Asset Mispricing with Francis A. Longstaff and Lubomir Petrasek
NBER Working Paper No. 23231 (2017)
Finalist, 2018 AQR Insight Award
We use a unique dataset of corporate bonds guaranteed by the full faith and credit of the U.S. to test a number of recent theories about why asset prices may diverge from fundamental values. These models emphasize the role of funding liquidity, slow-moving capital, the leverage of financial intermediaries, and other frictions in allowing mispricing to occur. Consistent with theory, we find there are strong patterns of commonality in mispricing and that changes in dealer haircuts and funding costs are significant drivers of mispricing. Furthermore, mispricing can trigger short-term margin and funding-cost spirals. Using detailed bond and dealer-level data, we find that most of the cross-sectional variation in mispricing is explained by differences in dealer funding costs, inventory positions, and trading liquidity measures. These results provide strong empirical support for a number of current theoretical models.
Corporate Bond Illiquidity: Evidence from Government Guarantees, with Lubomir Petrasek.
Nowcasting Gross Domestic Output, with Travis Berge.
Credit Risk, Liquidity, and Lies with Thomas King
International Journal of Central Banking (forthcoming)
We examine the relative effects of credit risk and liquidity in the interbank market using bank-level panel data on Libor submissions and CDS spreads, allowing for the possibility that Libor-submitting firms may strategically misreport their funding costs. We find that interbank spreads were very sensitive to credit risk at the peak of the crisis. However, liquidity premia constitute the bulk of those spreads on average, and Federal Reserve interventions coincide with improvements in liquidity at short maturities. Accounting for misreporting, which is large at times, is important for obtaining these results.
Accepted Version (June 2019)
Measuring the Natural Rate of Interest: A Note on Transitory Shocks with Francisco Vazquez-Grande
Journal of Applied Econometrics (2019) Vol. 34 (3), pp. 425-436
We present evidence that the natural rate of interest is buffeted by both permanent and transitory shocks. We establish this result by estimating a benchmark model with Bayesian methods and loose priors on the unobserved drivers of the natural rate. When subject to transitory shocks, the median estimate for the U.S. economy is more procyclical, displays a less marked secular decline, and is therefore higher following the Great Recession than most estimates in the literature.
Accepted Version, Online Appendix, Replication Code
Empirical Bayesian Density Forecasting in Iowa and Shrinkage for the Monte Carlo Era with Charles H. Whiteman
Journal of Forecasting (2015) Vol. 34 (1), pp. 15-35
The track record of a 20-year history of density forecasts of state tax revenue in Iowa is studied, and potential improvements sought through a search for better-performing “priors” similar to that conducted three decades ago for point forecasts by Doan, Litterman and Sims (Econometric Reviews, 1984). Comparisons of the point and density forecasts produced under the flat prior are made to those produced by the traditional (mixed estimation) “Bayesian” VAR methods of Doan, Litterman and Sims, as well as to fully Bayesian “Minnesota” prior forecasts. The actual record and, to a somewhat lesser extent, the record of the alternative procedures studied in pseudo-real-time forecasting experiments, share a characteristic: subsequently realized revenues are in the lower tails of the predicted distributions too often. An alternative empirically based prior is found by working directly on the probability distribution for the vector autoregression parameters–the goal being to discover a better-performing entropically tilted prior that minimizes out-of-sample mean squared error subject to a Kullback-Leibler divergence constraint that the new prior not differ too much from the original. We also study the closely related topic of robust prediction appropriate for situations of ambiguity. Robust “priors” are competitive in out-of-sample forecasting; despite the freedom afforded the entropically tilted prior, it does not perform better than the simple alternatives.
Using Policy Intervention to Identify Financial Stress with Mark A. Carlson and William Nelson
International Journal of Finance and Economics (2014) Vol. 19 (1), pp. 59-72
This paper describes the construction of a financial stress index (FSI). Our index incorporates the level, volatility and comovement of a variety of financial series, rather than a single dimension of the data. To determine which time periods are ones of notable financial stress and thus the relevant ones for determining the role of the level, volatility and comovement of our financial series, we use actions taken by policymakers. In addition to describing the construction of our FSI, we discuss issues relevant to the general construction of stress indexes such as how an FSI differs from a financial conditions index, the challenges of combining different financial series into a single measure and the role historical experience plays in index construction.
