Choosing between persistent and stationary volatility

Ilias Chronopoulos, Liudas Giraitis, George Kapetanios

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
37 Downloads (Pure)

Abstract

This paper suggests a multiplicative volatility model where volatility is decomposed into a stationary and a nonstationary persistent part. We provide a testing procedure to determine which type of volatility is prevalent in the data. The persistent part of volatility is associated with a nonstationary persistent process satisfying some smoothness and moment conditions. The stationary part is related to stationary conditional heteroskedasticity. We outline theory and conditions that allow the extraction of the persistent part from the data and enable standard conditional heteroskedasticity tests to detect stationary volatility after persistent volatility is taken into account. Monte Carlo results support the testing strategy in small samples. The empirical application of the theory supports the persistent volatility paradigm, suggesting that stationary conditional heteroskedasticity is considerably less pronounced than previously thought.

Original languageEnglish
Pages (from-to)3466-3483
Number of pages18
JournalANNALS OF STATISTICS
Volume50
Issue number6
DOIs
Publication statusPublished - 31 Dec 2022

Keywords

  • ARCH effect
  • nonparametric estimation
  • persistence
  • time-varying coefficient models
  • volatility

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