Volume 31, Issue 1

Here we consider a more flexible class of asymmetric mixture normal distribution and investigate some of its important statistical properties. We define its location-scale extension and discuss the method of maximum likelihood for estimating its parameters. Two real life data sets are considered for illustrating the usefulness of the model. Further, a simulation study is carried out for examining the efficiency of maximum likelihood estimators of the parameters of the distribution.

In this paper, we will study an important property on the regularity of the trajectories of the stochastic flow generated by a famous model in finance. More precisely, we prove the differentiability with respect to initial data of the solution of the stochastic differential equation associated with this model based on Gronwall’s lemma, Itô’s isometry and Burkholder–Davis–Gundy’s and Hölder’s inequalities. This is the main motivation of our research.

In this work, we will try to weaken the hypothesis imposed by Hu and Peng. We will be concerned with finding the solution of locally monotone BSDEs associated to fBm. As an auxiliary step, we study the existence and uniqueness of a solution to the monotone backward SDEs associated to fBm. Then we connect these two kinds of fractional backward SDEs with the corresponding semilinear partial differential equations (PDEs for short).

We propose two fractional risk models, where the classical risk process is time-changed by the mixture of tempered stable inverse subordinators. We characterize the risk processes by deriving the marginal distributions and establish the moments and covariance structure. We study the main characteristics of these models such as ruin probability and time to ruin and illustrate the results with Monte Carlo simulations. The data suggest that the ruin time can be approximated by the inverse gaussian distribution and its generalizations.

In this paper, we study the stochastic zero-sum differential game in finite horizon in a general case. We first prove that the BSDE associated with a specific generator (the Hamiltonian function for the game) has a unique solution. Then we characterize the value function as that solution to prove the existence of a saddle point for the game. Finally, in the Markovian framework, we show that the value function is the unique viscosity solution for the related partial differential equation.

In this paper, we introduce Wiener integrals with respect to the generalized Hermite process and we prove a non-central limit theorem in which this integral appears as limit. As an application, we investigate the corresponding stochastic differential equations with the generalized Hermite process as a driving noise, we prove the existence and the uniqueness of the solution, and we give a generalization of the Hermite Ornstein–Uhlenbeck process and the Hermite-driving Vasicek process.