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Can someone explain to me which of these two methods is more accurate or commonly used to calculate Realized Volatility? I'm seeing both used, but I get very different results from them.
1) Standard deviation of log returns x Sqrt of 252.
2) Sqrt of the Realized Variance based on the summed squares.
Thank you
In econometrics, if you have access to high-frequency (HF) data, then the realised variance approach works better than simply computing the standard deviation. The reason is that you use much more data and thus can utilise the additional information HF data carries, thus RV typically performs better than, say, GARCH models and a plain standard deviation. There are of course many extensions of realised variance which address biases (noise robust estimators) arising from market microstructure and take jumps into account. Furthermore, using heterogeneous autoregressive (HAR) models, you can use RV to forecast volatility.
