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Ghosh, Himadri, G. Sunilkumar, and Prajneshu. "Mixture Nonlinear Time-Series Analysis : Modelling and Forecasting." Calcutta Statistical Association Bulletin 57, no. 1-2 (March 2005): 95–108. http://dx.doi.org/10.1177/0008068320050108.
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Gaussian mixture transition distribution (GMTD) models and mixture autoregressive (MAR) models are generally employed to describe those data sets that depict sudden bursts, outliers and flat stretches at irregular time epochs. In this paper , these two approaches are compared by considering weekly wholesale onion price data during April, 1998 to November, 2001. After eliminating trend, seasonal fluctuations are studied by fitting BoxJenkins airline model to residual series. To this end, null hypothesis of presence of nonseasonal and seasonal stochastic trends is tested by using OsboruChuiSmithBirchenhall (OCSB) auxiliary regression. Subsequently, appropriate filters in airline model for seasonal fluctuations are selected. Presence of autoregressive co nditional heteroscedasticity (ARCH) is tested by Naive Lagrange multiplier (Nave LM) test. Estimation of parameters is carric~d out using ExpectationMaximization (EM) algorithm and the best model is selected on the basis of Bayesian information criterion (BIC). Outofsample forecasting is performed for onestep and twostep ahead prediction by uaive approach, proposed by Wong and Li (2000). It is concluded that, for data under consideration, a threecomponent MAR model performs the best.
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Kokoszka, Piotr, Gregory Rice, and Han Lin Shang. "Inference for the autocovariance of a functional time series under conditional heteroscedasticity." Journal of Multivariate Analysis 162 (November 2017): 32–50. http://dx.doi.org/10.1016/j.jmva.2017.08.004.
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Hasanah, Primadina, Siti Qomariyah Nasir, and Subchan Subchan. "Gold Return Volatility Modeling Using Garch." Indonesian Journal of Mathematics Education 2, no. 1 (April 30, 2019): 20. http://dx.doi.org/10.31002/ijome.v2i1.1222.
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<p class="JRPMAbstractBodyEnglish">This research aims to resolve the heteroscedasticity problem in time series data by modeling and analyzing volatility the gold return using GARCH models. Heteroscedasticity means not the constant variance of residuals. The sample data is a return data from January 1, 2014 to September 23, 2016. The data analysis technique used is a stationary test, model identification, model estimation, diagnostic check, heteroscedasticity test, GARCH model estimation, and evaluation. The results showed that ARIMA (3,0,3)-GARCH (1.1) is the best model.</p>
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Kasse, Irwan, Andi Mariani, Serly Utari, and Didiharyono D. "Investment Risk Analysis On Bitcoin With Applied of VaR-APARCH Model." JTAM (Jurnal Teori dan Aplikasi Matematika) 5, no. 1 (April 17, 2021): 1. http://dx.doi.org/10.31764/jtam.v5i1.3220.
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Investment can be defined as an activity to postpone consumption at the present time with the aim to obtain maximum profits in the future. However, the greater the benefits, the greater the risk. For that we need a way to predict how much the risk will be borne. Modelling data that experiences heteroscedasticity and asymmetricity can use the Asymmetric Power Autoregressive Conditional Heteroscedasticity (APARCH) model. This research discusses the time series data risk analysis using the Value at Risk-Asymmetric Power Autoregressive Conditional Heteroscedasticity (VaR-APARCH) model using the daily closing price data of Bitcoin USD period January 1 2019 to 31 December 2019. The best APARCH model was chosen based on the value of Akaike's Information Criterion (AIC). From the analysis results obtained the best model, namely ARIMA (6,1,1) and APARCH (1,1) with the risk of loss in the initial investment of IDR 100,000,000 in the next day IDR 26,617,000. The results of this study can be used as additional information and apply knowledge about the risk of investing in Bitcoin with the VaR-APARCH model.
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WU, EDMOND H. C., PHILIP L. H. YU, and W. K. LI. "VALUE AT RISK ESTIMATION USING INDEPENDENT COMPONENT ANALYSIS-GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (ICA-GARCH) MODELS." International Journal of Neural Systems 16, no. 05 (October 2006): 371–82. http://dx.doi.org/10.1142/s0129065706000779.
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We suggest using independent component analysis (ICA) to decompose multivariate time series into statistically independent time series. Then, we propose to use ICA-GARCH models which are computationally efficient to estimate the multivariate volatilities. The experimental results show that the ICA-GARCH models are more effective than existing methods, including DCC, PCA-GARCH, and EWMA. We also apply the proposed models to compute value at risk (VaR) for risk management applications. The backtesting and the out-of-sample tests validate the performance of ICA-GARCH models for value at risk estimation.
