noise is considered. A general stochastic volatility framework with jumps for the underlying asset dynamics is defined … parameter and average jumps size reveals that the characteristics of the dataset are crucial to determine which is the proper …

We develop a new method that detects jumps nonparametrically in financial time series and significantly outperforms the … generated by a process that experiences both jumps and volatility bursts. As a result, the network learns how to disentangle the …: we obtain fewer spurious detection and identify a larger number of true jumps. When applied to real data, our approach …

to overestimate the number of jumps in yield spreads and puts the coherence of test results at risk. We formalize the …

jumps over a grid of thresholds and selects the optimal threshold at what we term the “take-off” point in the estimated … number of jumps. We show that this method consistently estimates the jumps and their indices as the sampling interval goes to … jumps and its ability to distinguish between true jumps and large diffusive moves. In one of these Monte Carlo studies we …

of factor jumps. Such jump dependence is implied by standard linear factor models. Our inference is based on a panel of …

jumps over a grid of thresholds and selects the optimal threshold at what we term the ‘take-off’ point in the estimated … number of jumps. We show that this method consistently estimates the jumps and their indices as the sampling interval goes to … jumps and its ability to distinguish between true jumps and large diffusive moves. In one of these Monte Carlo studies we …