Stock market indices from several countries are modelled as discretely sampled diffusions whose parameters change at certain times. To estimate these times of parameter changes we employ both a sequential likelihood‐ratio test and a non‐parametric, spectral algorithm designed specifically for time series with multiple changepoints. Finally, we use point‐process techniques to model relationships between changepoints of different financial time series.
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Lenardon et al. (2006) studied this question.
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