Modelling stock volatilities during financial crises: a time varying coefficient approach

Menelaos Karanasos*, Alexandros G. Paraskevopoulos, Faek Menla Ali, Michail Karoglou, Stavroula Yfanti

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

We examine how the most prevalent stochastic properties of key financial time series have been affected during the recent financial crises. In particular we focus on changes associated with the remarkable economic events of the last two decades in the volatility dynamics, including the underlying volatility persistence and volatility spillover structure. Using daily data from several key stock market indices, the results of our bivariate GARCH models show the existence of time varying correlations as well as time varying shock and volatility spillovers between the returns of FTSE and DAX, and those of NIKKEI and Hang Seng, which became more prominent during the recent financial crisis. Our theoretical considerations on the time varying model which provides the platform upon which we integrate our multifaceted empirical approaches are also of independent interest. In particular, we provide the general solution for time varying asymmetric GARCH specifications, which is a long standing research topic. This enables us to characterize these models by deriving, first, their multistep ahead predictors, second, the first two time varying unconditional moments, and third, their covariance structure.

Original languageEnglish
Pages (from-to)113-128
Number of pages16
JournalJournal of Empirical Finance
Volume29
Early online date28 Aug 2014
DOIs
Publication statusPublished - Dec 2014

Bibliographical note

© 2014 Published by Elsevier B.V. Creative Commons Attribution 3.0 Unported (CC BY 3.0)

Keywords

  • financial crisis
  • structural breaks
  • time varying GARCH models
  • volatility spillovers

Fingerprint

Dive into the research topics of 'Modelling stock volatilities during financial crises: a time varying coefficient approach'. Together they form a unique fingerprint.

Cite this