Long memory versus structural breaks in modeling and forecasting realized volatility

Abstract

We explore the possibility of structural breaks in the realized volatility with the observed long-memory property for the daily Deutschemark/Dollar, Yen/Dollar and Yen/Deutschemark spot exchange rate realized volatility. The paper finds that the structural breaks can partly explain the persistence of realized volatility. We propose a VAR-RV-Break model that provides a superior predictive ability compared to most of the forecasting models when the future break is known. With unknown break dates and sizes, we find that the VAR-RV-I(d) long memory model, however, is a very robust forecasting method even when the true financial volatility series are generated by structural breaks.JEL classification: C32, C52, C53, G10

It's all about volatility of volatility: Evidence from a two-factor stochastic volatility model

“…However, as noted by Maheu and McCurdy (2002) and McAleer and Medeiros (2008), the dynamic pattern of RV is subject to structural breaks and could potentially vary over time. This evidence is also confirmed by Liu and Maheu (2008), Choi et al (2010) and Bordignon and Raggi (2012) who find that structural breaks in the mean are partly responsible for the persistence of RV.…”

Long Memory Property and Structural Breaks in Volatility: Evidence from Turkey and Brazil

“…In this model, is the logarithmic realized volatility at time ; and is the mean of the logarithmic realized volatility. As explained by Choi et al (2010) may be serial correlated and heteroskedastic. The break points ( 1, … , ) are explicitly treated as unknown.…”

“…The sequential method of Bai-Perron multiple structural breaks analysis indicated 3 and 4 breaks for the BIST100 and the BOVESPA indexes, respectively. By considering this information we adjusted both index returns following the method of Choi et al (2010) and performed the previous tests once again. According to the final results, conditional variance of both indexes have long memory property, that is, both of index volatilities are foreseeable under the past price information.…”

Modeling High Frequency Data with Long Memory and Structural Change: A-HYEGARCH Model

“…Substantially lower estimates of the long memory parameter d are observed. Choi et al (2010) compared estimates of d in HYGARCH models before and after adjusting for structure breaks. They found that, when structure breaks are present, d tends to be overestimated.…”