Change Point Estimation in Volatility of a Time series using a Kolmogorov Smirnov Type Test Statistic
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Date
2019-04Author
Ngure, Josephine Njeri
Waititu, Anthony Gichuhi
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Detection of structural change in volatility of a time series is very important for understanding
volatility dynamics and the stylized facts observed in financial time series. By applying the
Nadaraya Watson kernel estimator of the mean function, estimated residuals are obtained. In this
work, a Kolmogorov Smirnov type test statistic for change point estimation is developed and
applied to conditional variances obtained from the squared residuals. The consistency of the
change point estimator is shown through simulations. The developed estimator is then applied to
KES/USD exchange rate data set to estimate a single change point