Footnote:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments November 1, 2021 erstellt
Description:
We examine the clustering behaviour of price and variance jumps using high-frequency data, modelled as a marked Hawkes process embedded in a bivariate jump-diffusion model. After de-periodisation of the intraday data, we find that the jumps of both individual stocks and a broad index exhibit self-exciting behaviour. The three dimensions of the model, namely positive price jumps, negative price jumps and variance jumps, impact one another in an asymmetric manner, that is positively and significantly correlated with jump size. We estimate model parameters using Bayesian inference by Markov Chain Monte Carlo, and find that the inclusion of the jump parameters improves model fitness. We also study number of jumps in clusters, we find under high-frequency settings, there are maximum over twenty return jumps in a cluster which cover more than three trading days, we also find that marked Hawkes process models mostly outperform others in terms of reproducing two cluster-related characteristics