The normal inverse Gaussian (NIG) process is a Lévy process with no Brownian component and NIG-distributed increments. The NIG process can be constructed either as a process with NIG increments or, alternatively, via random time change of Brownian motion using the inverse Gaussian process to determine time. The article presents the normal inverse Gaussian distribution function and the corresponding characteristic function and Lévy measure. Further, the NIG process is used to construct a market model for financial assets. Option pricing can be done using the NIG density function, the NIG Lévy characteristics, or the NIG characteristic function
We model Normal Inverse Gaussian distributed log-returns with the assumption of stochastic volatilit...
In this paper we explore some crude approximation, calibration and estimation procedures for Normal ...
We model Normal Inverse Gaussian distributed log-returns with the assumption of stochastic volatilit...
The normal inverse Gaussian (NIG) process is a Lévy process with no Brownian component and NIG-distr...
The normal inverse Gaussian (NIG) process is a Lévy process with no Brownian component and NIG-distr...
Normal inverse Gaussian (NIG) distribution is quite a new distribution introduced in 1997. This is d...
The normal inverse Gaussian (NIG) distribution is a recent flexible closed form distribution that ma...
We discuss the Normal inverse Gaussian (NIG) distribution in modeling volatility in the financial ma...
The normal inverse Gaussian process has been used to model both stock returns and interest rate proc...
Revised June 15, 1998This paper explores the possibility of using the Normal Inverse Gaussian (NIG) ...
This paper explores the possibility of using the Normal Inverse Gaussian (NIG) distribution introduc...
Revised June 15, 1998This paper explores the possibility of using the Normal Inverse Gaussian (NIG) ...
A fractional normal inverse Gaussian (FNIG) process is a fractional Brownian motion subordinated to ...
This thesis aims to study a new Levy-driven diffusion process, where the random innovations that und...
In this paper we explore some crude approximation, calibration and estimation procedures for Normal ...
We model Normal Inverse Gaussian distributed log-returns with the assumption of stochastic volatilit...
In this paper we explore some crude approximation, calibration and estimation procedures for Normal ...
We model Normal Inverse Gaussian distributed log-returns with the assumption of stochastic volatilit...
The normal inverse Gaussian (NIG) process is a Lévy process with no Brownian component and NIG-distr...
The normal inverse Gaussian (NIG) process is a Lévy process with no Brownian component and NIG-distr...
Normal inverse Gaussian (NIG) distribution is quite a new distribution introduced in 1997. This is d...
The normal inverse Gaussian (NIG) distribution is a recent flexible closed form distribution that ma...
We discuss the Normal inverse Gaussian (NIG) distribution in modeling volatility in the financial ma...
The normal inverse Gaussian process has been used to model both stock returns and interest rate proc...
Revised June 15, 1998This paper explores the possibility of using the Normal Inverse Gaussian (NIG) ...
This paper explores the possibility of using the Normal Inverse Gaussian (NIG) distribution introduc...
Revised June 15, 1998This paper explores the possibility of using the Normal Inverse Gaussian (NIG) ...
A fractional normal inverse Gaussian (FNIG) process is a fractional Brownian motion subordinated to ...
This thesis aims to study a new Levy-driven diffusion process, where the random innovations that und...
In this paper we explore some crude approximation, calibration and estimation procedures for Normal ...
We model Normal Inverse Gaussian distributed log-returns with the assumption of stochastic volatilit...
In this paper we explore some crude approximation, calibration and estimation procedures for Normal ...
We model Normal Inverse Gaussian distributed log-returns with the assumption of stochastic volatilit...