Corporate Finance

In this paper, we propose a method of accounting for stock options that tracks the effect of the options on shareholder value. The accounting approach we outline can be applied not only to employee stock options but to all claims that are effectively convertible into common shares, including convertible preferred stock, warrants, and call and put options on the firm's own stock. Our proposal also aims to make accounting consistent with stock prices, since the market surely takes account of the (potential) valuation effects of these claims when setting stock prices.

This paper examines model specification issues and estimates diffusive and jump risk premia using S&P futures option prices from 1987 to 2003. We first develop a time series test to detect the presence of jumps in volatility, and find strong evidence in support of their presence. Next, using the cross section of option prices, we find strong evidence for jumps in prices and modest evidence for jumps in volatility based on model fit. The evidence points toward economically and statistically significant jump risk premia, which are important for understanding option returns.

I develop a model of group decision-making, in which a committee generates proposals and holds open discussions, but the ultimate decision is either taken by a leader (decision by authority) or by majority vote. Optimal communication processes are studied that combine both cheap talk statements (proposals) and costly state verification (discussions). I show that by favouring one particular agent—the leader—authoritative decisionmaking reduces rent-seeking discussions and often results in a higher decision-quality relative to majority decision-making.

This chapter provides an overview of modeling and computational issues associated with portfolio credit risk. We consider the problem of calculating the loss distribution in a portfolio of assets exposed to credit risk, such as corporate bonds or bank loans. We also discuss the pricing of portfolio credit derivatives, such as basket default swaps and collateralized debt obligations. A portfolio view of credit risk requires capturing dependence between the assets in the portfolio; we discuss models of dependence and associated computational techniques.

This paper proposes a simple back testing procedure that is shown to dramatically improve a panel data model's ability to produce out of sample forecasts. Here the procedure is used to forecast mutual fund alphas. Using monthly data with an OLS model it has been difficult to consistently predict which portfolio managers will produce above market returns for their investors. This paper provides empirical evidence that sorting on the estimated alphas populates the top and bottom deciles not with the best and worst funds, but with those having the greatest estimation error.

In this paper, we provide an exact particle filtering and parameter learning algorithm. Our approach exactly samples from a particle approximation to the joint posterior distribution of both parameters and latent states, thus avoiding the use of and the degeneracies inherent to sequential importance sampling. Exact particle filtering algorithms for pure state filtering are also provided. We illustrate the efficiency of our approach by sequentially learning parameters and filtering states in two models.

The conference paper by Johnson (2006, Review of Accounting Studies, forthcoming) develops an incomplete-contracting transfer pricing model with a number of novel features: taxation, sequential investments, and intangible assets being transferred. This discussion aims to disentangle these features so as to highlight those that are the key drivers of the results. Moreover, I show that some of the results can be generalized to settings involving a greater level of technological interdependency between the divisions.

Can one identify a "philosophy of regulation" that underlies the regulatory advocacy of the Fed under Chairman Greenspan? Although the Fed's advocacy on various matters may appear somewhat contradictory or, at least, philosophically heterodox, the Fed has behaved in a manner that is remarkably predictable, once one takes account of the political arena in which both regulatory and monetary policy are made. There is fairly straightforward logic to the Fed's regulatory advocacy.

When a boom ends, the downturn is generally sharp and short. When growth resumes, the boom is more gradual. Our explanation rests on learning about productivity. When agents believe productivity is high, they work, invest, and produce more. More production generates higher precision information. When the boom ends, precise estimates of the slowdown prompt decisive reactions: Investment and labor fall sharply. When growth resumes, low production yields noisy estimates of recovery. Noise impedes learning, slows recovery, and makes booms more gradual than downturns.