Summary
Shipping freight rates are notoriously volatile and shipping investors are perceived to be risk loving. The paper explores the stochastic properties of freight rates in the shipping industry ...and derives the analytical equations for their moments in downside and upside markets by using a two‐piece extension of the generalized error distribution. Pricing equations developed across shipping segments show how conditional risk and conditional skewness are priced along with their risk spillover effects. Results reveal the existence of a positive skewness premium, suggesting that shipping investors are willing to accept lower expected returns for the opportunity to earn high pay‐offs in the future.
The paper looks at the results of Apergis, Christou and Kynigakis (2019) and proposes a novel model that allows time variation in volatility, skewness and kurtosis, based on multivariate stable ...distributions. The analysis also looks at bank sector CDS, insurance sector CDS, sovereign bonds, equity and volatility indices. The findings corroborate their results and indicate significant evidence of contagion, especially through the channels of co‐skewness and co‐kurtosis. In addition, it establishes a higher order channel of causality between co‐skewness and co‐kurtosis.
A bilateral exchange rate is the price of a base currency in terms of a quote currency. In this situation, how do international investors decide the contributions of both the base currency and the ...quote currency to the mean, variance, skewness, and kurtosis of the movements in each bilateral exchange rate? For a group of currencies, each bilateral exchange rate can be decomposed into a difference between two multilateral exchange rates: a base-currency multilateral exchange rate minus a quote-currency multilateral exchange rate. In this paper, the decomposition of bilateral exchange rates is used to decompose the moments of the movements in bilateral exchange rates. The result is a quantitative methodology to perform currency attribution, where the mean, variance, skewness, and kurtosis of the movements in each bilateral exchange rate can be attributed to the base currency and the quote currency.
We find that initial public offerings (IPOs) with high expected skewness experience significantly greater first-day returns. The skewness effect is stronger during periods of high investor sentiment ...and is related to differences in skewness across industries as well as to time-series variation in the level of skewness in the market. IPOs with high expected skewness earn more negative abnormal returns in the following one to five years. High expected skewness is also associated with a higher fraction of small-sized trades on the first day of trading, which is consistent with a greater shift in holdings from institutions to individuals. The results suggest that first-day IPO returns are related to a preference for skewness.
This paper was accepted by Brad Barber, Teck Ho, and Terrance Odean, special issue editors.
Previous studies have adopted unsupervised machine learning with dimension reduction functions for cyberattack detection, which are limited to performing robust anomaly detection with ...high-dimensional and sparse data. Most of them usually assume homogeneous parameters with a specific Gaussian distribution for each domain, ignoring the robust testing of data skewness. This paper proposes to use unsupervised ensemble autoencoders connected to the Gaussian mixture model (GMM) to adapt to multiple domains regardless of the skewness of each domain. In the hidden space of the ensemble autoencoder, the attention-based latent representation and reconstructed features of the minimum error are utilized. The expectation maximization (EM) algorithm is used to estimate the sample density in the GMM. When the estimated sample density exceeds the learning threshold obtained in the training phase, the sample is identified as an outlier related to an attack anomaly. Finally, the ensemble autoencoder and the GMM are jointly optimized, which transforms the optimization of objective function into a Lagrangian dual problem. Experiments conducted on three public data sets validate that the performance of the proposed model is significantly competitive with the selected anomaly detection baselines.
•An ensemble framework of multichannel network anomaly detection model that combines deep autoencoders and the GMM.•A robust optimization version of EM3 for multiple domains, which transforms the optimization problem of the objective function into a Lagrangian dual.•We deduce the formula and analyze the convergence of the full text, and prove that our model has stability and robustness.•To the best of our knowledge is the first work that performs algorithms on both differentiated data domains and data distributions.
For clinical studies with continuous outcomes, when the data are potentially skewed, researchers may choose to report the whole or part of the five-number summary (the sample median, the first and ...third quartiles, and the minimum and maximum values) rather than the sample mean and standard deviation. In the recent literature, it is often suggested to transform the five-number summary back to the sample mean and standard deviation, which can be subsequently used in a meta-analysis. However, if a study contains skewed data, this transformation and hence the conclusions from the meta-analysis are unreliable. Therefore, we introduce a novel method for detecting the skewness of data using only the five-number summary and the sample size, and meanwhile, propose a new flow chart to handle the skewed studies in a different manner. We further show by simulations that our skewness tests are able to control the type I error rates and provide good statistical power, followed by a simulated meta-analysis and a real data example that illustrate the usefulness of our new method in meta-analysis and evidence-based medicine.
Split sample skewness Adil, Iftikhar Hussain; Wahid, Abdul; Mantell, Edmund H.
Communications in statistics. Theory and methods,
11/2021, Volume:
50, Issue:
22
Journal Article
Peer reviewed
The shape of a statistical distribution of data is important in such diverse areas as descriptive analysis, risk analysis, portfolio optimization and strategic decision making. If it is known (or ...believed) that the probability density function of a random variable is not symmetric, the question of its skewness becomes important. Various methods of assessing skewness have been formulated, but none are totally satisfactory. The classical measurement of skewness is based on higher moments of the random variable about its mean. However, it is well known that those measurements are sensitive to extreme outliers. The implication of the sensitivity is that mean-based metrics of skewness are inefficient, especially in small or medium size sample data. Those mean-based metrics are known as Pearson skewness, Quartile skewness and Octile skewness. All have been devised to try to accommodate for the presence of outliers. However, those test statistics are demonstrably inefficient in the presence of outliers. That inefficiency motivates the approach in this paper; the development of an efficient and more robust skewness metric we call Split Sample Skewness, hereafter referred to as SSS. In this context, efficiency means that the SSS metric requires fewer sample observations than the less efficient metrics cited above to achieve the same level of statistical robustness. The name reflects a methodology that partitions the sample into two subgroups at the median. This paper displays the findings of multiple simulation studies adducing evidence bearing on the efficiency of split ample skewness relative to other measures of skewness.
In the face of complex financial phenomena, describing the high uncertainties in financial markets remains a challenging issue in modelling and decision making. In this paper, chance theory is used ...to analyse the hybrid uncertainty that combines random returns and uncertain returns. We regard the total return as an uncertain random variable and study the uncertain random portfolio optimization problem. We first define the skewness of an uncertain random variable and derive some important properties and explicit expressions with deterministic distributions. These theoretical results help transform the model into a deterministic form. Then, uncertain random mean-variance-skewness portfolio optimization models and their corresponding equivalents are established to meet the diverse needs of investors. Finally, we provide two numerical experiments to illustrate the applicability of the proposed model, one of which demonstrates that the model can be applied in the area of energy finance. It is shown that when uncertain asset returns are asymmetric, this model can be used to make investment decisions for investors. We also find that a higher return is accompanied by higher risk and higher skewness.
In this work, the application of the bicoherence (a squared normalized version of the bispectrum) of the stray flux signal is proposed as a way of detecting faults in the field winding of synchronous ...motors. These signals are analyzed both under the starting and at steady state regime. Likewise, two quantitative indicators are proposed, the first one based on the maximum values of the asymmetry and the kurtosis of the bicoherence matrix obtained from the flux signals and the second one relying on an algorithm based on the bicoherence image segmentation of the obtained pattern for each analyzed state. The results are analyzed through a comparative study for the two considered motor regimes, obtaining satisfactory results that sustain the potential application of the proposed methodology for the automatic field winding fault detection in real applications.
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