Testing for Extreme Volatility Transmission with Realized Volatility Measures
Gilles de Truchis (),
Elena Ivona Dumitrescu and
Sessi Tokpavi ()
No 2017-20, EconomiX Working Papers from University of Paris West - Nanterre la Défense, EconomiX
This paper proposes a simple and parsimonious semi-parametric testing procedure for variance transmission. Our test focuses on conditional extreme values of the unobserved process of integrated variance since they are of utmost concern for policy makers due to their sudden and destabilizing effects. The test statistic is based on realized measures of variance and has a convenient asymptotic chi-square distribution under the null hypothesis of no Granger causality, which is free of estimation risk. Extensive Monte Carlo simulations show that the test has good small sample size and power properties. An extension to the case of spillovers in quadratic variation is also developed. An empirical application on extreme variance transmission from US to EU equity markets is further proposed. We find that the test performs very well in identifying periods of significant causality in extreme variance, that are subsequently found to be correlated with changes in US monetary policy.
Keywords: Extreme volatility transmission; Granger causality; Integrated variance; Realized variance; Semi-parametric test; Financial contagion. (search for similar items in EconPapers)
JEL-codes: C12 C32 C58 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets and nep-ore
References: Add references at CitEc
Citations Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: http://EconPapers.repec.org/RePEc:drm:wpaper:2017-20
Access Statistics for this paper
More papers in EconomiX Working Papers from University of Paris West - Nanterre la Défense, EconomiX Contact information at EDIRC.
Series data maintained by Valérie Mignon ().