A major issue with predicting inflation rates using predictive regressions is that estimation errors can overwhelm the information content. This article proposes a new approach that uses a monetary-policy rule as a bridge between inflation rates and short-term interest rates and relies on the forward-interest-rate curve to predict future interest-rate movements. The 2-step procedure estimates the predictive relation not through a predictive regression but far more accurately through the contemporaneous monetary-policy linkage. Historical analysis shows that the approach outperforms random walk out of sample by 30%–50% over horizons from 1 to 5 years.

The aim of this paper is to forecast (out-of-sample) the distribution of financial returns based on realized volatility measures constructed from high-frequency returns. We adopt a semi-parametric model for the distribution by assuming that the return quantiles depend on the realized measures and evaluate the distribution, quantile and interval forecasts of the quantile model in comparison to a benchmark GARCH model. The results suggest that the model outperforms an asymmetric GARCH specification when applied to the S&P 500 futures returns, in particular on the right tail of the distribution. However, the model provides similar accuracy to a GARCH (1,1) model when the 30-year Treasury bond futures return is considered.

We present resiliency as a measure of liquidity and assess its relationship to expected returns. We establish a covariance-based measure, RES, that captures opening period resiliency, and use it to find a significant nonresiliency premium that ranges from 33 to 57 basis points per month. The premium persists after accounting for an extensive list of other liquidity-related measures and control variables. The results are significant for both value-weighted and equal-weighted returns, when micro-cap stocks are excluded, and for a sample of large cap stocks. The premium is particularly pronounced when trading volume is high.

This paper analyzes the causes and consequences of founding family engagement in firm ownership and management. We hypothesize that families manage their firms because they are able to make contributions that non-family managers cannot provide. However, roadblocks arising from within the family, from markets, and from surrounding institutions challenge family ownership. We propose a new framework for organizing these assets and roadblocks, called the family business map; this framework is useful for categorizing the papers presented in this Special Issue. We support the predictions of the framework with evidence from Chinese family firm succession, and conclude that family firm organization is an adaptation to environmental opportunities and constraints. We end the paper with suggestions for future research.