PARAMETER ESTIMATION FOR A CLASS OF STOCHASTIC DIFFERENTIAL EQUATIONS DRIVEN BY SMALL STABLE NOISES FROM DISCRETE OBSERVATIONS

We study the least squares estimation of drift parameters for a class of stochastic differential equations driven by small α-stable noises, observed at n regularly spaced time points ti=i/n, i=1,···,n on [0, 1]. Under some regularity conditions, we obtain the consistency and the rate of convergence of the least squares estimator (LSE) when a small dispersion parameter ε→0 and n→∞ simultaneously. The asymptotic distribution of the LSE in our setting is shown to be stable, which is completely different from the classical cases where asymptotic distributions are normal.     

SYMMETRIC INTEGRAL AND THE APPROXIMATION THEOREM OF STOCHASTIC INTEGRAL IN THE PLANE

In this paper, we consider the approximation problem of stochastic integral with respect to two-parameter Wiener process. We first introduce a kind of symmetric integral and prove it obeys the chain rule. Then we apply an integral formula of bounded variation functions with two variables to show the approximation theorem of stochastic integral in the plane. In particular, we prove that the symmetric stochastic integral is stable when the limit is taken in the sense of L~2convergence.     

ON THE SINGULARITY OF LEAST SQUARES ESTIMATOR FOR MEAN-REVERTING α-STABLE MOTIONS

We study the problem of parameter estimation for mean-reverting α-stable motion, dXt = （a0 - θ0Xt）dt ＋ dZt, observed at discrete time instants. A least squares estimator is obtained and its asymptotics is discussed in the singular case （a0, θ0） = （0, 0）. If a0 = 0, then the mean-reverting α-stable motion becomes Ornstein-Uhlenbeck process and is studied in [7] in the ergodic case θ0 〉 0. For the Ornstein-Uhlenbeck process, asymptotics of the least squares estimators for the singular case （θ0 = 0） and for ergodic case （θ0 〉 0） are completely different.     

ANTICIPATING QUADRANT AND SYMMETRIC INTEGRALS IN THE PLANE WITH APPLICATION TO WIENER SPACE

1IntroductionItiswellknowntattheadaptednessconditionontheintegrandisnecessarytoestablishthetheoryofIt6'sstochasticintegrals.Butinmanysitustions,theprocessesmaynotsatisfytheadaptedness.TherehavebeenmanyattemptstoweakentheadaptednessconditionfortheintegrandofIt6'stochasticintegral.ThemostsignificantcontributionisSkorohod'sintegralwhichwasintroducedbySkorohodin1975.Skorohodintegralgeneralizesboththeits'sforwardandbackwardintegralsandmakesnorestrictionontheintegrand.ButSkorohod'sintegralhasalso…     

ON THE RATES OF CONVERGENCE IN THE CENTRAL LIMIT THEOREM FOR TWO－PARAMETER MARTINGALE DIFFERENCES

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THE APPROXIMATION THEOREM OF STOCHASTIC DIFFERENTIAL EQUATIONS IN THE PLANE

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