This thesis attempts to model the returns on Regional Greenhouse Gas Initiative (RGGI) allowances using logged monthly returns from 2011-2018. This asset, shown to be a useful diversifier in portfolios, has been identified by previous literature to behave similarly to commodities. I used auto-regressive, GARCH, and Markov regime switching models to analyze the returns because the returns displayed changing volatility. These models were comparatively analyzed both in and out-of-sample. In this limited data analysis, the Markov model outperformed both alternatives in-sample. The Markov and Garch models displayed similar predictive power out-of-sample, however neither were particularly effective.