We study whether it is better to enforce the zero lower bound (ZLB) in models of U.S. Treasury yields using a shadow rate model or a quadratic term structure model. We show that the models achieve a similar in-sample fit and perform comparably in matching conditional expectations of future yields. However, when the recent ZLB period is included in the sample, the models’ ability to match conditional expectations away from the ZLB deteriorates because the time-series dynamics of the pricing factors change. In addition, neither model provides a reasonable description of conditional volatilities when yields are away from the ZLB.

We examine whether industry product market conditions are important in assessing the benefits and costs of chief executive officer (CEO) power. We find that firms are more likely to have powerful CEOs in high demand product markets where firms are facing entry threats. In these markets, investors react favorably to announcements granting more power to CEOs, and CEO power is associated with higher market value, sales growth, investment, advertising, and the introduction of more new products. Our results remain significant when addressing the endogeneity of CEO power by instrumenting CEO power with past non-CEO executive and director sudden deaths.

We investigate whether access to information prior to an initial public offering (IPO) generates a trading advantage after the IPO. We find that limited partners (LPs) of lead venture capital funds obtain high returns when they invest in newly listed stocks backed by their funds. These returns are not explained by LPs’ differing stock-picking abilities, and they are higher when LPs’ information advantage over the public is higher. LPs are more likely to invest if they have an information advantage, and access to information eliminates the familiarity bias that they display otherwise.

We propose a consistent and computationally efficient 2-step methodology for the estimation of multidimensional non-Gaussian asset models built using Lévy processes. The proposed framework allows for dependence between assets and different tail behaviors and jump structures for each asset. Our procedure can be applied to portfolios with a large number of assets because it is immune to estimation dimensionality problems. Simulations show good finite sample properties and significant efficiency gains. This method is especially relevant for risk management purposes such as, for example, the computation of portfolio Value at Risk and intra-horizon Value at Risk, as we show in detail in an empirical illustration.