On Heteroskedasticity and Regimes in Volatility Forecasting
22 Pages Posted: 19 Sep 2017
Date Written: September 14, 2017
In this paper we discuss some deep implications of the recent paper by Bollerslev et al. (2016) (BPQ). In BPQ the volatility dynamics modeled as a HAR is augmented by a term involving quarticity in order to correct measurement errors in realized variance. We show that the model is observationally equivalent to another where a quadratic term in realized variance accounts for a faster mean reversion when volatility is high. We argue that heteroskedasticity (volatility of volatility) and a time-varying mean seem to play a role of higher order of importance than measurement errors. In fact, the quarticity/quadratic terms disappear within an AMEM, and more so when a Markov Switching dynamics is considered. Some simulation results show that when the DGP is a (MS-)AMEM, such terms turn out spuriously significant in a HAR. Forecast performance of the (MS-)AMEM is superior to the augmented HARs.
Keywords: Realized Volatility, Forecasting, Measurement Errors, HAR, AMEM, Markov Switching, Volatility of Volatility
JEL Classification: C22, C51, C53, C58
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