Maximum likelihood estimation of stochastic volatility models

We develop and implement a method for maximum likelihood estimation in closed-form of stochastic volatility models. Using Monte Carlo simulations, we compare a full likelihood procedure, where an option price is inverted into the unobservable volatility state, to an approximate likelihood procedure where the volatility state is replaced by proxies based on the implied volatility of a short-dated at-the-money option. The approximation results in a small loss of accuracy relative to the standard errors due to sampling noise. We apply this method to market prices of index options for several stochastic volatility models, and compare the characteristics of the estimated models. The evidence for a general CEV model, which nests both the affine Heston model and a GARCH model, suggests that the elasticity of variance of volatility lies between that assumed by the two nested models.     

The accounting and market consequences of accelerated share repurchases

We evaluate the representational faithfulness of the accounting treatment of a recent and well-established type of structured transaction—accelerated share repurchases (ASRs). ASRs are popular because accretive earnings per share benefits are recognized immediately, while any gains or losses on the forward contract used to execute an ASR bypass income, and are reported directly in equity. We document lower value relevance for the liabilities of ASR companies compared with a size- and industry-matched sample. ERC tests also indicate a market discount for the earnings of ASR companies compared with the control sample. Finally, we document significant abnormal returns to a trading strategy based on unrealized gains or losses on ASR transactions. Our results indicate that the current accounting for ASRs does not result in representative reporting of these transactions. As a result, financial statement users might benefit from recognition of ASR elements in financial statements.     

Estimating affine multifactor term structure models using closed-form likelihood expansions

We develop and implement a technique for closed-form maximum likelihood estimation (MLE) of multifactor affine yield models. We derive closed-form approximations to likelihoods for nine Dai and Singleton (2000) affine models. Simulations show our technique very accurately approximates true (but infeasible) MLE. Using US Treasury data, we estimate nine affine yield models with different market price of risk specifications. MLE allows non-nested model comparison using likelihood ratio tests; the preferred model depends on the market price of risk. Estimation with simulated and real data suggests our technique is much closer to true MLE than Euler and quasi-maximum likelihood (QML) methods.