Volatility Analysis of Exchange Rate with Correlated Errors: A Sliding Data Matrix Approach
No Thumbnail Available
Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
Hindawi
Abstract
The main objective of this study is to propose a method of analysing the volatility of a seemingly random walk time series with
correlated errors without transforming the series as performed traditionally. The proposed method involves the computation of
moving volatilities based on sliding and cumulative data matrices. Our method rests on the assumption that the number of
subperiods for which the series is available is the same for all periods and on the assumption that the series observations in
each subperiod for all the periods under consideration are a random sample from a particular distribution. The method was
successfully implemented on a simulated dataset. A paired sample t-test, Wilcoxon signed rank test, repeated measures
(ANOVA), and Friedman tests were used to compare the volatilities of the traditional method and the proposed method under
both sliding and cumulative data matrices. It was found that the differences among the average volatilities of the traditional
method and sliding and cumulative matrix methods were insignificant for the simulated series that follow the random walk
theorem. The implementation of the method on exchange rates for Canada, China, South Africa, and Switzerland resulted in
adjudging South Africa to have the highest fluctuating exchange rates and hence the most unstable economy.
Description
Research Article