Skewness Index Research Articles

Livestock production and poverty among rural farming households in Ethiopia

PurposeThis study assessed the impact of livestock production on the poverty of rural households in Ethiopia.Design/methodology/approachData from the Ethiopia Living Standard Measurement Survey 2021/2022 was employed. The study assessed poverty using additional poverty aversion parameters (skewness and kurtosis) in the Foster–Greer–Thorbecke (FGT) and the conditional mixed process (CMP) model.FindingsThe result shows that 49% of the livestock farming households are poor. The poverty skewness and kurtosis indices show that the bulk of the population is far from the poverty line and poverty extremities exist. The CMP estimates show that age, household size, marital status, distance to market and capital region, income and agroecology influenced tropical livestock units. Also, an additional increase in tropical livestock significantly increases the household per capita expenditure.Social implicationsAlthough livestock production positively contributed to household expenditure, poverty still exists in all regions in Ethiopia. Interventions should consider local conditions and community needs in the value chain approach.Originality/valueThis study examined rural household poverty beyond the conventional headcount, gap and severity by including poverty skewness and kurtosis.Peer reviewThe peer review history for this article is available at https://publons.com/publon/10.1108/IJSE-05-2024-0415.

Fear, extreme fear and U.S. stock market returns

This paper investigates the multiscale co-movement between the levels and changes in VIX and SKEW indices and the returns of the S&P 500 market indices at the aggregate and sector levels of data. By utilizing the wavelet localized multiple correlation (WLMC) method over the most recent time period (January 3, 2006 - October 26, 2023), we demonstrate that very short-term market participants, such as traders and speculators, are watchful about the combined information from the VIX and SKEW levels; while the changes in VIX and SKEW are more informative for stock returns in the medium- to long-term, which should concern the decisions of investors and portfolio managers. We also show that VIX in general exerts a more dominant influence on the S&P 500 Index returns than SKEW, but more for VIX changes than for VIX levels. The very short-run informativeness of VIX and SKEW for stock returns is markedly pronounced around the European debt crisis (2012) and the COVID-19 meltdown (2020). The stock sector analysis reveals that defensive sectors are sensitive to the SKEW index, whereas the cyclical sectors are predominantly related to the fluctuations in the VIX index. Overall, our findings provide useful new insights into the stock market sentiment and the resulting market participants’ behavior at the market microstructure level. As such, they may be of interest not only to traders and investors, but also to financial analysts and government policy-makers in their attempts to understand the multiscale relationship between monetary policy and the stock market.

Skew Index: a machine learning forecasting approach

Skew Index: a machine learning forecasting approach

Forecasting the CBOE VIX and SKEW Indices Using Heterogeneous Autoregressive Models

We analyze the predictability of daily data on the CBOE VIX and SKEW indices, used to capture the average level of risk-neutral risk and downside risk, respectively, as implied by S&P 500 index options. In particular, we use forecast models from the Heterogeneous Autoregressive (HAR) class to test whether and how lagged values of the VIX and of the SKEW may increase the forecasting power of HAR for the SKEW and the VIX. We find that a simple HAR is very hard to beat in out-of-sample experiments aimed at forecasting the VIX. In the case of the SKEW, the benchmarks (the random walk and an AR(1)) are clearly outperformed by HAR models at all the forecast horizons considered and there is evidence that special definitions of the SKEW index based on put options data only yield superior forecasts at all horizons.

