This study examines the ability of the linear-rational square-root model to simultaneously capture cross-sectional and time-series dynamics of bond yields and their variances. The preferred model specification comprises five factors, two of which are not spanned by the yield curve, introducing unspanned stochastic volatility (USV). This specification provides a close in-sample fit to yields and yield variances, emphasizing the need for USV. Out-of-sample testing demonstrates low variance forecast errors. The specification provides evidence of USV in conditional yield variance and bond risk premia, linked to macroeconomic uncertainty.

We propose a term structure model featuring double-autoregressive dynamics, which can accommodate unit roots and cointegrating relations while maintaining stationarity. In an empirical application, the model reduces in-sample misspecification and significantly improves out-of-sample yield forecasts compared with term structure models based on linear VAR dynamics. The double-autoregressive process implies term premia that resemble the term spread. We test whether these premia help in overcoming the persistence problem of stationary VAR models with mixed results. Finally, we discuss alternative interpretations of the mechanism inducing stationarity in our model.

Some projects take time to build or are slow to yield cash flows. This may impact the dynamics of investment and liquidity management, although few studies test their financial implications. We exploit the peculiar advantages of copper mines as a laboratory to identify cash-flow sensitivities. In this context, investment decisions depend on the expectations of the long run price of the commodity, while the spread between the spot price and this long run expectations shifts current cash-flows. For this study we compiled a sample of copper firms between 2002 and 2012. We do not find significant effects of cash flow on current capital expenditures, but we do observe a systematic cash flow sensitivity of cash holdings, meaning that some of these transitory earnings are retained as liquidity. This cash stockpiling is stronger among financially constrained firms. In a context of time-to-build, our findings support financial theories emphasizing the salience of cash as buffer stock for liquidity in preparation for future investment opportunities.

This paper studies the performance and persistence of Norwegian mutual funds utilizing a new data set of daily returns. Daily data allow us to evaluate the performance over short time horizons in a reliable manner, which is important because the risk exposure of funds can change over time. We complement the existing literature by providing the first study based on daily data outside of the US. Our results show that the performance of top and bottom funds cannot be explained by luck. The performance of these top and bottom funds persists for short horizons, of only up to one year. The mutual fund industry as a whole underperforms the benchmark by approximately the fund fees.

This paper addresses the estimation of default probabilities and associated confidence sets with special focus on rare events. Research on rating transition data has documented a tendency for recently downgraded issuers to be at an increased risk of experiencing further downgrades compared to issuers that have held the same rating for a longer period of time. To capture this non-Markov effect we introduce a continuous-time hidden Markov chain model in which downgrades firms enter into a hidden, ‘excited’ state. Using data from Moody’s we estimate the parameters of the model, and conclude that both default probabilities and confidence sets are strongly influenced by the introduction of hidden excited states.