We generalize the Franke-Härdle (1992) spectral-density bootstrap to the multivariate case. The extension is nontrivial and facilitates use of the Franke-Härdle bootstrap in frequency-domain econometric work, which often centers on crossvariable dynamic interactions. We document the bootstrap's good finite-sample performance in a small Monte Carlo experiment, and we conclude by highlighting key directions for future research.

It depends. If volatility fluctuates in a forecastable way, volatility forecasts are useful for risk management (hence the interest in volatility forecastability in the risk management literature). Volatility forecastability, however, varies with horizon, and different horizons are relevant in different applications. Moreover, existing assessments of volatility forecastability are plagued by the fact that they are joint assessments of volatility forecastability and an assumed model, and the results can vary not only with the horizon but also with the assumed model. To address this problem, we develop a model-free procedure for assessing volatility forecastability across horizons. Perhaps surprisingly, we find that volatility forecastability decays quickly with horizon. Volatility forecastability—although clearly of relevance for risk management at the short horizons relevant for, say, trading desk management—may be much less important at longer horizons.

We propose a constructive, multivariate framework for assessing agreement between (generally misspecified) dynamic equilibrium models and data, which enables a complete second-order comparison of the dynamic properties of models and data. We use bootstrap algorithms to evaluate the significance of deviations between models and data, and we use goodness-of-fit criteria to produce estimators that optimize economically-relevant loss functions. We provide a detailed illustrative application to modelling the U.S. cattle cycle.

Baillie and Bollerslev (1989) have recently argued that nominal dollar spot exchange rates are cointegrated. Here we examine an immediate implication of their finding, namely, that cointegration implies an error‐correction representation yielding forecasts superior to those from a martingale benchmark, in light of a large earlier literature highlighting the predictive superiority of the martingale. In an out‐of‐sample forecasting exercise, we find the martingale model to be superior. We then perform a battery of improved cointegration tests and find that the evidence for cointegration is much less strong than previously thought, a result consistent with the outcome of the forecasting exercise.

Consumption (Economics); Income

Real-Time Macroeconomic Monitoring: Real Activity, Inflation, and Interactions by S. Borağan Aruoba and Francis X. Diebold. Published in volume 100, issue 2, pages 20-24 of American Economic Review, May 2010

Does the termination probability of a business expansion or contraction increase with age? This question may be formally addressed by analyzing the nature of duration dependence in aggregate economic activity. Our null hypothesis is that there is no duration dependence, which we test via intentionally nonparametric procedures. We also argue that common notion of business cycle periodicity can be usefully interpreted in terms of whole-cycle duration dependence. We find some evidence for duration dependence in whole cycles and in prewar expansions, but little evidence elsewhere.

The turmoil in the capital markets in 1997 and 1998 has highlighted the need for systematic stress testing of banks' portfolios, including both their trading and lending books. We propose that underlying macroeconomic volatility is a key part of a useful conceptual framework for stress testing credit portfolios, and that credit migration matrices provide the specific linkages between underlying macroeconomic conditions and asset quality. Credit migration matrices, which characterize the expected changes in credit quality of obligors, are cardinal inputs to many applications, including portfolio risk assessment, modeling the term structure of credit risk premia, and pricing of credit derivatives. They are also an integral part of many of the credit portfolio models used by financial institutions. By separating the economy into two states or regimes, expansion and contraction, and conditioning the migration matrix on these states, we show that the loss distribution of credit portfolios can differ greatly, as can the concomitant level of economic capital to be assigned.

A growing literature documents important gains in asset return volatility forecasting via use of realized variation measures constructed from high-frequency returns. We progress by using newly developed bipower variation measures and corresponding nonparametric tests for jumps. Our empirical analyses of exchange rates, equity index returns, and bond yields suggest that the volatility jump component is both highly important and distinctly less persistent than the continuous component, and that separating the rough jump moves from the smooth continuous moves results in significant out-of-sample volatility forecast improvements. Moreover, many of the significant jumps are associated with specific macroeconomic news announcements.

From a macroeconomic perspective, the shortterm interest rate is a policy instrument under the direct control of the central bank, which adjusts the rate to achieve its economic stabilization goals. From a finance perspective, the short rate is a fundamental building block for yields of other maturities, which are just riskadjusted averages of expected future short rates. Thus, as illustrated by much recent research, a joint macro-finance modeling strategy will provide the most comprehensive understanding of the term structure of interest rates. In this paper, we discuss some salient questions that arise in this research, and we also present a new examination of the relationship between two prominent dynamic, latent factor models in this literature: the Nelson-Siegel and affine no-arbitrage term-structure models. I. Questions about Modeling Yields 1. Why Use Factor Models for Bond Yields?—The first problem faced in term-structure modeling is how to summarize the price information at any point in time for the large number of nominal bonds that are traded. In fact, since only a small number of sources of systematic risk appear to underlie the pricing of the myriad of tradable financial assets, nearly all bond price information can be summarized with just a few constructed variables or factors. Therefore, yield-curve models almost invariably employ a structure that consists of a small set of factors and the associated factor loadings