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I find that the standard class of a#ne models produces poor forecasts of future changes in Treasury yields. Better forecasts are generated by assuming that yields follow random walks. The failure of these models is driven by one of their key features: The compensation that investors receive for facing risk is a multiple of the variance of the risk. This means that risk compensation cannot vary independently of interest rate volatility. I also describe and empirically estimate a class of models that is broader than the standard a#ne class. These "essentially a#ne" models retain the tractability of the usual models, but allow the compensation for interest rate risk to vary independently of interest rate volatility. This additional flexibility proves useful in forming accurate forecasts of future yields. Address correspondence to the University of California, Haas School of Business, 545 Student Services Building #1900, Berkeley, CA 94720. Phone: 510-642-1435. Email address: du#ee@haas.b...

Central banks have several reasons for extracting information from asset prices. Asset prices may embody more accurate and more up-to-date macroeconomic data than what is currently published or directly available to policy makers. Aberrations in some asset prices may indicate

We examine the empirical evidence on the expectations hypothesis of the term structure of interest rates in the United States, the United Kingdom, and Germany using the Campbell-Shiller (1991) regressions and a vector-autoregressive methodology. We argue that anomalies in the U.S. term structure, documented by Campbell and Shiller (1991), may be due to a generalized peso problem in which a high-interest rate regime occuued less frequently in the sample of U.S. data than was rationally anticipated. We formalize this idea as a regime-switching model of short-term interest rates estimated with data from seven countries. Technically, this model extends recent research on regime-switching models with state-dependent transitions to a cross-sectional setting. Use of the small sample distributions generated by the regime-switching model for inference considerably weakens the evidence against the expectations hypothesis, but it remains somewhat implausible that our data-generating process produced the U.S. data. However, a model that combines moderate time-variation in term premiums with peso-problem effects is largely consistent with term structure

We develop a term structure model where the short interest rate and the market price of risks are subject to discrete regime shifts. Empirical evidence from efficient method of moments estimation provides considerable support for the regime shifts model. Standard models, which include affine specifications with up to three factors, are sharply rejected in the data. Our diagnostics show that only the regime shifts model can account for the well-documented violations of the expectations hypothesis, the observed conditional volatility, and the conditional correlation across yields. We find that regimes are intimately related to business cycles. MANY PAPERS DOCUMENT THAT THE UNIVARIATE short interest rate process can be reasonably well modeled in the time series as a regime switching process ~see Hamilton ~1988!, Garcia and Perron ~1996!!. In addition to this statistical evidence, there are economic reasons as well to believe that regime shifts are important to understanding the behavior of the entire yield curve. For example, business cycle expansion and contraction “regimes ” potentially

A lot, including a few things you may not expect. Previous studies find that the term spread forecasts GDP but these regressions are unconstrained and do not model regressor endogeneity. We build a dynamic model for GDP growth and yields that completely characterizes expectations of GDP. The model does not permit arbitrage. Contrary to previous findings, we predict that the short rate has more predictive power than any term spread. We confirm this finding by forecasting GDP out-of-sample. The model also recommends the use of lagged GDP and the longest maturity yield to measure slope. Greater efficiency enables the yield-curve model to produce superior out-of-sample GDP forecasts than unconstrained OLS at all horizons.

This paper empirically tests the expectations hypothesis on both daily EONIA swap rates and monthly EURIBOR rates extended backwards with German LIBOR rates. In addition, we quantify the size of the risk premia in the money market at maturities of one, three, six and nine months. Using implied forward and spot rates in a cointegrated VAR model, we find that the data support the expectations hypothesis in the euro area and in Germany prior to 1999. We find that risk premia are relatively limited at the shorter maturities but more significant at maturities of six and nine months. Furthermore, the results on LIBOR/EURIBOR rates tentatively indicate a downward shift in the structure of the risk premia after the introduction of the euro.

In 2004 all publications will carry a motif taken from the €100 banknote. This paper can be downloaded without charge from

Abstract: We estimate a model that summarizes the yield curve using latent factors (specifically, level, slope, and curvature) and also includes observable macroeconomic variables (specifically, real activity, inflation, and the monetary policy instrument). Our goal is to provide a characterization of the dynamic interactions between the macroeconomy and the yield curve. We find strong evidence of the effects of macro variables on future movements in the yield curve and evidence for a reverse influence as well. We also relate our results to the expectations hypothesis.

Richardson were especially helpful on this manuscript. Thanks to Deepak Agrawal for computational assistance and thoughtful comments. I would also like to thank Yakov Amihud, Anthony Lynch, Jennifer Carpenter, Paul Wachtel and Cliff Green for their comments and help. As always, none of the aforementioned are responsible for any opinions expressed or any errors. 1 One of the fundamental issues in finance is what are the factors that affect expected return on assets, the sensitivity of expected return to these factors, and the reward for bearing this sensitivity. There is a long history of testing in this area, and it is clearly one of the most investigated areas in finance. Almost all of the testing I am aware of involves using realized returns as a proxy for expected returns. The use of average realized returns as a proxy for expected returns relies on a belief that information surprises tend to cancel out over the period of the study and realized returns are therefore an unbiased estimate of expected returns. However, I believe that there is ample evidence that this belief is misplaced. There are periods longer than ten years where stock market realized returns are on average less than the risk-free rate (1973 to 1984). There are periods longer than fifty years in which risky long-term bonds on average underperformed the risk free