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L., Wiri, Sibeate P.U., and Isaac D.E. "Markov Switching Intercept Vector Autoregressive Model (MSI(2)-VAR(2)) of Nigeria Inflation Rate and Crude Oil Price (Using Views 11)." African Journal of Mathematics and Statistics Studies 4, no. 2 (August 9, 2021): 88–100. http://dx.doi.org/10.52589/ajmss-vy1oocxz.
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To model inflation rate and crude oil prices, we used Markov Switching intercept heteroscedasticity Vector Autoregressive models. The data for this analysis was gathered from the Central Bank of Nigeria Statistical Bulletin monthly. The upward and downward movement in the series revealed by the time plot suggests that the series exhibit a regime-switching pattern: the period of expansion and contraction. The variable was stationary at first differences, the Augmented Dickey-Fuller test was used to screen for stationarity. The information criteria were used to test the number of regime and regime two were selected. Eight models were estimated for the MSI-VAR model. The best model was chosen based on the criterion of least information criterion, Markov-switching intercept heteroscedasticity – Vector Autoregressive model (MSIH(2)-VAR(2)) with AIC (8.596641) and SC (8.973119). The model was used to predict the series' values over a one-year cycle (12 months).
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Xie, Pin Jie, Chen Chen Huang, and Xian You Pan. "Characteristic Analysis of the Electricity Price Fluctuation: An Empirical Analysis Based on California’s Day-Ahead Market." Advanced Materials Research 1070-1072 (December 2014): 1534–40. http://dx.doi.org/10.4028/www.scientific.net/amr.1070-1072.1534.
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The paper deals with the Day-ahead Market of California between Apr. 1st, 1998 and Jan. 31, 2001 and divided each day to high-load period and low-load period, described the characteristics of electricity price fluctuation by ARCH models. The results showed that ARCH models under t-distribution matched the volatility of the sample series quite well, captured the series’ heteroscedasticity and the obvious peak and fat tail effectively; the total risk of the day-ahead market in the sample was high, the impacts from external information on the conditional variances was permanent and sustainable, the impacts could not disappear in a short time once the price were fluctuated; the daily mean price fluctuation and low-load period price fluctuation were not asymmetric; while high-load period were significantly asymmetrical.
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Orman, Turgut, and İlkay Dellal. "Cointegration Analysis of Exchange Rate Volatility and Agricultural Exports in Turkey: an Ardl Approch." Turkish Journal of Agriculture - Food Science and Technology 9, no. 6 (July 4, 2021): 1180–85. http://dx.doi.org/10.24925/turjaf.v9i6.1180-1185.4456.
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This study aims to reveal the impact of exchange rate volatility on agricultural exports of Turkey by using the Autoregressive Distributed Lag Model. While quarterly time series data covering period of 2001: Q1 to 2018: Q4 were used to carry out analyses, Exponential Generalized Autoregressive Conditional Heteroscedasticity (1.1) is used to acquire exchange rate volatility series. The research findings showed that agricultural export is cointegrated with exchange rate volatility, producer price index and real effective exchange rate. Furthermore, our findings indicate that increases in real effective exchange rate have a statistically significant positive influence on the export volume whereas exchange rate volatility has negative impact on it.
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Akbar, Dody, Sarce B. Awom, and Siti Aisah Bauw. "Pengaruh Pendidikan Dan Kesehatan Terhadap Pertumbuhan Ekonomi Di Kabupaten Teluk Bintuni Periode 2010-2018." JFRES: Journal of Fiscal and Regional Economy Studies 4, no. 1 (March 30, 2021): 8–14. http://dx.doi.org/10.36883/jfres.v4i1.45.
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This study aims to determine the effect of education and health on economic growth in Teluk Bintuni Regency for the 2010-2018 period. This type of research is quantitative research. This research uses time series data and secondary data collection techniques. Analysis of the data using the Coefficient of Determination Test Heteroscedasticity Test f Test t test. The results of this study show (X1) Education and (X2) Health have a positive and significant effect on (Y) Economic Growth.
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Sun, Kaiying. "Equity Return Modeling and Prediction Using Hybrid ARIMA-GARCH Model." International Journal of Financial Research 8, no. 3 (June 12, 2017): 154. http://dx.doi.org/10.5430/ijfr.v8n3p154.