International convergence towards a climate-neutral economy: modeling the agricultural sector

Purpose. This article aims to construct a comprehensive convergent model for assessing the global and EU’s progress, degree of consolidation and symmetry of agricultural economies towards climate neutrality in the context of key international green initiatives. Methodology / approach. This research employs both qualitative and quantitative analyses to assess convergence measures in the agricultural sector’s carbon emissions. The quantitative component relies on the sigma and beta convergence models to evaluate international convergence in carbon emissions CO2 dynamics. The dimensions of modeling are as follows: global sample within 194 countries with reliable emissions data; European Union, focusing on convergence within the EU27 member states, the United Kingdom, and Ukraine. The time period covers open data from 1972–2022. The dynamics of sigma and beta convergence is determined for the points, namely UN Stockholm Conference (1972), UN Rio Conference (1992), Kyoto Protocol (entry into force in 2005), Paris Agreement (2015). Additionally, statistical indicators of variation, skewness, Gini and Theil indices were calculated for delineating global smoothness and the concentration of agricultural emissions. Results. The results of the study reveal an inertial and divergent trend of the agricultural economy towards decarbonisation, which slows down the overall movement towards “net zero” due to the presence of clubs of lagging agricultural countries that increase emissions in violation of international agreements. The reduction in emissions skewness in recent years shows that more countries are “pulling” decarbonisation due to their high capacity to move towards net zero, but this is not enough. The EU is the most prominent example of accelerated climate convergence, but markers of its weakening in recent years are identified due to the inability of economies to maintain the pace of decarbonisation caused by economic constraints, technological barriers, policy and regulatory issues, and misunderstandings of climate neutrality goals. It is shown that the long-term decarbonisation capacity of the agricultural sector is the key trigger for a country to make a positive contribution to the global convergence towards climate neutrality. At the current stage, the pace of decarbonisation plays a much greater role for consolidating efforts in the agricultural economy and achieving climate neutrality than the initial level of emissions in the sector. Factors in this process include proactive compliance with global climate agreements, technology sharing and cooperation, digitalisation and smart agriculture, and green financing and investment. Its implementation requires a three-way integration of stakeholder actions, strategy selection and results evaluation. Originality / scientific novelty. The study’s originality lies in its large-scale analysis of over 50 years of emissions dynamics and the context of five key green agreements that provided support for the green transition. It allows studying international convergence in agricultural sectors globally and within the EU. The novelty implies the integrated use of sigma and beta convergence models that identifies predictors of convergent and divergent processes and separates countries into leaders and laggards of agricultural decarbonisation. This approach provides a comprehensive view of modern climate policy, the impact of international green initiatives, and the position of individual towards climate neutrality in agriculture. Practical value / implications. The practical value lies in the ability to adjust climate policies for the agricultural economy’s decarbonisation, facilitating the determination of prospective outcomes for achieving climate neutrality. The aforementioned factors facilitate the process of governmental decision-making. The assessment of international programmatic agreements' effectiveness is enhanced through these models. The study offers a framework for global convergence towards climate neutrality in agriculture, highlighting the importance of digital technologies and smart agriculture as significant factors in global convergence.

Time-varying expected returns, conditional skewness and Bitcoin return predictability

We employ a GARCH-type model to jointly estimate returns, conditional variance and skewness and show that conditional skewness outperforms sample skewness and conditional and sample variance in predicting future Bitcoin returns. Interestingly, the results show that the relationship between conditional skewness and future Bitcoin returns is different depending on the sample period. In the first subsample (2018–2020), a period of relative calm in the Bitcoin market, the relationship is negative, which is in line with that found in the literature. However, in the second subsample (2021–2022), a period of major turmoil in the Bitcoin market, the relationship is positive, which is consistent with that found in previous papers on the relationship between conditional market skewness and future index returns during crisis periods. Based on these results, a dynamic buy and sell strategy of buying or selling Bitcoin based on the estimated conditional skewness is proposed. This dynamic strategy outperforms a static buy-and-hold strategy. The profitability of this strategy can be viewed as the reward that investors demand for bearing the risk associated with the changing conditions in the cryptocurrency market that generate time-varying expected returns.

