Expected returns on commodity ETFs and their underlying assets

This paper proposes a new way of estimating ETFs' expected returns. Instead of using traditional CAPM-like expected return models on ETFs' market prices, it consists of implementing ETFs' investment strategy on the underlying assets and using these assets' pricing models to estimate the expected returns on the ETFs. The hypothesis is that whenever valuable knowledge is available on the underlying asset returns, this information can be helpful when estimating expected ETF returns.

We illustrate our approach by choosing the United States Oil Fund (USO), the largest oil futures-based ETF. We propose estimating ETF returns using their investment strategy in oil futures and an oil pricing model. We use a three-factor stochastic process for oil futures and forecasts calibrated using a Kalman Filter and maximum likelihood estimation procedure.

Using historical futures prices, we successfully replicate historical NAV values following their investment strategy. We then estimate ETFs' expected returns using NAVs as a proxy for ETFs' market values and implement their investment strategy priced using the oil price model. We then compare our results with the more traditional CAPM expected return estimation, obtaining a similar average but a time-varying expected ETF return that reacts to market conditions and allows us to analyze their macroeconomic determinants.