Variance Equation Research Articles

Simulation of CO2 capture process in gas-solid bubbling fluidized bed by computational mass transfer

In this paper, a recently developed two-equation turbulent (TET) model based on the methodology of computational mass transfer (CMT) [1] is adopted to simulate the capture process of carbon dioxide by potassium carbonate particles in a gas-solid bubbling fluidized bed (BFB). The TET model uses two auxiliary equations, namely the concentration variance equation and its dissipation rate equation to determine the turbulent mass diffusion of species, which enables the simulation on mass transfer process be more rigorous than that based on empirical turbulent Schmidt number model. Comparison between the simulated results and experimental data in literature is made, which shows a satisfactory agreement (AARD% are 2.47 % and 8.45 % respectively for two cases of simulation) and thus verifies the validity of the model. Furthermore, the turbulent mass diffusion in the BFB is characterized and discussed. The calculated turbulent Schmidt number by the present model varies along axial and radial directions (with averaged value of 0.32), revealing that the use of constant turbulent Schmidt number (i.e., 0.7) is only an approximate.

The impact of human resource management practices on affective commitment: An empirical study in micro, small and medium enterprises using partial least squares structural equation modelling

Purpose: The research empirically investigates the effect of human resource management practices (HRMP), consisting of recruitment and selection, reward as well as recognition, training, performance appraisal (PA) and career development on affective commitment (AC) among employees working for micro, small and medium enterprises (MSME) in Tirupur, the commercial hub of India. This study develops a better understanding of employer–employee exchange relationships by drawing on the theoretical framework of social exchange theory.Design/methodology/approach: A structured questionnaire was employed to conduct survey-based research on 210 personnel, comprising managers, supervisors and employees. A questionnaire that used a set of 35 pre-existing items was adapted from prior studies to measure each latent variable, with slight alterations to fit the Indian context. It was then analysed with SMART PLS and the Statistical Package for the Social Sciences (SPSS), which included descriptive statistics, analysis of variance and structural equation modeling.Findings/results: The findings established a substantial positive association between AC and component-by-component evaluation of HRMP. In comparison, career advancement was observed to be the most important predictor of AC, followed by reward and recognition.Practical implications: Micro, small and medium enterprises are the fastest-growing revenue-generating and job-creating sector. Therefore, well-designed and executed HRMP in these organisations will nurture AC by lowering turnover besides absenteeism, while improving employee performance. This research will be extremely useful to stakeholders in any emerging nation who seek to expedite their organisation’s development by strengthening human-resource competencies.Originality/value: This study is unique in the context of developing countries, as the impact of HRMP on AC in MSME has not been well addressed. The author’s contribution extends to the scant literature available on MSMEs.

COVID-19 and prices of pulses in Major markets of India: Impact of nationwide lockdown.

The COVID-19 pandemic has impacted almost all the sectors including agriculture in the country. The present paper investigates the impact of COVID-19 induced lockdown on both wholesale and retail prices of major pulses in India. The daily wholesale and retail price data on five major pulses namely Lentil, Moong, Arhar, Urad and Gram are collected for five major markets in India namely Delhi, Mumbai, Kolkata, Chennai and Hyderabad during the period January, 2019 to September, 2020 from Ministry of Consumer Affairs, Food & Public Distribution, Government of India. The Government of India declared nationwide lockdown since March, 24, to May, 31, 2020 in different phases in order to restrict the spread of the infection due to COVID-19. To see the impact of lockdown on price and price volatility, time series model namely Autoregressive integrated moving average (ARIMA) model with error following Generalized autoregressive conditional heteroscedastic (GARCH) model incorporating exogenous variable as lockdown dummy in both mean as well variance equations. It is observed that in almost all the markets, lockdown has significant impact on price of the pulses whereas in few cases, it has significant impact on price volatility.

Augmenting the Realized-GARCH: the role of signed-jumps, attenuation-biases and long-memory effects

Abstract This paper extends the Realized-GARCH framework, by allowing the conditional variance equation to incorporate exogenous variables related to intra-day realized measures. The choice of these measures is motivated by the so-called heterogeneous auto-regressive (HAR) class of models. Our augmented model is found to outperform both the Realized-GARCH and the various HAR models in terms of in-sample fitting and out-of-sample forecasting accuracy. The new model specification is examined under alternative parametric density assumptions for the return innovations. Non-normality seems to be very important for filtering the return innovations to which variance responds and helps significantly upon the prediction performance of the suggested model.

