Understanding strikes in CES estimates

Understanding pooled subjective probability estimates

Decision makers often must pool probability estimates from multiple experts before making a choice. Such pooling sometimes improves accuracy and other times diagnosticity. This article uses a cognitive model of the judge and the decision maker’s classification of the information to explain why. Given a very weak model of the judge, the important factor is the degree to which their information b...

Understanding Covariance Estimates in Expectation Propagation

Bayesian inference for most models of modern interest requires approximation of the posterior distribution. Variational inference (VI) methods formulate posterior approximation as a particular optimization problem and have grown in popularity due to their fast runtime on large datasets. There is recent work that suggests it might be beneficial to instead consider an alternative optimization obj...

Understanding Estimates of AI Sire Fertility

Asymptotic Formulas with Error Estimates for Call Pricing Functions and the Implied Volatility at Extreme Strikes

In this paper, we obtain asymptotic formulas with error estimates for the implied volatility associated with a European call pricing function. We show that these formulas imply Lee’s moment formulas for the implied volatility and the tail-wing formulas due to Benaim and Friz. In addition, we analyze Pareto-type tails of stock price distributions in uncorrelated Hull-White, Stein-Stein, and Hest...

CES Working Paper 2-05