Bond yields respond to policy decisions by the Federal Reserve and vice versa. To learn about these responses, I model a high‐frequency policy rule based on yield curve information and an arbitrage‐free bond market. In continuous time, the Fed's target is a pure jump process. Jump intensities depend on the state of the economy and the meeting calendar of the Federal Open Market Committee. The model has closed‐form solutions for yields as functions of a few state variables. Introducing monetary policy helps to match the whole yield curve, because the target is an observable state variable that pins down its short end and introduces important seasonalities around FOMC meetings. The volatility of yields is "snake shaped," which the model explains with policy inertia. The policy rule crucially depends on the two‐year yield and describes Fed policy better than Taylor rules.

Corporate cash flows are highly volatile and strongly procyclical. We examine the asset-pricing implications of the sensitivity of corporate cash flows to economic shocks within a continuous-time model in which dividends are a stochastic fraction of aggregate consumption. We provide closed-form solutions for stock values and show that the equity premium can be represented as the sum of three components which we call the consumption-risk, event-risk, and corporate-risk premia. Calibrated to historical data, the model implies a total equity premium many times larger than in the standard model. The model also generates levels of equity volatility consistent with those experienced in the stock market.

Recent events have stimulated interest in the joint behavior of prices and quantities in credit markets. Data sources such as the Federal Reserve Board’s Flow of Funds Accounts (FFA) provide statistics on a rich set of credit market instruments. However, it is challenging to inter pret such data using economic models that speak to the allocation of risk across agents, such as households or intermediaries. On the one hand, an instrument class such as “Treasury bonds” typically contains many dif ferent instruments that trade at different prices and have different exposure to interest rate shocks (for example, because of differences in duration). On the other hand, a lot of the price movements in instruments like Treasury bonds and mortgage backed securities are due to com mon interest rate shocks, making those instru ments close substitutes from a portfolio choice perspective. For understanding how interest rate risk is allocated in the economy, one would thus like to use information on many positions at the same time, rather than, say, focus on one set of instruments only. At the same time, models with many closely substitutable assets are problem atic. Instead, it would be desirable to compress position data into simple sets of portfolios, like “long” and “short” bonds, but with some con fidence that the risk properties of the original instruments are not lost along the way. DemanD anD Supply for Government BonDS

This paper studies household beliefs dur ing the recent US housing boom. The first part presents evidence from the Michigan Survey of Consumers. To characterize the heterogeneity in households’ views about housing and the econ omy, we perform a cluster analysis on survey responses at different stages of the boom. The estimation always finds a small cluster of house holds that believe it is a good time to buy a house because house prices will rise further. The size of this “momentum” cluster strongly increased toward the end of the boom. The second part of the paper provides a simple search model of the housing market to show how a small number of optimistic investors can have a large effect on prices without buying a large share of the housing stock. The raw survey data suggest that the housing boom had two distinct phases. During the early boom years 2002–2003, a large and increasing fraction of households believed that the time for buying a house was good. This fraction peaked at 85.2 percent in 2003:II. The most important reason—cited by up to 72 percent of house

This paper considers a consumption-based asset pricing model where housing is explicitly modeled both as an asset and as a consumption good. Nonseparable preferences describe households’ concern with composition risk, that is, fluctuations in the relative share of housing in their consumption basket. Since the housing share moves slowly, a concern with composition risk induces low frequency movements in stock prices that are not driven by news about cash flow. Moreover, the model predicts that the housing share can be used to forecast excess returns on stocks. We document that this indeed true in the data. The presence of composition risk also implies that the riskless rate is low which further helps the model improve on the standard CCAPM.

We study time variation in expected excess bond returns. We run regressions of one-year excess returns on initial forward rates. We find that a single factor, a single tent-shaped linear combination of forward rates, predicts excess returns on one-to five-year maturity bonds with R 2 up to 0.44. The return-forecasting factor is countercyclical and forecasts stock returns. An important component of the return-forecasting factor is unrelated to the level, slope, and curvature movements described by most term structure models. We document that measurement errors do not affect our central results.

We measure monetary policy shocks as changes in the Fed funds target rate that surprise bond markets in daily data. These shock series avoid the omitted variable, time-varying parameter, and orthogonalization problem of monthly VARs, and do not impose the expectations hypothesis. We find surprisingly large and persistent responses of bond yields to these shocks. 10 year rates rise as much as 8/10 of a percent to a one percent target shock. The usual view that monetary policy only temporarily raises long term rates and influences inflation would lead one to predict a negative long rate response.

This paper uses an assignment model to understand the cross section of house prices within a metro area. Movers’ demand for housing is derived from a life-cycle problem with credit market frictions. Equilibrium house prices adjust to assign houses that differ by quality to movers who differ by age, income, and wealth. To quantify the model, we measure distributions of house prices, house qualities, and mover characteristics from micro-data on San Diego County during the 2000s boom. The main result is that cheaper credit for poor households was a major driver of prices, especially at the low end of the market.

This paper studies housing markets where a subset of houses in a restricted area is available exclusively to a subset of “eligible” buyers. An empirical part shows that houses on Stanford campus (available only to faculty) trade at substantial discounts to comparable houses off campus. The theoretical part describes an assignment model with heterogeneous houses and buyers which predicts such discounts if the matchup of quality and buyer pools is sufficiently different inside versus outside the restricted area. The restriction can distort allocations by making eligible buyers choose either higher or lower qualities than ineligible buyers with the same characteristics.

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