Examination of digital citizenship, online information searching strategy and information literacy depending on changing state of experience in using digital technologies during COVID-19 pandemic

As educational processes are adapted to the online environment due to COVID-19 pandemic, digital citizenship and online information searching strategies came into prominence. In this context, the aim of this study is the examination of digital citizenship awareness, online information searching strategies and differentiation of the level of information literacy depending on changing state of experience in using digital technologies before and during COVID-19 pandemic. Also, it is aimed to examine the presence of relations between variables of the research. The study was conducted with the participation of 255 university students. Analyses were performed using multivariate analysis of variance (MANOVA) and partial least squares structural equation modelling (PLS-SEM). According to the results of the study, during COVID-19 pandemic, there was an increase in digital citizenship awareness of students who spend more time in social media and the ones who improved digital technology usage skills developed online information searching strategies. According to the model analysed, online information searching strategies and information literacy have an effect on digital citizenship while online information searching strategies have an effect on information literacy. The results of the study showed that the students with higher level of information literacy and online information searching strategies may help them develop digital citizenship awareness.

A New K–ε Turbulence Parameterization for Mesoscale Meteorological Models

Abstract A new one-dimensional 1.5-order planetary boundary layer (PBL) scheme, based on the K–ε turbulence closure applied to the Reynolds-averaged Navier–Stokes (RANS) equations, is developed and implemented within the Weather Research and Forecasting (WRF) Model. The new scheme includes an analytic solution of the coupled equations for turbulent kinetic energy and dissipation rate. Different versions of the PBL scheme are proposed, with increasing levels of complexity, including a model for the calculation of the Prandtl number, a correction to the dissipation rate equation, and a prognostic equation for the temperature variance. Five different idealized cases are tested: four of them explore convective conditions, and they differ in initial thermal stratification and terrain complexity, while one simulates the very stable boundary layer case known as GABLS. For each case study, an ensemble of different large-eddy simulations (LES) is taken as reference for the comparison with the novel PBL schemes and other state-of-the-art 1- and 1.5-order turbulence closures. Results show that the new PBL K–ε scheme brings improvements in all the cases tested in this study. Specifically, the more significant are obtained with the turbulence closure including a prognostic equation for the temperature variance. Moreover, the largest benefits are obtained for the idealized cases simulating a typical thermal circulation within a two-dimensional valley. This suggests that the use of prognostic equations for dissipation rate and temperature variance, which take into account their transport and history, is particularly important with the increasing complexity of PBL dynamics.

Exploring the predictors of teacher retention among beginning teachers in Ethiopia

There is a global challenge to retain qualified beginning teachers across the school system. In the first 10 years of entering the profession, a large number of beginning teachers exit altogether. While high-income countries have been designing policies and discussing effective ways to retain beginning teachers, the African share of such deliberation is scanty. Using Mason and Matas’ (2015) four-capital teacher retention model (structural, social, psychological and human capital) as a conceptual framework, we studied the predictors of retention of beginning teachers in Ethiopia. A questionnaire designed on the basis of the tenets of the conceptual framework was used for the data collection. In total, 204 beginning teachers who had graduated from elite teacher training institutions were recruited for this study. The data were analysed using t-tests, analysis of variance and structural equation modelling to estimate the strengths of the relationships and direct logistical and linear regressions. The results show a positive relationship between the four indicators and the demographic variables, such as gender, pre-service training, access to professional development, level of teaching and living with family in the community, which provided additional insight into teacher retention. We discuss the need for a multifaceted approach to retaining beginning teachers in Ethiopia as well as other implications.

