This paper is concerned with the estimation of the time-varying jump intensity of the Borsa Istanbul 100 (BIST 100) index. In the estimation phase, we utilize a new two-step method. In the first step, we employ wavelet filters to compute the number of jumps as a counting process. Next, we apply an integer-valued generalized autoregressive conditional heteroscedasticity model to examine the deterministic and stochastic components of the jump dynamics. Our results indicate not only deterministic diurnal patterns but also an autoregressive mechanism in BIST 100 jump dynamics.