The impacts of outliers on different estimators for GARCH processes: an empirical study

The Maximum likelihood estimation (MLE) is the most widely used method to estimate the parameters of a GARCH(p,q) process. This is owed to the fact that the MLE, among other properties, is asymptotically efficient. Even though the MLE is sensitive to outliers, which can occur in time series. In order to abate the influence of outliers, robust estimators are introduced. Afterwards an Monte Carlo study compares the introduced estimators. --GARCH,Robust-Estimates,M-Estimates

Detecting outliers in time series

In parametric time series analysis there is the implicit assumption of no aberrant observations, so-called outliers. Outliers are observations that seem to be inconsistent with the assumed model. When these observations are included to estimate the model parameters, the resulting estimates are biased. The fact that markets have been affected by shocks (i.e. East Asian crisis, Dot-com bubble, sub-prime mortgage crisis) make the assumption that no outlier is present questionable. This paper addresses the problem of detecting outlying observations in time series. Outliers can be understood as a short transient change of the underlying parameters. Unfortunately tests designed to detect structural breaks cannot be used to find outlying observations. To overcome this problem a test normally used to detect structural breaks is modified. This test is based on the cumulative sum (CUSUM) of the squared observations. In comparison to a likelihood-ratio test neither the underlying model nor the functional form of the outliers have to be specified. In a simulation study the finite sample behaviour of the proposed test is analysed. The simulation study shows that the test has reasonable power against a variety of alternatives. Moreover, to illustrate the behaviour of the proposed test we analyse the returns of the Volkswagen stock

Communication and Stochastic Processes in Some Bacterial Populations: Significance for Membrane Computing

Intercellular communication between bacterial cells belonging to the same population is well documented in Microbiology, sporulation and cannibalism in B. Subtilis and genetic competence and fratricide in S. pneumoniae being deeply studied in the last years. The investigation of individual cell behavior has revealed that populations of these bacteria sometimes bifurcate into phenotypically distinct, but genetically identical, subpopulations by random switching mechanisms. The probabilistic nature of the random switching mechanisms, the occurrence of some biochemical processes related to it at plasma membrane and the need to study the processes at the level of each individual cell make intercellular communication and stochastic processes very suitable to be modeled by P systems

The impacts of outliers on different estimators for GARCH processes: an empirical study

The Maximum likelihood estimation (MLE) is the most widely used method to estimate the parameters of a GARCH(p,q) process. This is owed to the fact that the MLE, among other properties, is asymptotically efficient. Even though the MLE is sensitive to outliers, which can occur in time series. In order to abate the influence of outliers, robust estimators are introduced. Afterwards an Monte Carlo study compares the introduced estimators

A China-EU electricity transmission link: Assessment of potential connecting countries and routes

The report looks at the potential routes for a future power interconnection between EU and China. High voltage direct current technology is considered and its potential is assessed. It analyses the renewable energy sources in the countries along the potential routes as well as the power sector and power grid in the countries crossed. Three potential routes are analysed.JRC.C.3-Energy Security, Distribution and Market

Outliers & predicting time series: A comparative study

Nonparametric prediction of time series is a viable alternative to parametric prediction, since parametric prediction relies on the correct specification of the process, its order and the distribution of the innovations. Often these are not known and have to be estimated from the data. Another source of nuisance can be the occurrence of outliers. By using nonparametric methods we circumvent both problems, the specification of the processes and the occurrence of outliers. In this article we compare the prediction power for parametric prediction, semiparametric prediction and nonparamatric methods such as support vector machines and pattern recognition. To measure the prediction power we use the MSE. Furthermore we test if the increase in prediction power is statistically significant

Spatially Varying Steady State Longitudinal Magnetization in Distant Dipolar Field-based Sequences

Sequences based on the Distant Dipolar Field (DDF) have shown great promise for novel spectroscopy and imaging. Unless spatial variation in the longitudinal magnetization, M_{z}(s), is eliminated by relaxation, diffusion, or spoiling techniques by the end of a single repetition, unexpected results can be obtained due to spatial harmonics in the steady state M_{z}^{SS}(s) profile. This is true even in a homogeneous single-component sample. We have developed an analytical expression for the M_{z}^{SS}(s) profile that occurs in DDF sequences when smearing by diffusion is negligible in the TR period. The expression has been verified by directly imaging the M_{z}^{SS}(s) profile after establishing the steady state. more keywords: magnetic resonance, intermolecular multiple quantum coherence, mesoscale structure, iMQC, DDFComment: 7 pages, 4 figures, submitted to Journal of Magnetic Resonanc