Properties of the Sample Autocorrelations of Nonlinear Transformations in Long-Memory Stochastic Volatility Models

  1. Perez, A. 1
  2. Ruiz, E. 2
  1. 1 Universidad de Valladolid
    info

    Universidad de Valladolid

    Valladolid, España

    ROR https://ror.org/01fvbaw18

  2. 2 Universidad Carlos III de Madrid
    info

    Universidad Carlos III de Madrid

    Madrid, España

    ROR https://ror.org/03ths8210

Revista:
Journal of Financial Econometrics

ISSN: 1479-8409,  1479-8417

Año de publicación: 2003

Volumen: 1

Número: 3

Páginas: 420-444

Tipo: Artículo

DOI: 10.1093/JJFINEC/NBG017 GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Journal of Financial Econometrics

Objetivos de desarrollo sostenible

Resumen

The autocorrelations of log-squared, squared, and absolute financial returns are often used to infer the dynamic properties of the underlying volatility. This article shows that, in the context of long-memory stochastic volatility models, these autocorrelations are smaller than the autocorrelations of the log volatility and so is the rate of decay for squared and absolute returns. Furthermore, the corresponding sample autocorrelations could have severe negative biases, making the identification of conditional heteroscedasticity and long memory a difficult task. Finally, we show that the power of some popular tests for homoscedasticity is larger when they are applied to absolute returns.