Estimating Financial Volatility with High-Frequency Returns

The primary value of a time series model lies in its ability to provide reliableapproximations of the modelled variable, both in-sample (where data are used to estimate modelparameters) and out-of-sample (where the model is updated with new information and producesforecasts). In this paper, an overview of the various models in the GARCH family is followed bytheir application in estimating the daily volatility of Citigroup Inc., a major player in the US subprime mortgage crisis. Fitting these estimates to the ex-post realised volatility measure constructedfrom high-frequency returns provides superior goodness-of-fit than fitting them to the conventionalabsolute returns measure. This suggests that when modelling latent financial volatility, informationrevealed by high-frequency data can greatly enhance GARCH estimates’ performance.