Gregory R. Duffee

Term Premia and Interest Rate Forecasts in Affine Models

The standard class of affine models produces poor forecasts of future 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: Compensation for risk is a multiple of the variance of the risk. Thus risk compensation cannot vary independently of interest rate volatility. I also describe a broader class of models. These “essentially affine” models retain the tractability of standard models, but allow compensation for interest rate risk to vary independently of interest rate volatility. This additional f lexibility proves useful in forecasting future yields. CAN WE USE F INANCE THEORY to tell us something about the empirical behavior of Treasury yields that we do not already know? In particular, can we sharpen our ability to predict future yields? A long-established fact about Treasury yields is that the current term structure contains information about future term structures. For example, long-maturity bond yields tend to fall over time when the slope of the yield curve is steeper than usual. These predictive relations are based exclusively on the time-series behavior of yields. To rule out arbitrage, the cross-sectional and time-series characteristics of the term structure are linked in an internally consistent way. In principle, imposing these restrictions should allow us to exploit more of the information in the current term structure, and thus improve forecasts. But in practice, existing no-arbitrage models impose other restrictions for the sake of tractability; thus their value as forecasting tools is a priori unclear. I examine the forecasting ability of the affine class of term structure models. By “affine,” I refer to models where zero-coupon bond yields, their physical dynamics, and their equivalent martingale dynamics are all affine functions of an underlying state vector. A variety of nonaffine models have been developed, but the tractability and apparent richness of the affine class has led the finance profession to focus most of its attention on such models. Although forecasting future yields is important in its own right, a model that is consistent with finance theory and produces accurate forecasts can make a deeper contribution to finance. It should allow us to address a key

The relation between Treasury yields and corporate bond yield spreads

Because the option to call a corporate bond should rise in value when bond yields fall, the relation between noncallable Treasury yields and spreads of corporate bond yields over Treasury yields should depend on the callability of the corporate bond. I confirm this hypothesis for investment-grade corporate bonds. Although yield spreads on both callable and noncallable corporate bonds fall when Treasury yields rise, this relation is much stronger for callable bonds. This result has important implications for interpreting the behavior of yields on commonly used corporate bond indexes, which are composed primarily of callable bonds. Copyright The American Finance Association 1998.

Stock Returns and Volatility: A Firm-Level Analysis

It has been previously documented that individual firms stock return volatility rises after stock prices fall. This paper finds that this statistical relation is largely due to a positive contemporaneous relation between firm stock returns and firm stock return volatility. This positive relation is strongest for both small firms and firms with little financial leverage. At the aggregate level, the sign of this contemporaneous relation is reversed. The reasons for the difference between the aggregate- and firm-level relations are explored.

Estimation of Dynamic Term Structure Models

We study the finite-sample properties of some of the standard techniques used to estimate modern term structure models. For sample sizes and models similar to those used in most empirical work, we reach three surprising conclusions. First, while maximum likelihood works well for simple models, it produces strongly biased parameter estimates when the model includes a flexible specification of the dynamics of interest rate risk. Second, despite having the same asymptotic efficiency as maximum likelihood, the small-sample performance of Efficient Method of Moments (a commonly used method for estimating complicated models) is unacceptable even in the simplest term structure settings. Third, the linearized Kalman filter is a tractable and reasonably accurate estimation technique, which we recommend in settings where maximum likelihood is impractical.

Credit derivatives in banking: Useful tools for managing risk?

Abstract We model the effects on banks of the introduction of a market for credit derivatives; in particular, credit-default swaps. A bank can use such swaps to temporarily transfer credit risks of their loans to others, reducing the likelihood that defaulting loans trigger the banks financial distress. Because credit derivatives are more flexible at transferring risks than are other, more established tools, such as loan sales without recourse, these instruments make it easier for banks to circumvent the “lemons” problem caused by banks’ superior information about the credit quality of their loans. However, we find that the introduction of a credit-derivatives market is not necessarily desirable because it can cause other markets for loan risk-sharing to break down.

Bond Pricing and the Macroeconomy

This chapter reviews some of the academic literature that links nominal and real term structures with the macroeconomy. The main conclusion is that none of our models is consistent with basic properties of nominal yields. It is difficult to explain the average shape of the nominal yield curve, the variation of yields over time, and the predictability of excess bond returns. There are two overarching problems. First, much of the variation over time in economic activity is orthogonal to variation in nominal yields, and vice versa. Second, although mean excess returns to nominal Treasury bonds are positive, these returns do not appear to positively covary with risks that require compensation, at least according to standard asset-pricing models.

Forecasting Interest Rates

This chapter discusses what the asset-pricing literature concludes about the forecastability of interest rates. It outlines forecasting methodologies implied by this literature, including dynamic, no-arbitrage term structure models and their macro-finance extensions. It also reviews the empirical evidence concerning the predictability of future yields on Treasury bonds and future excess returns to holding these bonds. In particular, it critically evaluates theory and evidence that variables other than current bond yields are useful in forecasting.

What's Good for GM...? Using Auto Industry Stock Returns to Forecast Business Cycles and Test the Q-Theory of Investment

We examine the ability of auto industry stock returns to forecast quarterly changes in the growth rates of real GDP, consumption, and investment. We find that auto stock returns are superior to aggregate stock market returns in predicting growth rates of GDP and various forms of consumption. The superior predictive power of auto returns holds for both in-sample and out-of-sample forecasts and has not declined over time. We then apply a finding in this paper---that market returns have no explanatory power for future output or consumption growth when auto returns are included in the regression---to analyze the causal relation between the stock market and investment. We use auto returns to proxy for forecasts of future fundamentals, allowing market returns to capture the effect of the stock market on investment. We find that aggregate returns forecast equipment investment in the presence of auto returns, providing empirical support for q-theory. Results for structures investment are less convincing.