Emanuel Moench

Pricing the Term Structure with Linear Regressions

We show how to price the time series and cross section of the term structure of interest rates using a three-step linear regression approach. Our method allows computationally fast estimation of term structure models with a large number of pricing factors. We present specification tests favoring a model using five principal components of yields as factors. We demonstrate that this model outperforms the Cochrane and Piazzesi (2008) four-factor specification in out-of-sample exercises but generates similar in-sample term premium dynamics. Our regression approach can also incorporate unspanned factors and allows estimation of term structure models without observing a zero-coupon yield curve.

A hierarchical factor analysis of U.S. housing market dynamics

This paper studies the linkages between housing and consumption in the United States taking into account regional variation. We estimate national and regional housing factors from a comprehensive set of U.S. price and quantity data available at mixed frequencies and over different time spans. Our housing factors pick up the common components in the data and are less affected by the idiosyncratic noise in individual series. This allows us to get more reliable estimates of the consumption effects of housing market shocks. We find that shocks at the national level have large cumulative effects on retail sales in all regions. Though the effects of regional shocks are smaller, they are also significant. We analyse the driving forces of housing market activity by means of factor‐augmented vector autoregressions. Our results show that lowering mortgage rates has a larger effect than a similar reduction of the federal funds rate. Moreover, lower consumer confidence and stock prices can slow the recovery in the housing market.

The persistent effects of a false news shock

In September 2008, a six-year-old article about the 2002 bankruptcy of United Airlines parent company resurfaced on the Internet and was mistakenly believed to be reporting a new bankruptcy filing by the company. This episode caused the companys stock price to drop by as much as 76% in just a few minutes, before NASDAQ halted trading. After the news had been identified as false, the stock price rebounded, but still ended the day 11.2% below the previous close. We explore this natural experiment by using a simple asset-pricing model to study the aftermath of this false news shock. We find that, after three trading sessions, the companys stock was still trading below the two-standard-deviation band implied by the model and that it returned to within one standard deviation only during the sixth trading session. On the seventh day after the episode, the stock was trading at the level predicted by the asset-pricing model. We investigate several potential explanations for this finding, but fail to find empirical evidence supporting any of them. We also document that the false news shock had a persistent negative effect on the stock prices of other major airline companies. This is consistent with the view that contagion effects would have dominated competitive effects had the bankruptcy actually taken place.