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- oil price (5)
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- risk premium (2)
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#### Institute

- Did the renewable fuel standard shift market expectations of the price of ethanol? (2016)
- It is commonly believed that the response of the price of corn ethanol (and hence of the price of corn) to shifts in biofuel policies operates in part through market expectations and shifts in storage demand, yet to date it has proved difficult to measure these expectations and to empirically evaluate this view. We utilize a recently proposed methodology to estimate the market’s expectations of the prices of ethanol, unfinished motor gasoline and crude oil at horizons from three months to one year. We quantify the extent to which price changes were anticipated by the market, the extent to which they were unanticipated, and how the risk premium in these markets has evolved. We show that the Renewable Fuel Standard (RFS) is likely to have increased ethanol price expectations by as much $1.45 in the year before and in the year after the implementation of the RFS had started. Our analysis of the term structure of expectations provides support for the view that a shift in ethanol storage demand starting in 2005 caused an increase in the price of ethanol. There is no conclusive evidence that the tightening of the RFS in 2008 shifted market expectations, but our analysis suggests that policy uncertainty about how to deal with the blend wall raised the risk premium in the ethanol futures market in mid-2013 by as much as 50 cents at longer horizons. Finally, we present evidence against a tight link from ethanol price expectations to corn price expectations and hence to storage demand for corn in 2005-06.

- Are product spreads useful for forecasting? An empirical evaluation of the Verleger hypothesis (2013)
- Notwithstanding a resurgence in research on out-of-sample forecasts of the price of oil in recent years, there is one important approach to forecasting the real price of oil which has not been studied systematically to date. This approach is based on the premise that demand for crude oil derives from the demand for refined products such as gasoline or heating oil. Oil industry analysts such as Philip Verleger and financial analysts widely believe that there is predictive power in the product spread, defined as the difference between suitably weighted refined product market prices and the price of crude oil. Our objective is to evaluate this proposition. We derive from first principles a number of alternative forecasting model specifications involving product spreads and compare these models to the no-change forecast of the real price of oil. We show that not all product spread models are useful for out-of-sample forecasting, but some models are, even at horizons between one and two years. The most accurate model is a time-varying parameter model of gasoline and heating oil spot spreads that allows the marginal product market to change over time. We document MSPE reductions as high as 20% and directional accuracy as high as 63% at the two-year horizon, making product spread models a good complement to forecasting models based on economic fundamentals, which work best at short horizons.

- Anticipation, tax avoidance, and the price elasticity of gasoline demand (2015)
- Traditional least squares estimates of the responsiveness of gasoline consumption to changes in gasoline prices are biased toward zero, given the endogeneity of gasoline prices. A seemingly natural solution to this problem is to instrument for gasoline prices using gasoline taxes, but this approach tends to yield implausibly large price elasticities. We demonstrate that anticipatory behavior provides an important explanation for this result. We provide evidence that gasoline buyers increase gasoline purchases before tax increases and delay gasoline purchases before tax decreases. This intertemporal substitution renders the tax instrument endogenous, invalidating conventional IV analysis. We show that including suitable leads and lags in the regression restores the validity of the IV estimator, resulting in much lower and more plausible elasticity estimates. Our analysis has implications more broadly for the IV analysis of markets in which buyers may store purchases for future consumption.

- A general approach to recovering market expectations from futures prices with an application to crude oil (2014)
- Futures markets are a potentially valuable source of information about market expectations. Exploiting this information has proved difficult in practice, because the presence of a time-varying risk premium often renders the futures price a poor measure of the market expectation of the price of the underlying asset. Even though the expectation in principle may be recovered by adjusting the futures price by the estimated risk premium, a common problem in applied work is that there are as many measures of market expectations as there are estimates of the risk premium. We propose a general solution to this problem that allows us to uniquely pin down the best possible estimate of the market expectation for any set of risk premium estimates. We illustrate this approach by solving the long-standing problem of how to recover the market expectation of the price of crude oil. We provide a new measure of oil price expectations that is considerably more accurate than the alternatives and more economically plausible. We discuss implications of our analysis for the estimation of economic models of energy-intensive durables, for the debate on speculation in oil markets, and for oil price forecasting.