Using unobservable conditional variance as measure, latentvariable approaches, such as GARCH and stochasticvolatility models, have traditionally been dominating the empirical finance literature. In recent years, with the availability of highfrequency financial market data modeling realized...

We present a new construction of the normal inverse Gaussian (NIG) fractal activity time model for a risky asset. The construction uses superpositions of diffusion processes and allows for specified exact NIG marginal distributions of the returns and flexible and tractable dependence structure...

In this paper we explore some crude approximation, calibration and estimation procedures for Normal Inverse Gaussian (NIG) variates of potential use in risk management. Among others we treat in some detail the calibration of bivariate NIG consistent with marginal NIG.

In this paper we explore some crude approximation, calibration and estimation procedures for Normal Inverse Gaussian (NIG) variates of potential use in risk management. Among others we treat in some detail the calibration of bivariate NIG consistent with marginal NIG.

The Normal Inverse Gaussian (NIG) distribution recently introduced by Barndorff-Nielsen (1997) is a promising alternative for modelling financial data exhibiting skewness and fat tails. In this paper we explore the Bayesian estimation of NIG-parameters by Markov Chain Monte Carlo Methods.

The Normal Inverse Gaussian (NIG) distribution recently introduced by Barndorff-Nielsen (1997) is a promising alternative for modelling financial data exhibiting skewness and fat tails. In this paper we explore the Bayesian estimation of NIG-parameters by Markov Chain Monte Carlo Methods.

In recent years, with the availability of high-frequency financial market data modeling realized volatility has become a new and innovative research direction. The construction of “observable” or realized volatility series from intra-day transaction data and the use of standard time-series...

We propose a quasi-Monte Carlo (qMC) algorithm to simulate variates from the normal inverse Gaussian (NIG) distribution. The algorithm is based on a Monte Carlo technique found in Rydberg [13], and is based on sampling three independent uniform variables. We apply the algorithm to three problems...