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In this paper, a hybrid ARIMA-GARCH model is proposed to model and predict the equity returns for three US benchmark indices: Dow Transportation, S&P 500 and VIX. Equity returns are univariate time series data sets, one of the methods to predict them is using the Auto-Regressive Integrated Moving Average (ARIMA) models. Despite the fact that the ARIMA models are powerful and flexible, they are not be able to handle the volatility and nonlinearity that are present in the time series data. However, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models are designed to capture volatility clustering behavior in time series. In this paper, we provide motivations and descriptions of the hybrid ARIMA-GARCH model. A complete data analysis procedure that involves a series of hypothesis testings and a model fitting procedure using the Akaike Information Criterion (AIC) is provided in this paper as well. Simulation results of out of sample predictions are also provided in this paper as a reference.
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Li, Xuedi, Jie Ma, Zhu Chen, and Haitao Zheng. "Linkage Analysis among China’s Seven Emissions Trading Scheme Pilots." Sustainability 10, no. 10 (September 23, 2018): 3389. http://dx.doi.org/10.3390/su10103389.
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This paper focuses on the time-varying correlation among China’s seven emissions trading scheme markets. Correlation analysis shows a weak connection among these markets for the whole sample period, which spans from 9 June 2014 to 30 June 2017. The return rate series of the seven markets show the characteristics of a fat-tailed and skewed distribution, and the Vector Autoregression (VAR) residuals present a significant Autoregressive Conditional Heteroscedasticity (ARCH) effect. Therefore, we adopt Vector Autoregression Generalized ARCH model with Dynamic Conditional Correlation (VAR-DCC-GARCH) to capture the time-varying correlation coefficients. The results of the VAR-DCC-GARCH show that the conditional correlation coefficients fluctuate fiercely over time. At some points, the different markets present a significant correlation with the value of the even peaks of the coefficient at 0.8, which indicates that these markets are closely connected. However, the connection between each market does not last long. According to the actual situation of China’s regional carbon emission markets, policy factors may explain most of the temporary, significant co-movement among markets.
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Kobayashi, Daiki, Hana Hayashi, Hironori Kuga, Nagato Kuriyama, Yoshihiro Terasawa, Yasuhiro Osugi, Osamu Takahashi, Gautam Deshpande, and Ichiro Kawachi. "Alcohol consumption behaviours in the immediate aftermath of earthquakes: time series study." BMJ Open 9, no. 3 (March 2019): e026268. http://dx.doi.org/10.1136/bmjopen-2018-026268.
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ObjectivesEarthquakes are a distressing natural phenomenon that can disrupt normal health-related behaviours. The aim of this study was to investigate changes in alcohol consumption behaviours in the immediate aftermath of mild to moderate earthquakes.SettingThis retrospective cohort study was conducted at a large academic hospital in Tokyo, Japan from April 2004 to March 2017.ParticipantsWe included all adult patients presenting with acute alcohol intoxication in the emergency room.Primary and secondary outcome measuresOur outcome was the number of such patients per 24 hours period comparing days with and without earthquake activity. We mainly focused on mild to moderate earthquakes (Shindo scale of less than 3). We conducted a simple generalised autoregressive conditional heteroscedasticity (GARCH) analysis, followed by a multivariate GARCH, including year-fixed effects and secular changes in alcohol taxation. Subanalyses were conducted by gender and age group.ResultsDuring the study period, 706 earthquakes were observed with a median Shindo scale of 2 (IQR: 1). During this period, 6395 patients were admitted with acute ethanol intoxication; the mean age was 42.6 (SD: 16.9) years and 4592 (71.8%) patients were male. In univariate analyses, the occurrence of daytime earthquakes was marginally inversely related to the number of acutely intoxicated patients (β coefficient: −0.19, 95% CI −0.40 to 0.01). This finding remained similar in multivariate analyses after adjustment for covariates. In analyses stratified by gender, the inverse association between daytime earthquakes and alcohol intoxication was only observed among men (p<0.03 for males and p=0.99 for females). In subanalyses by age, older people were less likely to be admitted to the hospital due to acute alcohol intoxication on days with daytime earthquakes (p=0.11), but this was not the case for younger people (p=0.36).ConclusionOn days when a mild to moderate daytime earthquake occurred, the number of patients with acute alcohol intoxication was lower compared with days without earthquakes. Even milder forms of potentially catastrophic events appear to influence social behaviour; mild to moderate earthquake activity is associated with the avoidance of excessive alcohol consumption.
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Antunes, José Leopoldo Ferreira. ""Grow and multiply": social development, birth rates and demographic transition in the Municipality of São Paulo, Brazil, time-series for 1901-94." Revista Brasileira de Epidemiologia 1, no. 1 (April 1998): 61–78. http://dx.doi.org/10.1590/s1415-790x1998000100007.
