Stochastic chains with memory of variable length constitute an interesting family of stochastic chains of infinite order on a finite alphabet. The idea is that for each past, only a finite suffix of the past, called context, is enough to predict the next symbol. These models were first introduced in the information theory literature by Rissanen (1983) as a universal tool to perform data compres...

Let (Zt)t3⁄40 be an Rn-valued Lévy process. We consider stochastic differential equations of the form dX x t = Φ(X x t−) dZt X x 0 = x , x ∈R d , where Φ : Rd → Rd×n is Lipschitz continuous. We show that the infinitesimal generator of the solution process (X x t )t3⁄40 is a pseudo-differential operator whose symbol p : R d ×Rd → C can be calculated by p(x ,ξ) :=− lim t↓0 Ex ei(X σ t −x) >ξ − 1 ...

An indirect estimator is proposed for two long memory volatility models; the fractionally integrated generalised autoregressive conditional heteroskedasticity (FIGARCH) model and the long memory stochastic volatility (LMSV) model. The small sample properties of the indirect estimator are compared to the small sample properties of conventional maximum likelihood estimators. It is found that the ...

A memory control for T-S fuzzy discrete-time systems with sto- chastic input delay is proposed in this paper. Dierent from the common assumptions on the time delay in the existing literatures, it is assumed in this paper that the delays vary randomly and satisfy some probabilistic dis- tribution. A new state space model of the discrete-time T-S fuzzy system is derived by introducing some stocha...

We seek to determine the optimal amount of the insurer’s investment in all types of assets for a small and closed economy. The goal is to detect the implications and contributions the risk seeker and risk aversion insurer commonly make and the effectiveness in the investment decision. Also, finding the optimum portfolio for each is the main goal of the present study. To this end, we adopted the...

exist. We shall prove that under certain conditions we obtain (1) as a limit distribution of double sequences of independent and infinitesimal random variables and apply this theorem to stochastic processes with independent increments. Theorem 1 Let ξn1, ξn2, . . . , ξnkn (n = 1, 2, . . .) be a double sequence of random variables. Suppose that the random variables in each row are independent, t...