Robert C. Ready

Oil Prices and Long-Run Risk

I add an oil good endowment to the Long-Run Risk model of Bansal and Yaron (2004) to study the asset pricing implications of a constrained oil supply. Lack of responsiveness of the oil endowment changes both the physical and risk-neutral dynamics of oil prices, and explains significant differences in the observed behavior of oil futures prices and returns from 2004 to 2008 relative to the prior 15 years. The model predicts that an unresponsive oil supply increases the risk of exogenous oil shocks, but mitigates risk from other shocks to growth, thereby lowering overall economic risk and the equity premium.

Currency Risk Factors in a Recursive Multi-Country Economy

Focusing on the ten countries with the most-traded currencies, we provide novel empirical evidence about the existence of significant heterogenous exposure to global growth news shocks. We incorporate this empirical fact in a frictionless risk-sharing model with recursive preferences, multiple countries, and multiple consumption goods whose supply is subject to both global and local short- and long-run shocks. Since news shocks are priced, heterogenous exposure to global long-run growth shocks results in both a relevant reallocation of international resources and currency adjustments. Our unified framework replicates the properties of the HML-FX and HML-NFA carry trade strategies studied by Lustig et al. (2011) and Della Corte et al. (2013).

Fracking, Drilling, and Asset Pricing: Estimating the Economic Benefits of the Shale Revolution

We quantify the effect of a significant technological innovation, shale oil development, on asset prices. Using stock returns on major news announcement days allows us to link aggregate stock price fluctuations to shale technology innovations. We exploit cross-sectional variation in industry portfolio returns on days of major shale oil-related news announcements to construct a shale mimicking portfolio. This portfolio can explain a significant amount of variation in aggregate stock market returns, but only during the time period of shale oil development, which begins in 2012. Our estimates imply that

Using Futures Prices to Filter Short-Term Volatility and Recover a Latent, Long-Term Price Series for Oil

3.5 trillion of the increase in aggregate U.S. equity market capitalization since 2012 can be explained by this mimicking portfolio. Similar portfolios based on major monetary policy announcements do not explain the positive market returns over this period. We also show that exposure to shale oil technology has significant explanatory power for the cross-section of employment growth rates of U.S. industries over this period.

Oil Prices and the Stock Market

Oil prices are very volatile. But much of this volatility seems to reflect short-term, transitory factors that may have little or no influence on the price in the long run. Many major investment decisions should be guided by a model of the long-term price of oil and its dynamics. Data on futures prices can be used to filter out the short-term volatility and recover a time series of the latent, long-term price of oil. We test a leading model known as the 2-factor or short-term, long-term model. While the generated latent price variable is clearly an improvement over the raw spot oil price series, we also find that (1) the generated long-term price series still contains some of the short-term volatility, and (2) a naive use of a long-maturity futures price as a proxy for the long-term price successfully filters out a large majority of the short-term volatility and so may be convenient alternative to the more cumbersome model.