This study assessed the impacts of sudden changes on long memory volatility, and then incorporated
those impacts into the multivariate FIGARCH-CCC model to elucidate long memory volatility
transmission between the Japanese and Korean stock markets. The principal objectives of this
study were twofold: First, to detect sudden changes in volatility using the iterated cumulative
sums of squared (ICSS) algorithms and evaluate the impact of sudden changes on volatility persistence
using a univariate FIGARCH model. In particular, we examined whether the inclusion of sudden
changes in the FIGARCH model reduces the degree of long memory. Second, this study took sudden
changes into account to more accurately analyze volatility transmission between Japanese and Korean
stock markets. Our findings indicate that ignoring sudden changes overestimates the extant degree
of volatility transmission between the conditional variances of these stock markets. We conclude
that ignoring the effects of sudden changes may cause misinterpretations in the degree of long
memory volatility transmission between stock markets. These findings have important implications
for building accurate asset price models, forecasting the volatility of stock returns, managing market
capitalization, and further understanding information transmission mechanisms.
Keywords：Sudden Changes, Long Memory Volatility, ICSS Algorithm, FIGARCH-CCC Model,
Information Transmission