commodity prices, the impact of biofuel policy on commodity price variability is poorly understood. A good understanding of biofuel policy’s impact on price variability is important for mitigating food insecurity and assisting policy formation. We examine how U.S. ethanol policies such as the Renewable Fuel Standard (RFS) mandates and the blend wall affect the price variability of corn and gasoline. We first present an analytical and graphical framework to identify the effect and then use stochastic partial equilibrium simulation to measure the magnitude of the impacts. We show that RFS mandates and the blend wall both reduce the price elasticity of demand for corn and gasoline and therefore increase the price variability when supply shocks occur to the markets. This has important implications for policy actions with respect to maintaining or changing the current RFS mandates and/or blend wall in the US.     

Mild explosivity in recent crude oil prices

This paper provides an analysis of oil prices during and in the aftermath of the Global Financial Crisis, concentrating on the 2007–08 price spike and the 2014–16 price decline. The mildly explosive/multiple bubbles testing strategy by Phillips, Shi and Yu (2015, International Economic Review 56(4), 1043–1133) is used to test for price departures from an underlying stochastic trend and to assess whether any such departures can be explained by fundamentals or other proxy variables. The test dates two significant time periods in both Brent and WTI nominal and real front-month futures prices: a mildly explosive episode during the 2007–08 spike, prior to the peak of the Global Financial Crisis; and a significantly shorter, negative such episode during the 2014–16 price decline, whose commencement is dated around a key OPEC meeting in November 2014. Evidence using other commodity prices points to explanatory factors beyond commodity markets. A global economic activity proxy is found to be decisive in the episode in mid-2008; excess speculation is not. U.S. shale oil production, though contributing to the post-June 2014 price decline, is not seen to have been decisive. Against some recent work tying the CBOE Volatility Index (VIX) to oil futures prices, we find no evidence that the VIX decisively affected oil price levels during the sample period. The results are compared and contrasted with those obtained by Baumeister and Kilian (2016, Journal of the Association of Environmental and Resource Economists 3, 131-158) via a forecasting approach based on a structural vector autoregressive model without financial variables. Taken altogether, the results herein provide new evidence based on formal statistical testing that helps resolve a number of recent controversies in the oil price literature.     

Evaluation of new and renewable energy technologies in Korea using real options

For several years recently, the price of oil has fluctuated due to the weak US dollar and financial risks. In particular, the WTI crude oil price reached $147 per barrel in July of 2008, which is the highest price thus far. An awareness of an impending crisis and concern over climate change are driving an increase in R&D for alternative energy sources instead of fossil‐based energy. However, the researches based on traditional method show negative options about the economic value of new and renewable energy. This paper evaluates the value of new and renewable energy through a real option method which considers the uncertainty associated with fossil energy and the uncertainty of the success of R&D. The evaluating model assumes that the fossil energy price follows a geometric mean reverting process and that the probability of success with R&D on renewal forms of energy follows a binomial probability model. The model considers four options: the option to continue R&D, the option to delay R&D, the option to deploy R&D, and the option to abandon R&D. Finally, the value of Korean R&D on renewal forms of energy is analyzed by the model. Copyright © 2012 John Wiley & Sons, Ltd.     

A method for the correction of the feed-in tariff price for cogeneration based on a comparison between Croatia and EU Member States

The European Commission has adopted Directive 2004/8/EC on the promotion of cogeneration, which the EU Member States, as well as candidates including Croatia, were obliged to accept. Among other terms and conditions, the Directive requires certain support mechanisms, such as feed-in tariff prices and premiums added to market electricity prices. In this paper, the cost effectiveness of selling electricity at the feed-in tariff prices in the selected EU Member States is compared to selling it on the European electricity market, with or without premiums. The results of this comparison will indicate whether correction of the Croatian feed-in tariff price to a higher value would be justified. The cost effectiveness ratio of a cogeneration unit upgraded with mean reverting and jump diffusion processes is used for comparison. At the end of this paper, a method is suggested for the correction of feed-in tariff prices, with examples of corrected prices for the years 2008 and 2009. Such corrections have been proven to be justified and are compared to the feed-in tariff prices in most of the selected EU Member States.     

The economic feasibility of sugar beet biofuel production in central North Dakota

This study examines the financial feasibility of producing ethanol biofuel from sugar beets in central North Dakota. Under the Energy Independence and Security Act (EISA) of 2007, biofuel from sugar beets uniquely qualifies as an “advanced biofuel”. EISA mandates production of 21 billion gallons of advanced biofuels annually by 2022. A stochastic simulation financial model was calibrated with irrigated sugar beet data from central North Dakota to determine economic feasibility and risks of production for 0.038 hm³y⁻¹ (or 10 MGY (Million Gallon per Year) and 0.076 hm³y⁻¹ (or 20 MGY) ethanol plants. Study results indicate that feedstock costs, which include sugar beets and beet molasses, account for more than 70 percent of total production expenses. The estimated breakeven ethanol price for the 0.076 hm³y⁻¹ plant is $400 m⁻³ ($1.52 per gallon) and $450 m⁻³ ($1.71 per gallon) for the 0.038 hm³y⁻¹ plant. Breakeven prices for feedstocks are also estimated and show that the 0.076 hm³y⁻¹ plant can tolerate greater ethanol and feedstock price risks than the 0.038 hm³y⁻¹ plant. Our results also show that one of the most important factors that affect investment success is the price of ethanol. At an ethanol price of $484.21 m⁻³ ($1.84 per gallon), and assuming other factors remain unchanged, the estimated net present value (NPV) for the 0.076 hm³y⁻¹ plant is $41.54 million. By comparison, the estimated NPV for the 0.038 hm³y⁻¹ plant is only $8.30 million. Other factors such as changes in prices of co-products and utilities have a relatively minor effect on investment viability.     