Pay Senedi Endeksleri ile Endeks Vadeli İşlemler Arasındaki Volatilite İlişkisi: Türkiye ve Dünya Örnekleri Arasında Karşılaştırmalı Analiz

Bu çalışmanın konusunu, pay senedi endeksleri ile bu endekslerin dayanak varlık olduğu vadeli işlemler arasındaki volatilite ilişkisinin analizi oluşturmaktadır. Bu çalışma, spot endeksler ile endeks vadeli işlemler arasındaki volatilite ilişkisinin yönünü tespit etmeyi amaçlamaktadır. Bu çalışmanın temel amacı, Türkiye örneğinde yüksek işlem hacmi göz önünde bulundurularak BIST 30 endeksi ve BIST 30 endeks vadeli işlemleri arasındaki volatilite ilişkisinin ekonometrik yöntemlerle incelenmesi, elde edilen verilerin Dünya’daki önemli endekslerle karşılaştırılması ve analiz sonuçlarının değerlendirilmesidir. Bu amaçla, BIST 30 endeksi ve BIST 30 endeks vadeli işlemleri arasında herhangi bir volatilite ilişkisi olup olmadığı hususu ekonometrik yöntemlerle analiz edilerek, Dünya örnekleri ile kıyaslamalar yapılmıştır. Dünyanın gelişmiş borsalarında işlem gören DAX 30 ve S&P 500 spot piyasa endeksleri ile DAX 30 ve S&P 500 endeks vadeli işlemleri arasındaki volatilite ilişkisine bakılarak Türkiye örneği ile karşılaştırmalar yapılmıştır. Getiri ve işlem hacimlerine ilişkin oynaklık tahminleri için Otoregresif Koşullu Değişen Varyans (ARCH) Modelleri kullanıldığından, spot endeksler ile endeks vadeli işlemler arasındaki volatilite ilişkisi GARCH, TARCH, EGARCH ve PARCH modelleri ile analiz edilmiştir. Uygulama aşamasında elde edilen bulgular, 2006-2021 yıllarını kapsayan dönemde her iki piyasa arasında çift yönlü volatilite ilişkisi olduğunu ortaya çıkarmıştır. Ancak bu çift yönlü ilişki, koşullu varyans denklemindeki açıklayıcı değişkenlerinin katsayılarının çok küçük olması nedeniyle istatistiksel olarak çok önem arz etmemektedir.

Development of arsenic removal unit with electrocoagulation and activated alumina sorption: Field trial at rural West Bengal, India

In the present study, electrocoagulation (EC) with iron electrodes along with adsorption by activated alumina has been evaluated as a treatment process for arsenic removal from groundwater. Batch scale experiments were performed with iron electrodes to assess the removal efficiency of electrocoagulation. The effects of inter-electrode distance and electro-charge loading (ECL) were evaluated on arsenic removal. EC was able to decrease the arsenic concentration in the aqueous phase below 10 ppb at ECL of 60 coulomb/L with 1 cm inter-electrode distance. The optimal condition was obtained by Response Surface Methodology (RSM) with the help of central composite design (CCD). The regression equation and analysis of variance were originated using Design-Expert software and F value (131.56), p-value (<0.05), and desirability value of 1 were obtained for the optimized model. Activated alumina adsorption has also been shown to be a promising arsenic removal technology (As5+) with 15 min of equilibrium time. An arsenic removal system was designed with EC unit, sand filter, and activated alumina column in series for field application. The field unit was able to deliver safe water to the local community. During the operation, the electrodes were passivated, and calcium-based deposition was found on the cathodes. Periodic electrode reversal was able to minimise the passivation. Due to the accumulation of ferric hydroxide flocs, the pores of the filter bed become clogged and they must be cleaned by backwashing. The Backwash system should be continued until the water runs clear. An arrangement of a suitable soak pit is made to contain the backwash water avoiding any environmental contamination. EC could be able to deliver safe water locally in rural areas at affordable prices due to its high efficiency and low operation and maintenance costs.

Revisiting and revising Tatarskii’s formulation for the temperature structure parameter (C_T^2) in atmospheric flows

In this paper, we revisit a well-known formulation of temperature structure parameter (C_T^2), originally proposed by V. I. Tatarskii. We point out its limitations and propose a revised formulation based on turbulence variance and flux budget equations. Our formulation includes a novel physically-based outer length scale which can be estimated from routine meteorological data.

