Investigation of the Relationship Between Chaos Data and €/$ Exchange Rate Index Data with RQA Method
A time series data contains a large amount of information in itself. Chaos data and volatility data which calculated by any time series are also derivative information included in the same time series. According to these assumptions, it is very important to question the ability of chaos and volatili...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Akif AKGUL
2023-07-01
|
| Series: | Chaos Theory and Applications |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/en/download/article-file/2989178 |
| _version_ | 1797295293335601152 |
|---|---|
| author | Hüseyin Serdar Yalçınkaya Nizamettin Başaran |
| author_facet | Hüseyin Serdar Yalçınkaya Nizamettin Başaran |
| author_sort | Hüseyin Serdar Yalçınkaya |
| collection | DOAJ |
| description | A time series data contains a large amount of information in itself. Chaos data and volatility data which calculated by any time series are also derivative information included in the same time series. According to these assumptions, it is very important to question the ability of chaos and volatility information to affect each other, and which information affects and which information is affected. It is very important to determine the causes of volatility, which is an important result indicator for the finance literature, and especially with this study, it was tried to determine whether the chaos data is in a causal relationship with volatility. If some of the chaos data can be identified as the cause of volatility, the detected chaos data can be used in other research as a leading indicator of volatility. The data set used in the study is the daily euro/dollar exchange rate index between 01.01.2005 and 10.11.2022. In the study, time series of chaos data were created with Windowed RQA method and Hatemi-J asymmetric causality analysis research was carried out between these time series and euro/dollar exchange rate index volatility. The findings of the study conclude that the chaos data LnRR, LnEntr and LnLAM could be used as leading indicators of the euro/dollar exchange rate index volatility. |
| first_indexed | 2024-03-07T21:45:38Z |
| format | Article |
| id | doaj.art-a8c37ab4623a4f90a614b664f1fc0172 |
| institution | Directory Open Access Journal |
| issn | 2687-4539 |
| language | English |
| last_indexed | 2024-03-07T21:45:38Z |
| publishDate | 2023-07-01 |
| publisher | Akif AKGUL |
| record_format | Article |
| series | Chaos Theory and Applications |
| spelling | doaj.art-a8c37ab4623a4f90a614b664f1fc01722024-02-25T19:10:00ZengAkif AKGULChaos Theory and Applications2687-45392023-07-0152788910.51537/chaos.12600491971Investigation of the Relationship Between Chaos Data and €/$ Exchange Rate Index Data with RQA MethodHüseyin Serdar Yalçınkaya0Nizamettin Başaran1NECMETTİN ERBAKAN ÜNİVERSİTESİNIGDE OMER HALISDEMIR UNIVERSITYA time series data contains a large amount of information in itself. Chaos data and volatility data which calculated by any time series are also derivative information included in the same time series. According to these assumptions, it is very important to question the ability of chaos and volatility information to affect each other, and which information affects and which information is affected. It is very important to determine the causes of volatility, which is an important result indicator for the finance literature, and especially with this study, it was tried to determine whether the chaos data is in a causal relationship with volatility. If some of the chaos data can be identified as the cause of volatility, the detected chaos data can be used in other research as a leading indicator of volatility. The data set used in the study is the daily euro/dollar exchange rate index between 01.01.2005 and 10.11.2022. In the study, time series of chaos data were created with Windowed RQA method and Hatemi-J asymmetric causality analysis research was carried out between these time series and euro/dollar exchange rate index volatility. The findings of the study conclude that the chaos data LnRR, LnEntr and LnLAM could be used as leading indicators of the euro/dollar exchange rate index volatility.https://dergipark.org.tr/en/download/article-file/2989178recurrence quantification analysischaos theory€/$ exchange rate indexvolatility |
| spellingShingle | Hüseyin Serdar Yalçınkaya Nizamettin Başaran Investigation of the Relationship Between Chaos Data and €/$ Exchange Rate Index Data with RQA Method Chaos Theory and Applications recurrence quantification analysis chaos theory €/$ exchange rate index volatility |
| title | Investigation of the Relationship Between Chaos Data and €/$ Exchange Rate Index Data with RQA Method |
| title_full | Investigation of the Relationship Between Chaos Data and €/$ Exchange Rate Index Data with RQA Method |
| title_fullStr | Investigation of the Relationship Between Chaos Data and €/$ Exchange Rate Index Data with RQA Method |
| title_full_unstemmed | Investigation of the Relationship Between Chaos Data and €/$ Exchange Rate Index Data with RQA Method |
| title_short | Investigation of the Relationship Between Chaos Data and €/$ Exchange Rate Index Data with RQA Method |
| title_sort | investigation of the relationship between chaos data and € exchange rate index data with rqa method |
| topic | recurrence quantification analysis chaos theory €/$ exchange rate index volatility |
| url | https://dergipark.org.tr/en/download/article-file/2989178 |
| work_keys_str_mv | AT huseyinserdaryalcınkaya investigationoftherelationshipbetweenchaosdataandexchangerateindexdatawithrqamethod AT nizamettinbasaran investigationoftherelationshipbetweenchaosdataandexchangerateindexdatawithrqamethod |