Examining country development indicators and e-waste under the moderating effect of country development levels and e-waste policy

PurposeThe purpose of this paper is to investigate if country development indicators, i.e. gross domestic product per capita (GDPPC), literacy rate, internet penetration and urban population, influence the generation of e-waste on a global level. The moderation effect due to differences between countries in terms of absence or presence of e-waste policy and level of development is also checked.Design/methodology/approachThis is an archival study that builds upon data from United Nations (UN), World Bank and Global E-waste Statistics Partnership. The authors did a path analysis comprising mediation and multigroup analyses to decipher the proposed rese arch model containing data from 172 countries.FindingsThe results indicate that GDPPC, literacy rate, internet penetration and urban population do not directly influence the generation of e-waste. However, higher internet penetration in developing countries leads to higher e-waste, while higher literacy rates in developed countries suppress e-waste generation. When it comes to e-waste policy, a higher urban population without a regulatory legal framework boosts higher e-waste. The authors observed that higher internet penetration leads to higher e-waste in the presence of e-waste policy as well.Originality/valueThis is the first study to include economic well-being indicators in elaborating e-waste generation, on a global scale. No previous study has observed differences between countries nested in e-waste policy and level of development.

Evaluating the Three-Dimensional Printing Accuracy of Partial-Arch Models According to Outer Wall Thickness: An In Vitro Study

The printing accuracy of three-dimensional (3D) dental models using photopolymer resin affects dental diagnostic procedures and prostheses. The accuracy of research into the outer wall thickness and printing direction data for partial-arch model printing has been insufficient. This study analyzed the effects of wall thickness and printing direction accuracy. Anterior and posterior partial-arch models were designed with different outer wall thicknesses. After 3D printing, a trueness analysis was performed. Those with full-arch models were the control group. The full-arch model had an error value of 73.60 ± 2.61 µm (mean ± standard deviation). The error values for the partial-arch models with 1-, 2-, and 3-mm thick outer walls were 54.80 ± 5.34, 47.58 ± 7.59, and 42.25 ± 9.19 μm, respectively, and that for the fully filled model was 38.20 ± 4.63 μm. The printing accuracies differed significantly between 0 degrees and 60 degrees, at 49.54 ± 8.16 and 40.66 ± 6.80 μm, respectively (F = 153.121, p < 0.001). In conclusion, the trueness of the partial-arch model was better than that of the full-arch model, and models with thick outer walls at 60 degrees were highly accurate.

Political risks and financial markets: emerging vs developed economies

PurposeThe authors provide a comparative analysis between emerging and developed financial markets in terms of the effects of political risks on stock market returns and volatility. The authors also examine whether this impact depends on the nature of political risks. Therefore, this study aims to detect which financial markets are the most profitable and the riskiest in terms of political risks.Design/methodology/approachThe authors investigate the impact of political risks on the excess stock market return and its conditional volatility using the generalized ARCH model for a sample of 46 developed and emerging markets over a period ranging from 1995 to 2019. In order to test how the nature of political risks affects equity excess returns and volatility differently in different markets, the authors employ (1) a composite political risk score, (2) the four subgroups of political risks as defined by Bekaert et al. (2005, 2014) and (3) the individual dimensions of political risks.FindingsThe findings indicate that the composite political risk is priced into both stock markets. The effect of political risks is positive for excess returns and negative for volatility. The authors show that the political risk leads to more volatility in developed markets. Nevertheless, the effect of individual components varies according to the market category.Practical implicationsThe authors provide a framework for predicting market returns and volatility using changes in the political risk of the country. The findings help investors make investment decisions based on the political decisions of governments. In other words, investors should consider political uncertainty when determining their expected earnings.Originality/valueThe authors engage monthly panel data methodology in terms of the political risk stock market relationship. In addition, the authors consider recent and very long data covering the period 1995–2019. Furthermore, this study combines three various political risk measures, and both equity returns and volatility.