On the existence of stationary threshold bilinear processes

This article investigates some statistical and probabilistic properties of general threshold bilinear processes. Sufficient conditions for the existence of a causal, strictly and weak stationary solution for the equation defining a self-exciting threshold superdiagonal bilinear SETBL\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\left( SETBL\\right) $$\\end{document} process are derived. Then it is shown that under well-specified hypotheses the higher-order moments of the SETBL process are finite. As a result, the skewness and kurtosis indexes are explicitly computed. The exact autocorrelation function is derived with an arbitrarily fixed number of regimes. Also, the covariance functions of the process and its powers are evaluated and the second (respectively, higher)-order structure is shown to be similar to that of a linear process. This implies that the considered process admits an ARMA representation. Finally, necessary and sufficient conditions for the invertibility and geometric ergodicity of a SETBL model are established. Some examples illustrate the obtained theoretical results.

QUALITY OF BLENDED LEARNING COURSES: STUDENTS’ PERSPECTIVE

This research paper focuses on studying students' perspectives on the quality of online course developed to support traditional face-to-face learning, specifically exploring their satisfaction levels. The study aims to identify the factors influencing student satisfaction and their impact on academic performance. A questionnaire was developed, consisting of four evaluated areas: course content and design, organization and preparation of training, communication and support in the learning process, and evaluation. The questionnaire was administered to 51 students who completed an Object-Oriented Programming course. The collected data was analysed using statistical techniques, including skewness and kurtosis indexes, Cronbach's alpha coefficients, average variance extracted (AVE), composite reliability (CR), and principal component analysis (PCA). The findings revealed a generally positive perception of course content, organization, communication, and evaluation with specific areas identified for improvement. The findings emphasize the importance of addressing students' satisfaction to enhance the overall quality of blended learning courses. The study contributes to the existing literature on student satisfaction with learning courses for blended learning. It highlights the need for institutions to prioritize course quality to meet students' expectations and needs.

Guidelines for ensuring the reliability of cameral evaluation of the authenticity of as-built survey maps

Aim. The paper aims to demonstrate a unique method of evaluating the authenticity of as-built survey maps using Microsoft Excel and verification results. The authenticity of the horizontal and vertical deviations of building structures documented in geodetic designs is evaluated by comparing the diagram of control point value distribution with a normal probability plot (Gaussian distribution). The verification of the developed method presented in the paper is based on a quantile plot, as well as calculations using three different methods involving the analysis of the parameters and statistical characteristics of the sought sample.Methods. The paper used the cameral method of evaluating as-built survey maps, as well as methods of mathematical statistics for verifying the research findings.Results. The hypothesis of normal distribution of the sample of control point deviations of the examined building structures was verified and confirmed using the following methods: quantile plotting; verification of the three sigma rule; analysis of the index of skewness and kurtosis; verification of the hypothesis of normal distribution per Pearson’s chi-squared test. That allows ensuring the reliability and verifiability of the results as regards the registration control of as-built drawings. The paper also substantiates the practical applicability of the method of evaluating the authenticity of asbuilt survey maps developed by the authors.

ZERO-INFLATED POISSON TRANSMUTED WEIGHTED EXPONENTIAL DISTRIBUTION: PROPERTIES AND APPLICATIONS

Count observations with high frequencies of zero counts abound in diverse fields. In actuary science, for example, insurance claims are often underreported, leading to a higher frequency of zero counts. This invariably reduces the mean and leads to over-dispersion. Different techniques to model such occurrences exist. This study uses the cubic rank transmutation map to compound the weighted exponential distribution and obtain a new count distribution in the mixed Poisson paradigm. The zero-inflated form of the proposition is obtained along with some mathematical properties. Simulated skewness, kurtosis, and dispersion index for the new distributions reveal they are suitable for model dispersed observation with positive skewness. Five datasets with high frequencies of zero counts are used to assess the performance of the new distribution along with some popular count distributions by using the maximum likelihood for parameter estimation. Results show that the natural form of the new proposition performs creditably better than its zero-inflated forms, even when there is a higher proportion of zero counts. The classical negative binomial distribution is also observed to outperform its zero-inflated form in most cases. In contrast, the zero-inflated Poisson distribution better fits the datasets than the classical Poisson distribution.

