We demonstrate that a financial intermediary-based asset pricing model offers a compelling explanation for a new set of conditional moments of equity term structure and convenience yields. The model’s key mechanism is that the time-varying tightness of intermediaries’ leverage constraints drives significant mean reversion in the price of risk. This model guides us in devising a novel empirical methodology to estimate the tightness of these constraints (i.e., the Relative Tightness Index) from cross-sectional returns of various asset classes. Our findings affirm that this measure significantly drives the dynamics of equity yield slope and convenience yields, both empirically and quantitatively.

Empirical support for the hypothesis that closed-end fund discounts are related to overhanging tax liabilities has been mixed. We introduce a new approach to testing this hypothesis by examining changes in discount levels following distributions of dividends and capital gains. Since distributions reduce future shareholder tax liabilities, the tax liability hypothesis implies that closed-end fund discounts should decline following distributions. Focusing on changes in discounts isolates this tax effect by eliminating the impact of other fund-specific factors on discount levels. Our results support the tax liability hypothesis, showing that short-run fluctuations in discounts are directly affected by taxable distributions.

We develop a specification test and a sequence of model selection procedures for non-nested, overlapping, and nested models based on the second Hansen-Jagannathan distance, which requires a good asset pricing model to not only have small pricing errors but also be arbitrage free. Our methods have reasonably good finite sample performances and are more powerful than existing ones in detecting misspecified models with small pricing errors but are not arbitrage-free and in differentiating models that have similar pricing errors of a given set of test assets. Using the Fama and French size and book-to-market portfolios, we reach dramatically different conclusions on model performances based on our approach and existing methods.