Penerapan Model ARIMA-ARCH untuk Meramalkan Harga Saham PT. Indofood Sukses Makmur Tbk

Abstract– The stock price is the value of the stock in the market that fluctuates from time to time. Time series data in the financial sector generally have quite high volatility which can cause heteroscedasticity problems. This study aims to model and to predict the stock price of PT Indofood Sukses Makmur Tbk using the ARIMA-ARCH model. The data used is daily stock prices from 2nd June 2020 to 15th February 2021 as training data, while from 16th February 2021 to 1st March 2021 as testing data. ARIMA-ARCH model is a model that combines Autoregressive Integrated Moving Average (ARIMA) and Autoregressive Conditional Heteroscedasticity (ARCH), which can be used to overcome the residues of the ARIMA model which are indicated to have heteroscedasticity problems. The result showed that the model that could be used was ARIMA(1,1,2)-ARCH(1). This model can provide good forecasting result with a relatively small MAPE value of 0.515785%. Abstrak– Harga saham adalah nilai saham di pasar yang berfluktuasi dari waktu ke waktu. Data runtun waktu di sektor keuangan umumnya memiliki volatilitas cukup tinggi yang dapat menyebabkan masalah heteroskedastisitas. Penelitian ini bertujuan untuk memodelkan dan meramalkan harga saham PT Indofood Sukses Makmur Tbk menggunakan model ARIMA-ARCH. Data yang digunakan adalah harga saham harian dari 2 Juni 2020 hingga 15 Februari 2021 sebagai data training, sedangkan dari 16 Februari 2021 hingga 1 Maret 2021 sebagai data testing. Model ARIMA-ARCH merupakan suatu model yang menggabungkan Autoregressive Integrated Moving Average (ARIMA) dan Autoregressive Conditional Heteroscedasticity (ARCH), yang dapat digunakan untuk mengatasi residu dari model ARIMA yang terindikasi memiliki masalah heteroskedastisitas. Hasil penelitian menunjukkan bahwa model yang dapat digunakan adalah ARIMA(1,1,2)-ARCH(1). Model tersebut mampu memberikan hasil peramalan yang baik dengan perolehan nilai MAPE yang relatif kecil yaitu 0,515785%.

Analysis On the In-Plane 2:2:1 Internal Resonance of a Complex Cable-Stayed Bridge System Under External Harmonic Excitation

Nonlinear dynamic analysis of a cable-stayed bridge has been a hot topic due to its structural flexibility. Based on integro-partial differential equations of a double-cable-stayed shallow-arch model, in-plane 2:2:1 internal resonance among three first in-plane modes of two cables and a shallow arch under external primary or subharmonic resonance is considered. Galerkin's method and the method of multiple scales are used to derive averaged equations of the cable-stayed bridge system. Nonlinear dynamic behaviours of the system are investigated via the numerical simulation. Results show rich nonlinear phenomena of the cable-stayed bridge system and some new phenomena are observed. Two identical cables that are symmetrically located above the shallow arch can have different dynamic behaviours even when initial conditions of the system are symmetrically given. Two cables with some differences between their parameters can exhibit either softening or hardening characteristics.

GSTARI-ARCH MODEL AND APPLICATION ON POSITIVE CONFIRMED DATA FOR COVID-19 IN WEST JAVA

Time series model that is commonly used is the Box-Jenkins based time series model. Time series data phenomena based on Box-Jenkins can be combined with spatial data, it is called the space time model One model based on Box-Jenkins model with heterogeneous location characteristics is the Generalized Space Time Autoregressive Integrated (GSTARI) model for a model that assumes data is not stationary or has a trend. This paper discusses the development of the GSTARI model with the assumption that the error variance is not constant which is applied to positive data confirmed by Covid-19 in West Java Province, especially in 4 regencies/cities that have cases in the high category from 6 March 2020 until 31 December 2020. Four regencies/cities are Depok City, Bekasi City, Bekasi Regency, and Karawang Regency. Parameter estimation method for the assumption of non-constant error variance can use Autoregressive Conditional Heteroscedasticity (ARCH) method. GSTARI-ARCH modeling procedure followed three Box-Jenkins stages, namely the identification process, parameter estimation and checking diagnostic. Application of the GSTARI-ARCH Model to Covid-19 positive confirmed data in 4 regencies/cities has a minimum value of RMSE in Bekasi City. The plot of forecast results for the four regencies/cities has a similar pattern to the actual data only applicable for a short time for 1-2 days.

