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The effect of periods of nontrading on volatility is examined. The empirical evidence suggests that volatility is higher on days which follow a period of nontrading. A nonparametric kernel regression is used to estimate a diffusion model with a volatility term dependent on the number of days of prior nontrading. The nonparametric estimates suggest that the presence of a prior period of nontrading may increase the volatility as much as 35%. A moving blocks bootstrap, taking into account the dependence in observations, is used in conjunction with the nonparametric regression to show that the differences estimated are statistically significant.