Regime Shift Behavior and Interconnectedness of Stock Market Volatility and Exchange Rate Movements: Empirical Evidence from Emerging and Developed Economies

Purpose: The purpose of this study is to examine the regime shift behavior and interconnectedness between stock market volatility and exchange rate in a pre and post COVID context.&#x0D; Design/Methodology/Approach: For this purpose Pakistan India and China has been taken as emerging economies and USA UK and Japan as developed economies. Daily data of exchange rates and stock market indices have been used from Jan,1 2011 to Apr 30, 2021. Markov regime switching model, GARCH model and Granger causality test has been used.&#x0D; Findings: The outcomes of this study empirically confirmed that the Markov regime switching model is flexible model because it captures regime shifts in the mean and variance equation of all sample economies. Moreover, the sum of GARCH (1,1) indicates that volatility shocks are persistent except India. Study also concluded that exchange rate volatility has significant positive impact on stock market of Pakistan, USA and Japan in a pre COVID context.&#x0D; Implications/Originality/Value: A post COVID context has significant impact on the stock market of India, U.S.A. and U.K. Granger causality results indicates unidirectional relationship for Pakistan, China and USA. Whereas for Japan bi-directional relationship is found but for India and UK no directional causality exist.

Effects of Multiple Financial News Shocks on Tourism Demand Volatility Modelling and Forecasting

Even though both symmetric and asymmetric conceptions of news impacts are well-established in the disciplines of economics and financial markets, the effects of combining multiple news shocks on the volatility of tourism demand have not yet been delved into or gauged in any tourist destination. This work hypothesises and verifies that the news impact curve (NIC), conditional heteroscedastic volatility models, and multiple news shocks are suitable for forecasting the volatility of the Malaysian tourist industry. Among them, three primarily volatility models (GARCH, EGARCH, and GJRGARCH) are used in conjunction with five financial news shocks (FFNSs), namely the Kuala Lumpur Composite Index (KLCI), the United States Dollar Index (DXY), the stock performance of 500 large companies listed on stock exchanges (S&amp;P500), Crude Oil (CO), and Gold Price (GP). Among the most significant findings of this study are the demonstration of monthly seasonality using conditional mean equations, asymmetry effects in EGARCH-FFNSs, and GJRGARCH-FFNSs models in conditional variance equations and 50 NICs, and the GARCH-FFNSs model’s evaluation of the persistence influence of news shocks on monthly visitor arrivals in Malaysia. The GJRGARCH-FFNSs model is the best model for Malaysian tourism demand volatility forecasting accuracy. Furthermore, KLCI and Gold Price have the most substantial impact on the number of tourists to Malaysia. In addition, it should be emphasised that the methodological framework utilised in this study can be a useful tool for creating and forecasting the performance of symmetry and asymmetry impacts on tourism demand volatility.

Trade, equilibrium prices and rents in European auctions for emission allowances

This paper analyzes the price behavior of Phase III (2013–2020) EU-ETS emission allowances of CO2 by focusing on the dynamics of daily auction equilibrium prices and on the changes of the volatility of the underlying stochastic process. The paper derives the main testable statistical hypotheses (particularly that about the determinants of the conditional variance of prices) on the results derived in a model of optimal bidders’ behavior given the ETS auction rules. Tests are conducted by estimating various versions of GARCH models for both mean and variance equations of price return. Results show that the price dynamics is affected by factors including a measure of excess demand/offer and the number of winning bidders and that, contrary to expectations, reforms of the auction rules introduced at the end of Phase III explain a great part of the increased price volatility. The increased volatility is also positively associated with the bid spread and negatively associated with the number of bidders active in each auction round.

Evaluation of flamelet/progress variable model for the applications in supersonic combustion using hybrid RANS/LES approach

The Flamelet/Progress Variables (FPV) model coupled with large eddy simulation (LES) has been performed to appraise its applicability and performance for supersonic hydrogen combustion, based on the simulations of strut based scramjet and cavity based combustors. For the two typical schemes involved in the present work, the validation of the numerical solver compiled by in-house code is realized by the comparisons with the experimental measurements including temperature, velocity, and wall pressure. The methods for calculating mixture fraction variance in the framework of LES are also investigated and examined in both cases. It can be observed that the diffusion combustion mode is in the dominant state for the whole reactive zone in both cases, where the FPV model is physically compatible. Through the comparisons of the scale similarity method and the transport equation method for calculating mixture fraction variance, the importance of calculating this sub-grid turbulent scalar flux is emphasized. The results show that the mixture fraction variance calculated by the scale similarity model is locally high, but its diffusion is insufficient, which makes the local temperature of the combustion flow field overpredicted and concentrated. On the contrary, the transport equation of the mixture fraction variance can overcome this problem and improve the combustion prediction accuracy, thus, it is more recommended if the computing resources are guaranteed. Qualitatively, the Flamelet/Progress Variables (FPV) model with the large eddy simulation approach can accurately capture the structure and characteristics of the supersonic combustion flow field.

