This paper introduces a new confidence interval (CI) for the autoregressive parameter (AR) in an AR(1) model that allows for conditional heteroskedasticity of a general form and AR parameters that are less than or equal to unity. The CI is a modification of Mikusheva's (2007a) modification of Stock's (1991) CI that employs the least squares estimator and a heteroskedasticity-robust variance estimator. The CI is shown to have correct asymptotic size and to be asymptotically similar (in a uniform sense). It does not require any tuning parameters. No existing procedures have these properties. Monte Carlo simulations show that the CI performs well in finite samples in terms of coverage probability and average length, for innovations with and without conditional heteroskedasticity.

Previous research indicates mixed conclusions on the potential mispricing of equity index put options. We examine the returns of put writing and other option strategies by comparing historical option returns with returns generated using option pricing models. We find it is generally possible to reject the hypothesis that put returns are consistent with option pricing models. An implication is that the average cost of buying out-of-the-money put options to provide tail-risk protection to a portfolio may include a significant premium. Our results are based on a sample period of 1987–2012 that includes periods of high volatility in equity returns.

A carbon tax is a promising tool for discouraging the greenhouse gas emissions that cause climate change. In principle, a well-designed tax could reduce the risk of climate change, minimize the cost of emissions reductions, encourage innovation in low-carbon technologies, and raise new public revenue. But designing a real-world carbon tax poses significant challenges. We analyze those challenges from a public finance perspective, emphasizing three tax policy design issues: setting the tax rate, collecting the tax, and using the resulting revenue. The benefits of a carbon tax will depend on how policymakers address those issues.