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This study reports the construction of time-series related to standardized mortality rate, proportional mortality ratio of Swaroop and Uemura, infant mortality rate, fetal death rate, expectation of life at birth and birth rate for the city of São Paulo, SP, Brazil, from 1901 to 1994. In order to determine the structural variation of these measures, the model, forecast and correlation of these series were submitted to statistical analysis. The results obtained were compared to the historical analysis of the major socioe-conomic phenomena during this period in an effort to explain populational movements in the city, with emphasis on the slow and late nature of the process of demographic transition in the city. It was concluded that time-series analysis for demographic measures is efficient in many ways: by allowing the application of statistical methodology to the human sciences, by passing the difficulties inherent in the characteristics of these values (serial correlation, heteroscedasticity, multicollinearity and non-normality of forecast error distribution), by integrating quantitative analysis with the historical interpretation of the phenomena approached, by projecting estimates of future trends on the basis of the behavior of the variables analyzed, and by systematizing the methodology for application in future studies of social research.
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Kamarianakis, Yiannis, Angelos Kanas, and Poulicos Prastacos. "Modeling Traffic Volatility Dynamics in an Urban Network." Transportation Research Record: Journal of the Transportation Research Board 1923, no. 1 (January 2005): 18–27. http://dx.doi.org/10.1177/0361198105192300103.
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This article discusses the application of generalized autoregressive conditional heteroscedasticity (GARCH) time series models for representing the dynamics of traffic flow volatility. The methods encountered in the literature focus on the levels of traffic flows and assume that variance is constant through time. The approach adopted in this paper concentrates primarily on the autoregressive properties of traffic variability, with the aim to provide better confidence intervals for traffic flow forecasts. The model-building procedure is illustrated with 7.5-min average traffic flow data for a set of 11 loop detectors located at major arterials that direct to the center of the city of Athens, Greece. A sensitivity analysis for coefficient estimates is undertaken with respect to both time and space.
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Nadirah Mohd Johari, Sarah, Fairuz Husna Muhamad Farid, Nur Afifah Enara Binti Nasrudin, Nur Sarah Liyana Bistamam, and Nur Syamira Syamimi Muhammad Shuhaili. "Predicting Stock Market Index Using Hybrid Intelligence Model." International Journal of Engineering & Technology 7, no. 3.15 (August 13, 2018): 36. http://dx.doi.org/10.14419/ijet.v7i3.15.17403.
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Predicting financial market changes is an important issue in time series analysis, receiving an increasing attention due to financial crisis. Autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting but ARIMA model cannot capture nonlinear patterns easily. Generalized autoregressive conditional heteroscedasticity (GARCH) model applied understanding of volatility depending to the estimation of previous forecast error and current volatility, improving ARIMA model. Support vector machine (SVM) and artificial neural network (ANN) have been successfully applied in solving nonlinear regression estimation problems. This study proposes hybrid methodology that exploits unique strength of GARCH + SVM model, and GARCH + ANN model in forecasting stock index. Real data sets of stock prices FTSE Bursa Malaysia KLCI were used to examine the forecasting accuracy of the proposed model. The results shows that the proposed hybrid model achieves best forecasting compared to other model.
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Poměnková, Jitka, Eva Klejmová, and Tobiáš Malach. "Optimized Segmentation-Adaptive-Based Testing of the Wavelet Co-movement Analysis: the Case of US and G8 Countries." ITM Web of Conferences 24 (2019): 01003. http://dx.doi.org/10.1051/itmconf/20192401003.
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The paper deals with the identification and the description of the co-movement between the US and G8 countries with regard to the impact of the structural change, i.e. the financial crisis in 2008. For the identification of the co-movement, we use an optimized segmentation-adaptive-based approach (SAB) of significance testing of the power wavelet cross-spectrum. The SAB testing is based on the standard testing for the power wavelet cross-spectrum adapted for the case if the data have several levels of volatility during the time evolution, i.e. the data can be split into several segments with different volatility. The number of segments is set by the heteroscedasticity test and the test for comparing variances in the segments of the time series. The SAB testing allows us to identify significant co-movement with respect to the local variance, which can reveal additional significant co-movement areas. We apply this approach to the monthly data of industrial production index for G8 countries in 1993–2017.
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Cheng, Cong, Ling Yu, and Liu Jie Chen. "Structural Nonlinear Damage Detection Based on ARMA-GARCH Model." Applied Mechanics and Materials 204-208 (October 2012): 2891–96. http://dx.doi.org/10.4028/www.scientific.net/amm.204-208.2891.
