Detail publikačního výsledku

Optimized Segmentation-Adaptive-Based Testing of the Wavelet Co-movement Analysis: the Case of US and G8 Countries

POMĚNKOVÁ, J.; KLEJMOVÁ, E.; MALACH, T.

Originální název

Optimized Segmentation-Adaptive-Based Testing of the Wavelet Co-movement Analysis: the Case of US and G8 Countries

Anglický název

Optimized Segmentation-Adaptive-Based Testing of the Wavelet Co-movement Analysis: the Case of US and G8 Countries

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

The paper deals with an identification and the description of the co-movement between the US and G8 countries with regard to the impact of the structural change, i.e. the financial crisis in 2008. For an identification of the co-movement we use optimized segmentation-adaptive-based approach (SAB) of significance testing of the power wavelet cross-spectrum. The SAB testing is based on the standard testing for the power wavelet cross-spectrum adapted for the case, when the data have several levels of volatility during the time evolution, i.e. the data can be split into several segments with different volatility. The number of segments is set with the help of heterosdasticity test and the test for comparison of variances in the segments of the time series. The SAB testing allows us an identification of significant co-movement with respect to the local variance, which can reveal additional significant co-movement areas. For the application we use monthly data of industrial production index for G8 countries in 1993–2017.

Anglický abstrakt

The paper deals with an identification and the description of the co-movement between the US and G8 countries with regard to the impact of the structural change, i.e. the financial crisis in 2008. For an identification of the co-movement we use optimized segmentation-adaptive-based approach (SAB) of significance testing of the power wavelet cross-spectrum. The SAB testing is based on the standard testing for the power wavelet cross-spectrum adapted for the case, when the data have several levels of volatility during the time evolution, i.e. the data can be split into several segments with different volatility. The number of segments is set with the help of heterosdasticity test and the test for comparison of variances in the segments of the time series. The SAB testing allows us an identification of significant co-movement with respect to the local variance, which can reveal additional significant co-movement areas. For the application we use monthly data of industrial production index for G8 countries in 1993–2017.

Klíčová slova

wavelets, comovement, significance testing

Klíčová slova v angličtině

wavelets, comovement, significance testing

Autoři

POMĚNKOVÁ, J.; KLEJMOVÁ, E.; MALACH, T.

Rok RIV

2019

Vydáno

01.02.2019

Místo

Roma, Italy

Kniha

ITM Web of Conferences

Edice

The 2018 International Conference Applied Mathematics, Computational Science and Systems Engineering

ISSN

2271-2097

Periodikum

ITM Web of Conferences

Svazek

16

Stát

Francouzská republika

Strany od

1

Strany do

7

Strany počet

7

URL

https://www.itm-conferences.org/articles/itmconf/abs/2019/01/itmconf_amcse18_01003/itmconf_amcse18_01003.html

BibTex

@inproceedings{BUT150978,
  author="Jitka {Dluhá} and Eva {Klejmová} and Tobiáš {Malach}",
  title="Optimized Segmentation-Adaptive-Based Testing of the Wavelet Co-movement Analysis: the Case of US and G8 Countries",
  booktitle="ITM Web of Conferences",
  year="2019",
  series="The 2018 International Conference Applied Mathematics, Computational Science and Systems Engineering",
  journal="ITM Web of Conferences",
  volume="16",
  number="17",
  pages="1--7",
  address="Roma, Italy",
  doi="10.1051/itmconf/20192401003",
  issn="2271-2097",
  url="https://www.itm-conferences.org/articles/itmconf/abs/2019/01/itmconf_amcse18_01003/itmconf_amcse18_01003.html"
}