In this paper I present a data-intensive econometric smoothing method capable of fully exploiting the information content of intra-day option quotes. This is shown to be useful for both traditional option pricing applications as well as risk management. My method yields full sets of smoothed option prices with corresponding deltas and gammas for any “target time” of the day, while successfully addressing many common data problems, such as discontinuities in trading, the discreteness of option quotes and asynchronous trading. The procedures I outline here can be applied either in a real time setting or at the end of a trading day, so as to identify pricing errors or to estimate deltas and gammas for hedging purposes. In the process of smoothing the data, I also obtain reliable data-intensive estimates of the risk neutral density of the underlying asset at expiration. For the case of S&P 500 index options, these risk neutral density estimates are demonstrated to be superior to estimates suggested by the methods of Jackwerth and Rubinstein or Ait-Sahalia and Lo, and more importantly can reasonably be applied in practice.