Forecasting-volatility models typically rely on either daily or high frequency (HF) data and the choice between these … two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer of many … limitations. HF data feature microstructure problem, such as the discreteness of the data, the properties of the trading mechanism …

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 …

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 …

idiosyncratic volatility is notoriously difficult and complex, especially in the presence of jumps, microstructure noise and … methodology is robust to jumps, microstructure noise and asynchronous observation times simultaneously. Analytical bias correction … volatility and realized R-Squared. Because the residual process is latent in the high frequency regression, the estimation of …