This paper investigates long-run dependencies of volatility and volume, supposing that are driven by the same informative process. Log-realized volatility and log-volume are characterized by upper and lower tail dependence, where the positive tail dependence is mainly due to the jump component. The possibility that volume and volatility are driven by a common fractionally integrated stochastic trend, as the Mixture Distribution Hypothesis prescribes, is rejected. We model the two series with a bivariate Fractionally Integrated VAR specification. The joint density is parameterized by means of with different copula functions, which provide flexibility in modeling the dependence in the extremes and are computationally convenient. Finally, we present a simulation exercise to validate the model.
Long Memory and Tail Dependence in Trading Volume and Volatility
ROSSI, EDUARDO;
2009-01-01
Abstract
This paper investigates long-run dependencies of volatility and volume, supposing that are driven by the same informative process. Log-realized volatility and log-volume are characterized by upper and lower tail dependence, where the positive tail dependence is mainly due to the jump component. The possibility that volume and volatility are driven by a common fractionally integrated stochastic trend, as the Mixture Distribution Hypothesis prescribes, is rejected. We model the two series with a bivariate Fractionally Integrated VAR specification. The joint density is parameterized by means of with different copula functions, which provide flexibility in modeling the dependence in the extremes and are computationally convenient. Finally, we present a simulation exercise to validate the model.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
