This paper provides explicit formulas for the first and second moments and the autocorrelation function of the number of jumps over a given interval for the multivariate Hawkes process. These computations are possible thanks to the affine property of this process. We unify the stock price models...

Various parametric volatility models for financial data have been developed to incorporate high-frequency realized volatilities and better capture market dynamics. However, because high-frequency trading data are not available during the close-to-open period, the volatility models often ignore...

This paper provides explicit formulas for the moments and the autocorrelation function of the number of jumps over a given interval for the Hawkes process. These computations are possible thanks to the affine property of this process. Using these quantities an implementation of the method of...

Novel model specifications that include a time-varying long run component in the dynamics of realized covariance matrices are proposed. The adopted modeling framework allows the secular component to enter the model structure either in an additive fashion or as a multiplicative factor, and to be...

The hedge fund represents a unique investment opportunity for the institutional and private investors in the diffusion-type financial systems. The main objective of this condensed article is to research the hedge fund’s optimal investment portfolio strategies selection in the global capital...

The financial econometrics literature includes several Multivariate GARCH models where the model parameter matrices depend on a clustering of financial assets. Those classes might be defined a priori or data-driven. When the latter approach is followed, one method for deriving asset groups is...

The paper investigates whether oil price shocks and oil price volatility provide predictive information for the state of the US stock market returns and volatility. The disaggregation of oil price shocks according to their origin allows us to assess whether they contain incremental forecasting...

Invertibility conditions for observation-driven time series models often fail to be guaranteed in empirical applications. As a result, the asymptotic theory of maximum likelihood and quasi-maximum likelihood estimators may be compromised. We derive considerably weaker conditions that can be used...

Modeling and forecasting of dynamically varying covariances have received much attention in the literature. The two most widely used conditional covariances and correlations models are BEKK and DCC. In this paper, we advance a new method to introduce targeting in both models to estimate matrices...

The paper investigates whether oil price shocks and oil price volatility provide predictive information for the state of the US stock market returns and volatility. The disaggregation of oil price shocks according to their origin allows us to assess whether they contain incremental forecasting...