Search results for "skewness"
showing 10 items of 43 documents
Modeling Conditional Skewness in Stock Returns
2007
Abstract In this paper, we propose a new GARCH-in-Mean (GARCH-M) model allowing for conditional skewness. The model is based on the so-called z distribution capable of modeling skewness and kurtosis of the size typically encountered in stock return series. The need to allow for skewness can also be readily tested. The model is consistent with the volatility feedback effect in that conditional skewness is dependent on conditional variance. Compared to previously presented GARCH models allowing for conditional skewness, the model is analytically tractable, parsimonious and facilitates straightforward interpretation.Our empirical results indicate the presence of conditional skewness in the mon…
"Table 5" of "Energy dependence of event shapes and of alpha(s) at LEP-2."
1999
Moments of the Heavy Jet Mass (MH**2/EVIS**2) distributions at cm energies 133, 161, 172 and 183 GeV.
"Table 1" of "Energy dependence of event shapes and of alpha(s) at LEP-2."
1999
Moments of the (1-THRUST) distributions at cm energies 133, 161, 172 and 183 GeV.
"Table 7" of "Energy dependence of event shapes and of alpha(s) at LEP-2."
1999
Moments of the Jet Mass Differences (MDIFF**2/EVIS**2) distributions at cm energies 133, 161, 172 and 183 GeV.
The Choice of Performance Measure Does Influence the Evaluation of Hedge Funds
2010
It is widely accepted that, when return distributions are non-normal, the use of the Sharpe ratio can lead to misleading conclusions. It is well documented that deviations of hedge fund return distributions from normality are statistically significant. The literature on performance evaluation that takes into account the non-normality of return distributions is a vast one. However, there is another stream of research that advocates that the choice of performance measure does not influence the evaluation of hedge funds. For example, Eling and Schuhmacher (2007) and Eling (2008) performed empirical studies and, judging by the values of rank correlations, concluded that the choice of performanc…
Approaches to characterise chromatographic column performance based on global parameters accounting for peak broadening and skewness.
2009
Peak broadening and skewness are fundamental parameters in chromatography, since they affect the resolution capability of a chromatographic column. A common practice to characterise chromatographic columns is to estimate the efficiency and asymmetry factor for the peaks of one or more solutes eluted at selected experimental conditions. This has the drawback that the extra-column contributions to the peak variance and skewness make the peak shape parameters depend on the retention time. We propose and discuss here the use of several approaches that allow the estimation of global parameters (non-dependent on the retention time) to describe the column performance. The global parameters arise f…
Parabolic-Lorentzian modified Gaussian model for describing and deconvolving chromatographic peaks.
2002
Abstract A new mathematical model for characterising skewed chromatographic peaks, which improves the previously reported polynomially modified Gaussian (PMG) model, is proposed. The model is a Gaussian based equation whose variance is a combined parabolic-Lorentzian function. The parabola accounts for the non-Gaussian shaped peak, whereas the Lorentzian function cancels the variance growth out of the elution region, which gives rise to a problematic baseline increase in the PMG model. The proposed parabolic-Lorentzian modified Gaussian (PLMG) model makes a correct description of peaks showing a wide range of asymmetry with positive and/or negative skewness. The new model is shown to give b…
Burst analysis tool for developing neuronal networks exhibiting highly varying action potential dynamics
2012
In this paper we propose a firing statistics based neuronal network burst detection algorithm for neuronal networks exhibiting highly variable action potential dynamics. Electrical activity of neuronal networks is generally analyzed by the occurrences of spikes and bursts both in time and space. Commonly accepted analysis tools employ burst detection algorithms based on predefined criteria. However, maturing neuronal networks, such as those originating from human embryonic stem cells (hESC), exhibit highly variable network structure and time-varying dynamics. To explore the developing burst/spike activities of such networks, we propose a burst detection algorithm which utilizes the firing s…
A Skewed GARCH-in-Mean Model: An Application to U.S. Stock Returns
2004
In this paper we consider a GARCH-in-Mean (GARCH-M) model based on the so-called z distribution. This distribution is capable of modeling moderate skewness and kurtosis typically encountered in financial return series, and the need to allow for skewness can be readily tested. We apply the new GARCH-M model to study the relationship between risk and return in monthly postwar U.S. stock market data. Our results indicate the presence of conditional skewness in U.S. stock returns, and, in contrast to the previous literature, we show that a positive and significant relationship between return and risk can be uncovered, once an appropriate probability distribution is employed to allow for conditi…
A granulometric analysis of specular microscopy images of human corneal endothelia
2005
The inner layer of the human cornea, called the corneal endothelium, plays an important role in the maintenance of corneal transparency. Specular microscopy is the most widely used technique to study the corneal endothelium in vivo. Improvements in technology have allowed us to obtain good quality specular images, but the detection and quantification of small size-shape cell changes is not obvious, specially when the physician wants to evaluate endothelial cell changes after some surgical procedures. This paper proposes a methodology to analyze specular microscopy images. Every corneal endothelium is described by means of different cumulative distribution functions or some moments (mean, st…