Errors in Implied Volatility Estimation
当期权特征存在合理误差时，通过Black-Scholes公式反推隐含波动率会产生较大误差，尤其对于价外期权。论文提出了可行的广义最小二乘估计量来降低噪声和偏差。
Estimating implied volatility by inverting the Black-Scholes formula is subject to considerable error when option characteristics are observed with plausible errors. Especially for options away from the money, large changes in volatility produce small changes in option prices. Conversely, small errors in option prices and other option characteristics produce large errors in implied volatilities. In the presence of small measurement errors, unobserved truncation of option prices that violate lower bounds for absence of arbitrage can also lead to systematic volatility smiles. The paper proposes feasible GLS estimators that reduce the noise and bias in implied volatility estimates.