Modeling and forecasting realized volatility is of paramount importance. Previous studies have examined the role of both the continuous and jump components of volatility in forecasting. This paper considers how to use index level jumps and cojumps across index constituents for forecasting index...

This paper investigates the dynamic behaviour of jumps in financial prices and volatility. The proposed model is based on a standard jump diffusion process for price and volatility augmented by a bivariate Hawkes process for the two jump components. The latter process speci.es a joint dynamic...

Numerous incidents in the financial world have exposed the need for the design and analysis of models for correlated default timings. Some models have been studied in this regard which can capture the feedback in case of a major credit event. We extend the research in the same direction by...

A continuous time econometric modelling framework for multivariate financial market event (or `transactions`) data is developed in which the model is specified via the vector stochastic intensity. This has the advantage that the conditioning sigma-field is updated continuously in time as new...

Modeling and forecasting realized volatility is of paramount importance. Recent econometric developments allow total volatility to be decomposed into its' constituent continuous and jump components. While previous studies have examined the role of both components in forecasting, little analysis...

In this paper, we study a bivariate shot noise self-exciting process. This process includes both externally excited joint jumps, which are distributed according to a shot noise Cox process, and two separate self-excited jumps, which are distributed according to the branching structure of a...

In this note, we cast a Hawkes process-based order book model into a markovian setting and; using techniques from the theory of Markov chains and stochastic stability, show that the order book is stable and leads to a diffusive price limit at large time scales.

Dynamic jumps in the price and volatility of an asset are modelled using a joint Hawkes process in conjunction with a bivariate jump diffusion. A state space representation is used to link observed returns, plus nonparametric measures of integrated volatility and price jumps, to the specified...

We propose a modeling framework which allows for creating probability predictions on a future market crash in the medium term, like sometime in the next five days. Our framework draws upon noticeable similarities between stock returns around a financial market crash and seismic activity around...