Dynamic Dispersed Information and the Credit Spread Puzzle

Abstract

We develop a dynamic nonlinear, noisy REE model of credit risk pricing under dispersed information that can theoretically and quantitatively account for the credit spread puzzle. The first contribution is a sharp analytical characterization of the dynamic REE equilibrium and its comparative statics. Second, we show that the nonlinearity of the bond payoff in the environment with dispersed information and limits to arbitrage leads to underpricing of corporate debt and to spreads that over-state the probability of default. This underpricing is most pronounced for high investment grade, short maturity bonds. Third, we calibrate to the empirical data on the belief dispersion and show that the model generates spreads that explain between 16 to 42% of the empirical values for 4-year high investment grade, and 35 to 46% for 10-year, high investment grade bonds. These magnitudes are in line with empirical estimates linking bond spreads to empirical measures of investor disagreement, and substantially higher than most structural models of credit risk. The primary contribution of our paper in moving NREE models towards a more realistic asset pricing environment -dynamic, nonlinear, and quantitative -that holds significant promise for explaining empirical asset pricing puzzles.

Measuring Financial Fragmentation in the Euro Area Corporate Bond Market

“…Studies using either reduced form models or calibrated models obtain that default probabilities explain only one third of the variations in credit spreads for investment grade bonds (Collin-Dufresn et al 2001;Duffie et al 2007;Giesecke et al 2011;Huang and Huang 2012). The remaining part of the spread can include tax asymmetries (Elton et al 2001) or dispersed information (Albagli et al 2014). Most of the literature focuses however on credit risk premia and liquidity premia.…”

Empirical Support for Fundamental, Factor Models Explaining Major Capital Markets Using Wavelets

“…The set of explanatory variables is enriched by other researchers to also account for market inefficiencies. For example, it can be assumed that there are limits to arbitrage which combined with noise leads to predictable deviations of market prices from the asset's fundamental value [19]. A solution could be a dynamic model with dispersed information in which noisy investors only learn about fundamental information with a time delay in order to solve the puzzle.…”

“…For investment grade bonds however they find that the proportion explained is much lower. Inefficiencies can lead to a higher proportion being explained by the models, for example see [19,21]. Again we want to analyze the data at different time horizons and simultaneously allow for inefficiencies such as delayed learning about relevant information or other forms of feedback, or technical trading and account for different investment horizons of market participants.…”

Institutional Investors and Information Acquisition: Implications for Asset Prices and Informational Efficiency

“…Barlevy and Veronesi (2000) andAlbagli, Hellwig, and Tsyvinski (2014) study risk-neutral investors,Peress (2003) studies general preferences using a (small-risk) log-linearization, van Nieuwerburgh and Veldkamp (2010) study a general form of utility function, and Breon-Drish (2015) as well asChabakauri, Yuan, and Zachariadis (2016) focus on distributions that are members of the "exponential family. "…”