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Managing Option Fragilitywith Brian J. Hall: w9059 We analyze and explore option fragility, the notion that option incentives are fragile due to their non-linear payoff structure. Option incentives become weaker as options fall underwater, leading to pressures to reprice options or restore incentives through additional grants of equity-based pay. We build a detailed data set on executives' portfolios of stock and options and find that executive options are frequently underwater, even when average stock returns have been high. For example, at the height of the bull market in 1999, approximately one-third of all executive options were underwater. We find that, in contrast to the incentives provided by stock, the incentives provided by options are quite sensitive to stock price changes, especially on the downside. Overall, we find that th... | |

Empirical Bayes Forecasts of One Time Series Using Many Predictorswith James H. Stock, Mark W. Watson: t0269 We consider both frequentist and empirical Bayes forecasts of a single time series using a linear model with T observations and K orthonormal predictors. The frequentist formulation considers estimators that are equivariant under permutations (reorderings) of the regressors. The empirical Bayes formulation (both parametric and nonparametric) treats the coefficients as i.i.d. and estimates their prior. Asymptotically, when K is proportional to T the empirical Bayes estimator is shown to be: (i) optimal in Robbins' (1955, 1964) sense; (ii) the minimum risk equivariant estimator; and (iii) minimax in both the frequentist and Bayesian problems over a class of nonGaussian error distributions. Also, the asymptotic frequentist risk of the minimum risk equivariant estimator is shown to equal the B... |

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