Valuation of investments in natural resources using contingent-claim framework with application to bituminous coal developments in Korea

As Brennan and Schwartz [Brennan M, Schwartz E. Evaluating natural resource investment. Journal of Business 1985;58:135–57] point out in their pioneering work, the valuation of natural resources projects is particularly difficult due to the high degree of uncertainty in output prices of resources. In general, there are two competing procedures to evaluate risky projects in natural resources developments. One is decision analytic, based on traditional discounted cash flow and stochastic dynamic programming [Fleten SE, Maribu KM, Wangensteen I. Optimal investment strategies in decentralized renewable power generation under uncertainty. Energy 2007;32:803–15; Smith J, McCardle K. Valuing oil properties: integrating option pricing and decision analysis approaches. Operations Research 1998;46(2):198–217; Szklo AS, Carneiro JTG, Machado G. Break-even price for upstream activities in Brazil: evaluation of the opportunity cost of oil production delay in a non-mature sedimentary production region. Energy 2008;33:589–600], and the other is contingent claims analysis, based on the no-arbitrage theory of financial markets [Brennan M, Schwartz E. Evaluating natural resource investment. Journal of Business 1985;58:135–57; Emhjellen M, Alaouze CM. A comparison of discounted cash flow and modern asset pricing methods – project selection and policy implications. Energy Policy 2003;31:1213–20; Laughton D. The management of flexibility in the upstream petroleum industry. The Energy Journal 1998;19:83–114; Paddock L, Siegel D, Smith J. Option valuation of claims on real assets: the case of offshore petroleum leases. Quarterly Journal of Economics 1988;103(3):479–508; Schwartz ES. Valuing long-term commodity assets. Journal of Energy Finance and Development 1998;3(2):85–99; Sezgen O, Goldman CA, Krishnarao P. Option value of electricity demand response. Energy 2007;32:108–19]. In this paper, we use the second approach to develop a new model, and the main contributions are providing a tractable and realistic means of incorporating the option value and optimal timing into the investment decision in natural resources and presenting an example that shows option and timing considerations to be important. We demonstrated the validity of the model using both numerical analysis and real data.     

Life cycle assessment and life cycle costing of bioethanol from sugarcane in Brazil

Brazil has always been the pioneer in the application of bioethanol as a main fuel for automobiles, hence environmental and economic analyses of the Brazilian ethanol industries are of crucial importance. This study presents a comparative life cycle assessment (LCA) on gasoline and ethanol as fuels, and with two types of blends of gasoline with bioethanol, all used in a midsize car. The focus is on a main application in Brazil, sugarcane based ethanol. The results of two cases are presented: base case—bioethanol production from sugarcane and electricity generation from bagasse; future case—bioethanol production from both sugarcane and bagasse and electricity generation from wastes. In both cases sugar is co-produced. The life cycles of fuels include gasoline production, agricultural production of sugarcane, ethanol production, sugar and electricity co-production, blending ethanol with gasoline to produce E10 (10% of ethanol) and E85 (85%), and finally the use of gasoline, E10, E85 and pure ethanol. Furthermore, a life cycle costing (LCC) was conducted to give an indication on fuel economy in both cases. The results show that in the base case less GHG is emitted; while the overall evaluation of these fuel options depends on the importance attached to different impacts. The future case is certainly more economically attractive, which has been the driving force for development in the ethanol industry in Brazil. Nevertheless, the outcomes depend very much on the assumed price for crude oil. In LCC a steady-state cost model was used and only the production cost was taken into account. In the real market the prices of fuels are very much dependent on the taxes and subsidies. Technological development can help in lowering both the environmental impact and the prices of the ethanol fuels.     

Speculation and volatility spillover in the crude oil and agricultural commodity markets: A Bayesian analysis

This paper assesses factors that potentially influence the volatility of crude oil prices and the possible linkage between this volatility and agricultural commodity markets. Stochastic volatility models are applied to weekly crude oil, corn, and wheat futures prices from November 1998 to January 2009. Model parameters are estimated using Bayesian Markov Chain Monte Carlo methods. Speculation, scalping, and petroleum inventories are found to be important in explaining the volatility of crude oil prices. Several properties of crude oil price dynamics are established, including mean-reversion, an asymmetry between returns and volatility, volatility clustering, and infrequent compound jumps. We find evidence of volatility spillover among crude oil, corn, and wheat markets after the fall of 2006. This can be largely explained by tightened interdependence between crude oil and these commodity markets induced by ethanol production.     

A multifactor stochastic volatility model of commodity prices

We propose a novel representation of commodity spot prices in which the cost-of-carry and the spot price volatility are both driven by an arbitrary number of risk factors, nesting many existing specifications. The model exhibits unspanned stochastic volatility, provides simple closed-form expressions of commodity futures, and yields analytic formulas of European options on futures. We estimate the model using oil futures and options data, and find that the pricing of traded contracts is accurate for a wide range of maturities and strike prices. The results suggest that at least three risk factors in the spot price volatility are needed to accurately fit the volatility surface of options on oil futures, highlighting the importance of using general multifactor models in pricing commodity contingent claims.