The relationship between news-based implied volatility and volatility of US stock market: What can we learn from multiscale perspective?
Bin Mo,
Jinqi Mu and
Bilin Zhang
Physica A: Statistical Mechanics and its Applications, 2019, vol. 526, issue C
Abstract:
This paper aims to employ the wavelet-based copula approach to empirically study the relationship between news-based implied volatility and the volatility of the US stock market using monthly data for the period January 1980 to March 2016. We find that the dependence structure is determined to be time-horizon dependent, in the short term, the correlation is very weak but quite strong in the long term. Furthermore, the asymmetric tail dependence structure can better explain the time-varying relationship in the long run shown by the time-varying SJC copula.
Keywords: News-based implied volatility; Volatility of US stock market; Wavelet; Copula (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (6)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437119306120
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:526:y:2019:i:c:s0378437119306120
DOI: 10.1016/j.physa.2019.04.239
Access Statistics for this article
Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis
More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().