Structural Breakpoints in Volatility in International Markets
Abstract
In this article, we test for the presence of structural breaks in volatility by two
alternative approaches: the Iterative Cumulative Sum of Squares (ICSS) algorithm and
wavelet analysis. Specifically, we look at the effect of the outbreak of the Asian crisis and
the terrorist attacks of September 11, 2001 on Emerging Asia, Europe, Latin America and
North America’s stock markets. In addition, we focus on the behavior of interest rates in
Chile after the Central Bank switched its monetary policy interest rate from an inflation-
indexed to a nominal target in August 2001.
Our estimation results show that the number of shifts detected by the two methods is
substantially reduced when filtering out the data for both conditional heteroskedasticity and
serial correlation. In addition, we conclude that the wavelet-based test tends to be more
robust.
JEL: C22, G15 Keywords: ICSS algorithm, wavelet analysis, volatility breakpoints.
I Introduction
To date, there is an extensive literature on the behavior of volatility of assets returns.
Indeed, the GARCH model and numerous variations of it have been fitted to different
financial time series around the world to account for the existence of conditional
heteroskedasticity (see, for instance, the survey by Poon and Granger, 2003). However, less
attention has been paid to the detection of multiple shifts in unconditional variance over
time. For example, Lamoureux and Lastrapes (1990) conclude that persistence in variance
may be overstated by not accounting for deterministic structural breakpoints in the variance
model.
A relatively recent approach to testing for volatility shifts is Inclan and Tiao
(1994)’s Iterative Cumulative Sums of Squares (ICSS) algorithm. This algorithm allows for
detecting multiple breakpoints in variance in a time series. Aggarwal, Inclan and Leal
(1999) present an application of this procedure for emerging markets over 1985-1995. They