Nonparametric Test for Volatility in Clustered Multiple Time Series

Erniel Barrios () and Paolo Victor T. Redondo
Additional contact information
Paolo Victor T. Redondo: King Abdullah University of Science and Technology

Computational Economics, 2024, vol. 63, issue 2, No 14, 876 pages

Abstract: Abstract Contagion arising from clustering of multiple time series like those in the stock market indicators can further complicate the nature of volatility, rendering a parametric test (relying on asymptotic distribution) to suffer from issues on size and power. We propose a test on volatility based on the bootstrap method for multiple time series, intended to account for possible presence of contagion effect. While the test is fairly robust to distributional assumptions, it depends on the nature of volatility. The test is correctly sized even in cases where the time series are almost nonstationary (i.e., autocorrelation coefficient $$\approx 1$$ ≈ 1 ). The test is also powerful specially when the time series are stationary in mean and that volatility are contained only in fewer clusters. We illustrate the method in global stock prices data.

Keywords: Multiple time series; Volatility; Clustering; Nonparametric test; Sieve bootstrap (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10614-023-10362-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
Working Paper: Nonparametric Test for Volatility in Clustered Multiple Time Series (2024)
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:kap:compec:v:63:y:2024:i:2:d:10.1007_s10614-023-10362-x

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-023-10362-x

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().