Data-based ranking of realised volatility estimators

94 citations recorded by the source corpus.

How researchers see this paper

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Good Volatility, Bad Volatility: Signed Jumps and The Persistence of Volatility (2015, background): “This “QLIKE” loss function has been shown to be robust to noise in the proxy for volatility in Patton (2011) and to have good power properties in Patton and Sheppard (2009).”
Does Anything Beat 5-Minute RV? A Comparison of Realized Measures Across Multiple Asset Classes (2015, unknown): “Consistent with the simulation results in Patton (2011a) and the results under MSE, we find similar results to our base case but with lower power.”
Exploiting the errors: A simple approach for improved volatility forecasting (2016, methodology): “(16)Liu, Patton, and Sheppard (2015) provide a recent discussion and empirical justification for this common choice.”
Optimal combinations of realised volatility estimators (2009, methodology): “The ranking method of Patton (2008) provides a means of consistently estimating the difference in average accuracy of two competing estimators.”
Selecting volatility forecasting models for portfolio allocation purposes (2015, methodology): “They are also consistent with the univariate results of Patton (2011).”