Operational modal identification of structures based on improved empirical wavelet transform

When empirical wavelet transform (EWT) is used to identify the modal parameters of civil engineering structures, the frequency band division is generally not accurate due to the noise effect on the Fourier spectrum. This phenomenon will lead to modal mixing and false modes in the analysis results. Therefore, this article establishes a signal frequency band division method by taking advantages of maximal spectrum technique and spectral skewness index. Combining the random decrement technique (RDT) and the least square method of single component modal parameter identification, an adaptive approach based on the improved EWT for operational modal parameter identification of civil engineering structures under stationary environmental excitations is proposed. The traditional frequency band division method based on EWT can not completely and effectively divide the meaningful frequency bands. While the proposed frequency band division technology based on the spectral skewness index can prevent the phenomenon of insufficient division and excessive division and realize adaptive frequency band division. The effectiveness of the proposed approach is validated by a numerical three-story frame and a field three-span concrete box girder bridge under ambient vibrations. The modal identification results from both numerical and experimental validations demonstrate that the proposed approach can effectively and accurately decompose the vibration responses and identify the structural modal parameters under operational conditions.

Research on gearbox fault detection model based on ridge regression and decision tree

Gearbox is widely used in mechanical equipment and plays an important role in mechanical transmission. Therefore, it is necessary to detect and diagnose the fault of the gearbox in time. This paper needs to establish a fault detection model of the gearbox to detect whether the gearbox is in a fault state. Because the result can only be yes or no, the ridge regression model is first established. However, because the difference between the original sample data is not obvious, the accuracy of the obtained ridge regression model is low. Therefore, this study extracts the features of the data of the four parts, and defines five indicators: effective value, pulse index, skewness index, margin index and kurtosis index. The decision tree model is established with 70 % of the sample data. Firstly, the depth of the largest tree is set to 5. Secondly, the importance of the feature is determined according to the size of the Gini value, and the fault detection decision tree model is constructed. Finally, the model is tested with 30 % of the test data, and the accuracy is 91.17 %. The precision rate is 88.9 %, and the recall rate is 94.12 %. It is considered that the model is more reliable.

Asia-Pacific stock market return and volatility in the uncertain world: Evidence from the nonlinear autoregressive distributed lag approach.

This paper examines the effects of three distinct groups of uncertainties on market return and volatility in the Asia-Pacific countries, including (i) the country-specific and US geopolitical risks; (ii) the US economic policy uncertainty; and (iii) the US stock market volatility (using the VIX and SKEW indices). Our sample includes 11 Asia-Pacific countries for the 1985-2022 period. We employ the nonlinear autoregressive distributed lag approach (ARDL) estimation technique to capture the asymmetric effects of uncertainties on market return and volatility, which are documented in the literature. Some findings are documented as follows. First, we find that US uncertainty indices, including US geopolitical risk, US economic policy uncertainty, and US VIX, significantly impact Asia-Pacific stock markets, while the impacts of domestic geopolitical risk and the US skewness index (SKEW) are relatively weak. Second, Asia-Pacific stock markets tend to overreact to uncertainty shocks stemming from US economic policy uncertainty and US geopolitical risk. Third, US economic policy uncertainty has more significant effects than the US geopolitical risk. Finally, our research documents that Asia-Pacific stock markets react heterogeneously to good and bad news from US VIX. Specifically, an increase in US VIX (bad news) has a stronger impact than a decrease in US VIX (good news). Policy implications have emerged based on the findings of this study.

