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 …

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 …

assumptions of jumps in prices and leverage effects for volatility. Findings suggest that daily-data models are preferred to HF …-data models at 5% and 1% VaR level. Specifically, independently from the data frequency, allowing for jumps in price (or providing …