Estimating Financial Volatility with High-Frequency Returns

Research Article

The primary value of a time series model lies in its ability to provide reliable approximations of the modelled variable, both in-sample (where data are used to estimate model parameters) and out-of-sample (where the model is updated with new information and produces forecasts). In this paper, an overview of the various models in the GARCH family is followed by their application in estimating the daily volatility of Citigroup Inc., a major player in the US. sub-prime mortgage crisis. Fitting these estimates to the ex-post realized volatility measure constructed from high-frequency returns provides superior goodness-of- t than tting them to the conventional absolute returns measure. This suggests that when modelling latent nancial volatility, information revealed by high-frequency data can greatly enhance GARCH estimates' performance.

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