Wavelet semi-parametric inference for long memory in volatility in the presence of a trend

Agnieszka Jach, Piotr Kokoszka

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)

Abstract

Risk of investing in a financial asset is quantified by functionals of squared returns. Discrete time stochastic volatility (SV) models impose a convenient and practically relevant time series dependence structure on the log-squared returns. Different long-term risk characteristics are postulated by short-memory SV and long-memory SV models. It is therefore important to test which of these two alternatives is suitable for a specific asset. Most standard tests are confounded by deterministic trends. This paper introduces a new, wavelet-based, test of the null hypothesis of short versus long memory in volatility which is robust to deterministic trends. In finite samples, the test performs better than currently available tests which are based on the Fourier transform.
Original languageEnglish
Peer-reviewed scientific journalJournal of Statistical Computation and Simulation
Volume87
Issue number8
Pages (from-to)1498-1519
ISSN0094-9655
DOIs
Publication statusPublished - 01.01.2017
MoE publication typeA1 Journal article - refereed

Keywords

  • 112 Statistics and probability
  • 512 Business and Management

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