Scale invariance in financial time series

We focus on new insights of scale invariance and scaling properties usefully applied in the framework of a statistical approach to study the empirical finance. Two stock returns of Sri Lankan stock market indices All Share Price Index and Milanka Price Index index were considered. Central parts of the probability distribution function of returns are well fitted by the Lorentzian distribution function. However, tail parts of the probability distribution function follow a power law asymptotic behavior. We found that the probability distribution function of returns for both All Share Price Index and Milanka Price Index , is outside the L´evy stable distribution. Sri Lankan stock market is not described by the random Gaussian stochastic processes.

Semiparametric estimation of duration models when the parameters are subject to inequality constraints and the error distribution is unknown

This paper proposes a semiparametric method for estimating duration models when there are inequality constraints on some parameters and the error distribution may be unknown. Thus, the setting considered here is particularly suitable for practical applications. The parameters in duration models are usually estimated by a quasi-MLE. Recent advances show that a semiparametrically efficient estimator [SPE] has better asymptotic optimality properties than the QMLE provided that the parameter space is unrestricted. However, in several important duration models, the parameter space is restricted, for example in the commonly used linear duration model some parameters are non-negative. In such cases, the SPE may turn out to be outside the allowed parameter space and hence are unsuitable for use. To overcome this difficulty, we propose a new constrained semiparametric estimator. In a simulation study involving duration models with inequality constraints on parameters, the new estimator proposed in this paper performed better than its competitors. An empirical example is provided to illustrate the application of the new constrained semiparametric estimator and to show how it overcomes difficulties encountered when the unconstrained estimator of nonnegative parameters turn out to be negative.Adaptive inference; Conditional duration model; Constrained inference; Efficient semiparametric estimation; Order restricted inference; Semiparametric efficiency bound.

Distances to Supernova Remnants G20.4+0.120.4+0.1, G24.7−0.624.7-0.6 and G28.6−0.128.6-0.1 and New Molecular Cloud Associations

Accurate distances to supernova remnants (SNRs) are crucial in determining their size, age, luminosity and evolutionary state. To determine distances, we chose three SNRs from the VLA Galactic Plane Survey (VGPS) for extraction of HI absorption spectra. Analysing HI absorption spectra, $^{13}$CO emission spectra, and HI and $^{13}$CO channel maps, kinematic velocities (or their limits) to the three SNRs were calculated. The three SNRs are probably associated with molecular clouds and the new distance to G$20.4+0.1$, G$24.7-0.6$ and G$28.6-0.1$ are $7.8 \pm0.5$ kpc, $3.8\pm 0.2$ kpc and $9.6 \pm 0.3$ kpc, respectively.Comment: 10 pages, 14 figure