Response surface methodology adhering central composite design for the optimization of Zn (II) adsorption using rice husk nanoadsorbent

Present study highlights the effectuality of rice husk nanoadsorbent RHN for the removal of Zn (II) from aqueous solution. A peculiar statistical technique central composite design of response surface methodology by using Design-Expert® software version 11 was executed to investigate Zn (II) removal by varying three adsorption parameters such as pH, dose and metal ion concentration. Quadratic model equation and analysis of variance were outlined in the study. Zn (II) removal was found to be 80% at optimum conditions pH 6, dose 0.8 g/L and metal ion concentration 15 mg/L and desirability was near 0.9. To comprehend the nature of adsorption process, linear forms of isotherms were gratified. Langmuir isotherm model was found to be compatible with adsorption data indicating monolayer adsorption on homogeneous active sites of nanoadsorbent with 0.9978 R2. Pseudo-second order kinetic model fitted well to the adsorption data with R2 0.992. Spontaneous and endothermic nature of the process was depicted by thermodynamic study. Adsorption mechanism is reasonably explained by hydrolysis, precipitation, electrostatic interactions and HASB inreaction. The regeneration of RHN was analysed upto three successive cycles.

GRG Non-Linear and ARWM Methods for Estimating the GARCH-M, GJR, and log-GARCH Models

Numerous variants of the basic Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models have been proposed to provide good volatility estimating and forecasting. Most of the study does not work Excel’s Solver to estimate GARCH-type models. The first purpose of this study is to provide the capability analyze of the GRG non-linear method built in Excel’s Solver to estimate the GARCH models in comparison to the adaptive random walk Metropolis method in Matlab by own codes. The second contribution of this study is to evaluate some characteristics and performance of the GARCH-M(1,1), GJR(1,1), and log-GARCH(1,1) models with Normal and Student-t error distributions that fitted to financial data. Empirical analyze is based on the application of models and methods to the DJIA, S&amp;P500, and S&amp;P CNX Nifty stock indices. The first empirical result showed that Excel’s Solver’s Generalized Reduced Gradient (GRG) non-linear method has capability to estimate the econometric models. Second, the GJR(1,1) models provide the best fitting, followed by the GARCH-M(1,1), GARCH(1,1), and log-GARCH(1,1) models. This study concludes that Excel’s Solver’s GRG non-linear can be recommended to the practitioners that do not have enough knowledge in the programming language in order to estimate the econometrics models. It also suggests to incorporate a risk premium in the return equation and an asymmetric effect in the variance equation.

Forecasting electricity prices with expert, linear, and nonlinear models

This paper compares several models for forecasting regional hourly day-ahead electricity prices, while accounting for fundamental drivers. Forecasts of demand, in-feed from renewable energy sources, fossil fuel prices, and physical flows are all included in linear and nonlinear specifications, ranging in the class of ARFIMA-GARCH models—hence including parsimonious autoregressive specifications (known as expert-type models). The results support the adoption of a simple structure that is able to adapt to market conditions. Indeed, we include forecasted demand, wind and solar power, actual generation from hydro, biomass, and waste, weighted imports, and traditional fossil fuels. The inclusion of these exogenous regressors, in both the conditional mean and variance equations, outperforms in point and, especially, in density forecasting when the superior set of models is considered. Indeed, using the model confidence set and considering northern Italian prices, predictions indicate the strong predictive power of regressors, in particular in an expert model augmented for GARCH-type time-varying volatility. Finally, we find that using professional and more timely predictions of consumption and renewable energy sources improves the forecast accuracy of electricity prices more than using predictions publicly available to researchers.

