Adjusting Short-Horizon Realized Volatility for Microstructure Noise

Summary

The document asks how to estimate realized volatility over a short interval from second-level price data, where market microstructure noise may distort high-frequency returns. It raises whether noise-reduction techniques commonly used for daily realized volatility, such as subsampling and realized kernels, remain useful when the target window is much shorter.

The response suggests two alternatives: fit an MA(1) model to intraday returns and aggregate squared residuals, or use a first-order-autocorrelation-adjusted realized volatility measure. These approaches aim to address serial dependence associated with microstructure effects. The excerpt does not provide implementation details, comparative results, or evidence that either measure performs well over the requested short window. The cited discussion of daily aggregation may not transfer directly to a short interval, so the sampling design and noise behavior would need to be assessed for the specific data.

Key ideas

• Second-level returns can contain microstructure noise that biases realized volatility estimates.
• Subsampling and realized kernels are mentioned as noise-aware approaches from the broader realized-volatility literature.
• An MA(1) adjustment can model serial correlation, with squared residuals aggregated as a volatility measure.
• A first-order-autocorrelation-adjusted realized volatility estimator is offered as another candidate.
• The excerpt provides no comparison or validation for applying these methods to a short measurement window.

Tags

# Realized Vol for 15 min interval using second Data


# Realized Vol for 15 min interval using second Data












I would like to calculate realized volatility for a 15 min period. Most of the literature I looked up shows how to construct daily realized volatility using intraday data. These literatures does use several techniques (e.g. subsampling, using realised kernel...) to account for microstructure noise. I'm not sure if using these techniques for the 15 min period would help to reduce the microstructure noise since I use seconds data. Do you have any suggestions how to calculate realized volatility for a 15 min interval?

## Answer by Greconomist (score 2)

https://quant.stackexchange.com/a/27618

I can think two techniques that may possible be of help. The first technique is the moving average adjusted returns originally proposed by Andersen et. al(2001). See Hansen et al. (2008) for details. In order to account for serial correlation an MA(1) is fitted to to the intraday returns data, the residual of which is then squared and aggregated over the trading day to produce the daily measure of realised volatility. Besides, is the Hansen and Lunde first order autocorrelation realized volatility measure on the paper Realized Variance and Microstructure Noise.

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