On behavioral Arrow Pratt risk process with applications to risk pricing, stochastic cash flows, and risk control

We introduce a closed form behavioural stochastic Arrow-Pratt risk process, decomposed into discrete asymmetric risk seeking and risk averse components that run on different local times in ϵ-disks centered at risk free states. Additionally, we embed Arrow-Pratt (“AP”) risk measure in a simple dynamic system of discounted cash flows with constant volatility, and time varying drift. Signal extraction of Arrow-Pratt risk measure shows that it is highly nonlinear in constant volatility for cash flows. Robust identifying restrictions on the system solution confirm that even for small time periods constant volatility is not a measure of AP risk. By contrast, time-varying volatility measures aspects of embedded AP risk. Whereupon maximal AP risk measure is obtained from a convolution of input volatility and idiosyncratic shocks to the system. We provide four applications for our theory. First, we find that Engle, Ng and Rothschild (1990) Factor-ARCH model for risk premia is misspecified because the factor price of risk is time varying and unstable. Our theory predicts that a hyper-ARCH correction factor is required to remove the Factor-ARCH specification. Second, when applied to analysts beliefs about interest rates and volatility, we find that AP risk measure is a feedback control over stochastic cash flows. Whereupon increased risk aversion to negative shocks to earnings increases volatility. Third, we use an oft cited example of Benes, Shepp and Witsenhausen (1980) to characterize a controlled AP diffusion for a conservative investor who wants to minimize the AP risk process for an asset. Fourth, we recover stochastic differential utility functional from the AP risk process and show how it is functionally equivalent to Duffie and Epstein’s (1992) parametrization.

MODELLING OF OIL PRICE VOLATILITY USING ARIMA-GARCH MODELS F. Merabet, H. Zeghdoudi, R. H Yahia, and I. Saba

In this paper, the behavior of the oil price series named OIL is examined. The non-stationarity on average and variance, with the non-normality of the OIL series distribution, indicate the volatility of the series. The study is based on a combination of the Box-Jenkins methodology with the GARCH processes (Engle and Bollerslev). The first part models the lnOIL series in which, by applying the first difference the series becomes DlnOIL. Then the Box-Jenkins methodology is applied. The choice of the model was made on basis of minimization of criterion -Akaike (AIC), Shwarz (SIC)- and maximization of log likelihood (LL). Of the four models identified, ARMA (3.1) is retained. According to the statistical indicators of the ARMA model (3,1), the nature of the residuals and other tests, it is shown that the series of squares of the residuals follows a conditionally heteroscedastic ARCH model. The second part is devoted to a symmetrical and asymmetrical GARCH modelling. The model used for predicting volatility is the EGARCH model (1,2). The data available relates to 3652 daily values of the change in OIL, from 01/01/2019 to 12/31/2019. The forecast is made for the first three months of 2020; the result concludes that the predicted values and the current values are very close, and that the model ARIMA (3,1,1) + EGARCH (1,2) is the best forecast model.

Complete-Arch Accuracy of Four Intraoral Scanners: An In Vitro Study

The purpose of this study is to define the accuracy of four intraoral scanners (IOS) through the analysis of digital impressions of a complete dental arch model. Eight metal inserts were placed on the model as reference points and then it was scanned with a laboratory scanner in order to obtain the reference model. Subsequently, the reference model was scanned with four IOS (Carestream 3600, CEREC Omnicam, True Definition Scanner, Trios 3Shape). Linear measurements were traced on an STL file between the chosen reference points and divided into four categories: three-element mesiodistal, five-element mesiodistal, diagonal, and contralateral measurements. The digital reference values for the measurements were then compared with the values obtained from the scans to analyze the accuracy of the IOS using ANOVA. There were no statistically significant differences between the measurements of the digital scans obtained with the four IOS systems for any of the measurement groups tested.