Short-term load and price forecasting using artificial neural network with enhanced Markov chain for ISO New England

Nowadays, forecasting methods have gained significant attention, particularly with the design and development of energy systems. In fact, accurate load and price forecasting is crucial for effective planning, controlling, and operation of power systems, especially with renewable energy sources (RES). This paper implemented an improved Markov Chain Artificial Neural network (ANN-MC) for load forecasting. The proposed design involved a two-step implementation process, considering various statistical factors such as daily and weekly load, date/time of the year, environmental factors (e.g., dry bulb temperature and dew point), and user behaviour on weekdays and weekends. The test cases were conducted using historical data from ISO New England spanning the years 2004 to 2020. Moreover, the validation of the proposed model has been confirmed through comparing the results with those of Gaussian Process Regression (GPR), Regression Decision Tree (RDT), deep learning Bi-Long Short Memory (bi-LSTM), MLP, and conventional ANN. This article discusses the use of various performance indices such as MAPE, MPE, skewness, kurtosis, and risk indices for evaluating model performance. The performance of a developed model is compared with a conventional ANN model, and its performance is studied for both yearly and seasonal variations. In addition to existing indices, the article proposes two risk indices. The first is based on evaluating the standard deviation of load increment for each time, while the second is based on MC-ANN, the error between forecasted and actual loads. The risk assessment is compared between different cases such as actual load, load forecasting with ANN, and enhanced ANN-MC. Finally, the result confirms that the enhanced ANN-MC provides a higher yearly MPE value compared to other methods. In addition, it has a higher computational time than the conventional ANN-MC model, which is approximately 180.7s and 221.8s, respectively.

Personality effective factors in reducing PTSD in earthquake in order to develop a structural model in Kermanshah city

Aim: The present study was conducted with the aim of determining the role of personality factors in predicting the symptoms of post-traumatic stress disorder in people exposed to trauma. Methods: The current research method was the type of correlation designs and structural equation modeling method. The statistical population in this research included all the earthquake victims of the recent Kermanshah earthquake, whose number is about 21,000 people. According to Morgan's table, 377 people were selected as simple random sampling. The data were obtained using the Mississippi PTSD citizenship scale (Kian et al., 1988) and the five-factor personality questionnaire (Costa & McCree, 1985). In order to describe the research variables, standard deviation, average, skewness and kurtosis indices were used. Frequency and percentage indicators were also reported for demographic characteristics. It should be mentioned that SPSS software was used for descriptive analysis. In the inferential part, in order to test the hypotheses of the research, modeling of structural equations with Amos software was used. Results: The results showed that the components of adaptability, conscientiousness and openness had a negative effect on PTSD, the relationship between adaptability and PTSD was directly equal (t=-3.97 and β=-0.18), the relationship of conscientiousness It is directly equal to PTSD (t=-3.84 and β=-0.17) and the relationship of openness with PTSD is directly equal to (t=-4.49 and β=-0.20). Also, the results show that the neuroticism component had a positive effect on PTSD, the relationship between neuroticism and PTSD is directly equal (t = 9.23, β = 0.41). Conclusion: It can be concluded that the results of the statistical test showed that the fit indices of the research model are in a favorable condition. Also, the components of adaptability, conscientiousness and openness have had a negative effect on PTSD, and the component of neuroticism has had a positive effect on PTSD.

Rheological and optical response of hydroxypropyl methylcellulose under variable temperatures for optical switching based on thermo‐optical effect

AbstractThis work is devoted to investigate novel aspects of the rheological and optical response of hydroxypropyl methylcellulose (HPMC) as a result of temperature variations from 308.15 to 338.15 K. First, new insights on correlation between the solution flow behavior and the film thickness uniformity, under distinct temperatures, are established. Then, the HPMC film is examined from the point of view of some basic surface parameters describing the surface morphology, such as root mean square roughness, surface area ratio, texture direction index, kurtosis, skewness and surface bearing index. The refraction properties of HPMC are reported for the first time as a function of wavelength and temperature. The changes in the optical dispersion parameters as a function of temperature increase reveal the reduction of strength of interband optical transitions from 15.147 to 14.086 eV and a smaller band gap from 6.475 to 6.137 eV. The latter is caused by temperature‐induced dilatation of the polymer lattice and its effect on the electron–lattice interactions. The thermo‐optic coefficient of HPMC is found to be larger than that of other polymers, while its dependence on the wavelength is small, varying from −2.03 × 10−4 K−1 at 489 nm to −2.31 × 10−4 K−1. The resulted properties are corresponding to the demands imposed for materials used in optical switching based on thermo‐optical effect.