Nexus between asymmetric information and stock market volatility: evidence from Sri Lanka

This study examines the impact of asymmetric information behaviour and macroeconomic variability in modelling the stock market volatility. CSE market does not show the characteristics which may potentially lead to larger volatility shocks. The idiosyncratic volatility is more subjected to the irrational investment decisions with the absence of relevant market information. Therefore, the information asymmetries motivate investors to highly depend on irrational reasons which lead to irrational volatility shocks. The variance equation of the EGARCH model was applied for identifying the impact of the asymmetric information behaviour. The mean-variance equation of EGARCH has been modelled with GDP, inflation, interest rate, and money supply for recognizing macroeconomic impacts. The study finds that the CSE market was significantly experiencing asymmetric information problem. As a result, uninformed investors make their decision based on the market sentiment creating irrational price volatilities. The mean-variance equation shows that macroeconomic variability has a significant impact on explaining the future asymmetric conditional volatility. However, CSE volatility spends a few weeks to adjust the relevant macroeconomic shocks.

The impact of COVID-19 on stock market returns: Empirical evidence from Nigeria

The study investigates the causal relationship between COVID-19 and changes in the prices and volume of stocks in the Nigerian stock market, to identify whether there is a short and or long-run relationship between changes in the Nigerian Stock Exchange All Share Index 30 (NSE ASI 30) and NSE 30 traceable to the outbreak and continued presence of the coronavirus-19 diseases (COVID-19) during the period 31 December 2019 to June 30, 2020.&#x0D; The paper seeks to estimate the effect of the COVID-19 shocks on the volatilities of returns in the NSE30 and ASI 30 stocks. After a property check on the times series data, a correlation matrix was drawn to understand the relationship between stock returns in NSE30 and ASI30 with total cases, test units, new cases, female and male smokers, and COVID-9 deaths. The ADF results helped us in selecting what the use in the representative model.&#x0D; We then applied the Lagrange Multiplier (LM) test on the residuals of both the mean and the variance equations of the NSE30 and the Generalized Autoregressive Conditional Heteroscedastic (GARCH) to estimate the short and long-run return spillovers and conditional correlations between the shock from COVID-19 and stock market returns.&#x0D; Generally, the results indicated a weak impact of COVID-19 on the returns and volume of both the NSE30 and NSE ASI 30 stocks. This is so because of some data issues and issues relating to empirics. More data will be sought, and a proper review of the paper undertaken, to ascertain its usefulness for policy.&#x0D; The study concludes that to spur economic growth in COVID-19, Nigeria’s economic managers, particularly, the monetary, fiscal, and capital market regulators must learn to work as a team, to ensure complementary in their policies and thus, propel the economy out of a likely recession.

Effects of Conditional Oil Volatility on Exchange Rate and Stock Markets Returns

The underlying volatility at a given time is called conditional volatility at this particular time and is modeled by various ARMA-GARCH conditional variance equations (GARCH, EGARCH, GJR, APARCH, IGARCH). How important are oil price fluctuations and oil price volatility in foreign exchange markets and stock markets? What is the nature of the relationship between these three markets? What are the political implications if volatility, using appropriate models to determine, turns out to be important? We evaluate these questions empirically, using the specification of Narayan and Narayan (2010). This specification, in our paper, deals with the determination of volatility appropriate models, based on information criteria, of the ARMA-ARCH family conditional volatility of oil returns using daily data for each country independently (i), and revolve around an analysis of the effect of the volatility of black gold price on the returns of the other two markets in Oil Importing Developed Countries category (ii). The selection of appropriate models of oil returns according to the period of the chosen data gives the ARMA(2,2)- GJR(1,2) model for the Germany and the ARMA(2,2)-GJR(2,2) model for the Japan and the USA. The results that the conditional variances of oil returns, foreign exchange market returns and stock market returns are contested and they have a long-term relationship in different countries. In addition, the results of the granger causality tests and the study of impulse response functions have shown that it has a sending effect of the volatility of oil prices on most foreign exchange markets and stock markets, highlighting the strong explanatory power of market volatility, but bidirectional causality is not always present. Our empirical results involved in the prevention of shocks are important for policymakers, for portfolio managers seeking optimal portfolio allocation, for monetary authorities who are studying changes in the exchange rate of the national currency against currencies, for oil-importing countries seeking to minimize their spending on crude oil, and for oil-exporting countries seeking the sound management of oil reserves. They also show that the volatility of crude oil prices on the world market is generally more significant for foreign exchange and stock markets than the volatility of oil price in the local market. This main conclusion gives political implications to policymakers.