LONG-RANGE DEPENDENCE IN THE CONDITIONAL VARIANCE OF STOCK RETURNS

We examine persistence in the conditional variance of U.S. stock retur ns indexes. Our results show evidence of long memory in high-frequency data, suggesting that models of conditional heteroskedasticity should be made flexible enough to accommodate these empirical findings.