Abstract
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.
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