We propose a Bayesian procedure for exploiting small, possibly long-lag linear predictability in the innovations of a finite order autoregression. We model the innovations as having a log-spectral density that is a continuous mean-zero Gaussian process of order 1/√T. This local embedding makes...

We study the nature of systemic sovereign credit risk using CDS spreads for the U.S. Treasury, individual U.S. states, and major European countries. Using a multifactor affine framework that allows for both systemic and sovereign-specific credit shocks, we find that there is considerable...

We study a competitive credit market in which lenders with partial knowledge of loan repayment use one of three decision criteria - maximization of expected utility, maximin, or minimax regret - to make lending decisions. Lenders allocate endowments between loans and a safe asset, while...

A growing literature shows that credit indicators forecast aggregate real outcomes. While researchers have proposed various explanations, the economic mechanism behind these results remains an open question. In this paper, we show that a simple, frictionless, model explains empirical findings...

What is the impact of granular credit risk on banks and on the economy? We provide the first causal identification of single-name counterparty exposure risk in bank portfolios by applying a new empirical approach on an administrative matched bank-firm dataset from Norway. Exploiting the fat tail...

We provide a first evaluation of the quality of banking stress tests in the European Union. We use stress tests scenarios and banks' estimated losses to recover bank level exposures to macroeconomic factors. Once macro outcomes are realized, we predict banks' losses and compare them to actual...

We study the effect of releasing public information about productivity or monetary shocks when agents learn from nominal prices. While public releases have the benefit of providing new information, they can have the cost of reducing the informational efficiency of the price system. We show that,...

This paper provides a general framework for integration of high-frequency intraday data into the measurement forecasting of daily and lower frequency volatility and return distributions. Most procedures for modeling and forecasting financial asset return volatilities, correlations, and...