Comparing SSD-Efficient Portfolios with a Skewed Reference Distribution

Portfolio selection models based on second-order stochastic dominance (SSD) have the advantage of providing portfolios that reflect the behavior of risk-averse investors without the need to specify the utility function. Several scholars apply SSD conditions with respect to a reference distribution, typically that of the market index, to find its dominant SSD portfolio. However, since the reference distribution could strongly influence asset allocation, in this article, we compare two SSD-based portfolio selection strategies with a reshaping of the reference distribution in terms of its skewness and, consequently, its variance. Through an extensive empirical analysis based on multiasset investment universes, we empirically show that the SSD portfolios dominating the new skewed benchmark index generally perform better.

Fault diagnosis method of high voltage circuit breaker based on the combination of time-frequency multi-characteristics of acoustic signal

Aiming at the problem of accurately identifying the mechanical state of circuit breaker in the actual operation environment, a new fault diagnosis method of high voltage circuit breaker based on the combination of time-frequency multi-characteristics of acoustic signal was proposed. Firstly, the background noise database was established to remove the template noise. On this basis, the adaptive wavelet transform (AWT) was used to remove the residual noise. Then the kurtosis, crest factor and skewness indexes were extracted respectively to construct the time-domain characteristics. The acoustic signal was decomposed by variational mode decomposition (VMD) to obtain the IMF component. The power spectrum of the IMF was converted to the polar coordinates of the divided sub-region. The sensitivity of the main peak region was improved by the divergence factor, and the spectral difference entropy characteristics were calculated. The two jointly constructed the time-frequency multi-characteristics. Finally, kernel fuzzy c means (KFCM) clustering was used to pre-classify the characteristics, and then support vector machine (SVM) was used to establish training models to realize mechanical state identification. The diagnosis result shows that the accuracy of time-frequency multi-characteristics combined with KFCM-SVM diagnosis method is 98.75 %. It can reflect the status information of circuit breaker from multiple dimensions, and has high practical popularization value.

MARKETWIDE LIQUIDITY AND OPTIONS MARKET

In this paper, we study the relationship between marketwide liquidity and options market. Using the Chicago Board Options Exchange (CBOE) Volatility Index, VIX as a measure of overall value of the S&P 500 (SPX) options, and the CBOE SKEW Index as a measure of market crash risk premium in the options market, we study the relation among marketwide liquidity, VIX and SKEW. Empirical results show that higher the marketwide liquidity, less expensive the options and the less likely options traders anticipate a market crash.

A waveform skewness index for measuring time series nonlinearity and its applications to the ENSO–Indian monsoon relationship

Abstract. Many geophysical time series possess nonlinear characteristics that reflect the underlying physics of the phenomena the time series describe. The nonlinear character of times series can change with time, so it is important to quantify time series nonlinearity without assuming stationarity. A common way of quantifying the time evolution of time series nonlinearity is to compute sliding skewness time series, but it is shown here that such an approach can be misleading when time series contain periodicities. To remedy this deficiency of skewness, a new waveform skewness index is proposed for quantifying local nonlinearities embedded in time series. A waveform skewness spectrum is proposed for determining the frequency components that are contributing to time series waveform skewness. The new methods are applied to the El Niño–Southern Oscillation (ENSO) and the Indian monsoon to test a recently proposed hypothesis that states that changes in the ENSO–Indian monsoon relationship are related to ENSO nonlinearity. We show that the ENSO–Indian rainfall relationship weakens during time periods of high ENSO waveform skewness. The results from two different analyses suggest that the breakdown of the ENSO–Indian monsoon relationship during time periods of high ENSO waveform skewness is related to the more frequent occurrence of strong central Pacific El Niño events, supporting arguments that changes in the ENSO–Indian rainfall relationship are not solely related to noise.