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

Agnieszka Jach, Piotr Kokoszka

Forskningsoutput: TidskriftsbidragArtikelVetenskapligPeer review

2 Citeringar (Scopus)

Sammanfattning

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.
OriginalspråkEngelska
Referentgranskad vetenskaplig tidskriftJournal of Statistical Computation and Simulation
Volym87
Utgåva8
Sidor (från-till)1498-1519
ISSN0094-9655
DOI
StatusPublicerad - 01.01.2017
MoE-publikationstypA1 Originalartikel i en vetenskaplig tidskrift

Nyckelord

  • 112 Statistik
  • 512 Företagsekonomi

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