Corporate Finance

Particle learning provides a simulation-based approach to sequential Bayesian computation. To sample from a posterior distribution of interest we use an essential state vector together with a predictive and propagation rule to build a resampling-sampling framework. Predictive inference and sequential Bayes factors are a direct by-product. Our approach provides a simple yet powerful framework for the construction of sequential posterior sampling strategies for a variety of commonly used models.

Leading up to the recent crisis, government encouraged risky lending, and failed to measure banks' risks credibly or to require sufficient capital. Regulators also failed to losses or enforce intervention protocols for timely resolution. This paper proposes radical policy changes to prevent a recurrence. The need is not for more complex rules and more supervisory discretion, but rather for simpler rules that are meaningful in measuring and limiting risk, hard for market participants to circumvent and credibly enforced by supervisors.

What discount rate should the government use to measure the financial status of various government programs and the impact of potential policy changes? How, if at all, should the discount rate take into account the risk of the underlying cash flows? We examine these questions in the context of one of the largest components of the U.S. budget: Social Security. Official measures of the U.S. Social Security system's present value funding shortfall are computed using the risk-free interest rate.

Contingent capital, a regulatory debt that must convert into common equity when a bank's equity value falls below a specified threshold (a trigger), does not in general lead to a unique equilibrium in the prices of the bank's equity and contingent capital. Multiplicity or absence of equilibrium arises because economic agents are not allowed to choose a conversion policy in their best interests. The lack of unique equilibrium introduces the potential for price manipulation, market uncertainty, inefficient capital allocation, and unreliability of conversion.

We analyze the computational problem of estimating financial risk in a nested simulation. In this approach, an outer simulation is used to generate financial scenarios and an inner simulation is used to estimate future portfolio values in each scenario. We focus on one risk measure, the probability of a large loss, and we propose a new algorithm to estimate this risk. Our algorithm sequentially allocates computational effort in the inner simulation based on marginal changes in the risk estimator in each scenario.

Asset-backed securitization transforms assets into securities and in the process separates the credit risk of a pool of assets from the credit risk of the securitization's sponsor, which gives securitization several important advantages over issuing corporate debt. Recent research, however, suggests a link between the financial condition of the sponsor and the performance of its ABS.

We characterize the optimal mortgage contract in a continuous time setting with stochastic growth in house price and income, costly foreclosure, and a risky borrower who requires incentives to repay his debt. We show that many features of subprime loans can be consistent with properties of the optimal contract and that, when house prices decline, mortgage modification can create value for borrowers and lenders.

Beginning with Shimer (2005) and Hall(2005), a recent branch of the business cycle literature has explored the role of wage rigidity in accounting for the statistical characteristics of key labor market variables; in particular high vacancy and unemployment volatility and a high negative correlation between the two. As a further exploration, we extend the Mortensen-Pissarides structure of period-by-period Nash wage bargaining to an environment where there is labor force heterogeneity (permanently employed "insiders" and "outsiders" subject to separations) and limited asset market parti