  We investigate scaling and memory effects in return intervals between price
volatilities above a certain threshold $q$ for the Japanese stock market using
daily and intraday data sets. We find that the distribution of return intervals
can be approximated by a scaling function that depends only on the ratio
between the return interval $\tau$ and its mean $<\tau>$. We also find memory
effects such that a large (or small) return interval follows a large (or small)
interval by investigating the conditional distribution and mean return
interval. The results are similar to previous studies of other markets and
indicate that similar statistical features appear in different financial
markets. We also compare our results between the period before and after the
big crash at the end of 1989. We find that scaling and memory effects of the
return intervals show similar features although the statistical properties of
the returns are different.
