Corporate Finance

Sovereign Wealth Funds (SWFs) are state-owned investment funds with combined asset holdings that are fast approaching four trillion dollars. Recently emerging as a major force in global financial markets, SWFs have other distinctive features besides their state-owned status: they are mainly located in developing countries and are intimately tied to energy and commodities exports, and they carry virtually no liabilities and have little redemption risk, which allows them to take a longer-term investment outlook than most other institutional investors.

The 2007-2009 financial crisis that started in the summer of 2007 had its origins in the US housing policies, the subprime mortgage market in particular, and the end of the real estate bubble in the US. Careful consideration of the causes, consequences and policy responses suggest that various factors contributed to the severity of the 2007-08 crisis, and experts disagree about the weights to attach to each in explaining what is now regarded as the most significant economic contraction since the Great Depression.

Defaults arising from illiquidity can lead to private workouts, formal bankruptcy proceedings or even liquidation. All these outcomes can result in deadweight losses. Corporate illiquidity in the presence of realistic capital market frictions can be managed by a) equity dilution, b) carrying positive cash balances, or c) entering into loan commitments with a syndicate of lenders. An efficient way to manage illiquidity is to rely on mechanisms that transfer cash from "good states" into "bad states" (i.e., financial distress) without wasting liquidity in the process.

We propose a model of dynamic corporate investment, financing, and risk management for a financially constrained firm. The model highlights the central importance of the endogenous marginal value of liquidity (cash and credit line) for corporate decisions.

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.

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.