Distress in the Financial Sector and Economic Activity with Mark A. Carlson and Thomas King
The B.E. Journal of Economic Analysis & Policy (2011) Vol. 11 (1), Article 35
We construct daily market-based measures of distance to default for large U.S. financial institutions since 1973. These measures have significant predictive power for institution bankruptcy more than one year in advance. We aggregate the distances to default across institutions to provide an index of the overall health of the financial-services industry. We show that deteriorations in this Financial Institution Health Index are associated with tighter lending standards and higher interest rates on bank loans and precede declines in employment and industrial production. We argue that this points to the condition of financial institutions as an independent source of macroeconomic variability, distinct from traditional accelerator mechanisms.
The Two-Period Rational Inattention Model: Accelerations and Analyses
Computational Economics (2008) Vol. 33 (1), pp. 79-97
This paper demonstrates the properties of, and a solution method for, the more general two-period Rational Inattention model of Sims (2006). It is shown that the corresponding optimization problem is convex and can be solved very quickly. This paper also demonstrates a computational tool well-suited to solving Rational Inattention models and further illustrates a critique raised in Sims (2006) regarding Rational Inattention models whose solutions assume parametric formulations rather than solve for their optimally-derived, non-parametric counterparts.
Robustifying Shiller: Do Stock Prices Move Enough To Be Justified by Subsequent Changes in Dividends? with Charles H. Whiteman (2012)
We study the consequences of a minor change to the present value formula for stock prices. In place of the squared-error-loss minimizing-expected present value of dividends, we use a predictor optimal for the min-max preference relationship appropriate under ambiguity. With such “robust” predictions, the variance bound of Shiller (1981) and LeRoy-Porter (1981) is spectacularly reversed: prices are predicted to be far more volatile than what is observed in the data. We also investigate an intermediate case in which the degree of ambiguity is limited, and discover that the model cannot be rejected in favor of an unrestricted time series model.
This paper extends the rational inattention framework of Sims (2006) to a finite-horizon dynamic setting. This is accomplished by creating a structure in which an agent faced with information-processing constraints necessarily views states as distributions and makes approximate, distributional choices for controls. In the model, limited information processing capacity is used optimally, and agents have the opportunity to trade processing capacity for higher expected future income. The framework is applied to the canonical life-cycle model of consumption and saving, and an analysis is conducted of the impact of preference parameters on optimal attention allocation is conducted. The model produces a distinct hump-shaped profile in aggregate consumption.
Recession Risk and the Excess Bond Premium with Giovanni Favara, Simon Gilchrist and Egon Zakrajsek, April 2016
A number of recent papers have emphasized that credit spreads, in particular, may help predict economic activity for reasons unrelated to default risk. In this FEDS Note, we evaluate the information content for recession risk of a component of credit spreads that is not directly attributable to expected default risk and thus to news about future cash flows. Specifically, we use a measure of investor sentiment or risk appetite in the corporate bond market–the so-called excess bond premium (EBP) introduced by Gilchrist and Zakrajsek (2012)–to predict the likelihood of an NBER-dated recession occurring over the next 12 months.
See the October, 2016 Update for the regularly updated data series.
Prediction Formulas with Charles H. Whiteman, 2008
This article reviews the derivation of formulas for linear least squares and robust prediction of stationary time series and geometrically discounted distributed leads of such series. The derivations employed are the classical, frequency-domain procedures employed by Whittle (1983) and Whiteman (1983), and result in nearly closed-form expressions. The formulas themselves are useful directly in forecasting, and have also found uses in economic modelling, primarily in macroeconomics. Indeed, Hansen and Sargent (1980) refer to the cross-equation restrictions connecting the time series representation of driving variables to the analogous representation for predicting the present value of such variables as the ‘hallmark of rational expectations models.’