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Two economic models, i.e. auto-regressive and moving average model (ARMA) and generalized auto-regressive conditional heteroscedasticity model (GARCH), are adopted to assess the conditions of structures and to detect structural nonlinear damage based on time series analysis in this study. To improve the reliability of the method for nonlinear damage detection, a new damage sensitive feature (DSF) for the ARMA-GARCH model is defined as a ratio of the standard deviation of the variance time series of ARMA-GARCH model residual errors in test condition to ones in reference condition. Compared to the traditional DSF defined as the ratio between the deviations of ARMA-GARCH model residual error in two conditions, the successful outcomes of the new DSF can give obvious explanation for the current states of structures and can detect the nonlinear damage exactly, which enhance the worth of structural health monitoring as well as condition-based maintenance in practical applications. This method is finally verified by a series of experimental data of three-story building structure made in Los Alamos National Laboratory USA.
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Ogbeide, F. N., J. O. Ehiorobo, O. C. Izinyon, and I. R. Ilaboya. "A Qualitative Study of Time Overrun of Completed Road Projects Awarded by the Niger Delta Development Commission in the Niger Delta Region of Nigeria." March 2021 5, no. 1 (February 2021): 271–80. http://dx.doi.org/10.36263/nijest.2021.01.0269.
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Time overrun of completed road projects awarded by the Niger Delta Development Commission (NDDC) in the Niger Delta Region of Nigeria from its inception in 2000 up to 2015 was studied. Out of 3315 roads awarded, only 1081 roads representing 31.65 percent were completed within the review period. The qualitative study was carried out on randomly selected completed 162 road projects for analysis, and a conceptual model of time series was developed. In developing the regression model, both dependent and independent variables were subjected to normality tests assessed by skewness coefficient, kurtosis value, Jarque-Bera test, residual probability plot, heteroscedasticity test and the variance inflation factor. Also, with knowledge of total road projects awarded by the Commission, it is now possible to predict proportions of roads experiencing schedule overruns.
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Mofema, Victor Mbua, and Gisele Mah. "An empirical analysis of volatility in South African oil prices." Journal of Energy in Southern Africa 32, no. 3 (September 19, 2021): 67–75. http://dx.doi.org/10.17159/2413-3051/2021/v32i3a8852.
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Volatility of the oil price has been around since the 1970s and an understanding of how it evolves provides insight into solving macroeconomic challenges. The main objective of this study was to analyse the volatility of South African oil prices using quarterly time series data from 2000 to 2020. The effect of growth in gross domestic product per capita, interest rate, inflation and money supply growth on oil price changes was assessed. Generalised autoregressive conditional heteroscedasticity (GARCH) was estimated and diagnostic tests – namely ARCH, normality and autocorrelation tests – were conducted. The GARCH (1,2) model was the best fit, based on the Alkaike information criterion. The result revealed that interest rates and money supply growth have a significant positive effect on oil price changes in South Africa, while growth in GDP per capita and inflation has an insignificant impact. Past one and two-quarters’ oil price volatility increases and decreases the current oil price volatility respectively. Based on the findings, a contractionary monetary policy is recommended in order to reduce the volatility of South African oil prices.
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Liu, Meili, Liwei Wang, Chun-Te Lee, and Jeng-Eng Lin. "Volatility Analysis and Visualization of Climate Data Based on Wavelets." Journal of Mathematics Research 13, no. 4 (July 12, 2021): 50. http://dx.doi.org/10.5539/jmr.v13n4p50.
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In this article, we analyze the real meteorological data recorded by Wenzhou Meteorological Bureau from 1951 to 1997. The data has not been used elsewhere and is available at Meteorological Station Wenzhou (ID: CHM00058659) at https://geographic.org/global_weather/china. We perform the time series volatility analysis including ARMA, ARIMA, ARCH-LM, PARCH, SARMA and Morlet wavelet analysis and use the Mann-Kendall (M-K) test to analyze both the trend and mutation defined by statistics sequence. In addition, a Morete wavelet time-frequency model is established to show that both the precipitation and temperature have a very important 12-month cycle and the precipitation is also very unstable. We then employ the STL, coif1 decompositions and NAR model to capture both the volatility and Heteroscedasticity in the data. In addition, the performance of the fitted model has been proven to be satisfactory on actual climate data with the small Mean Square Error (MSE), Root-Mean-Squarred Error (RMSE), and coefficient of determination. Finally, monthly average temperature is added as an exogenous (covariate) variable and a nonlinear autoregressive exogenous model is employed to improve the performance of the model. Our results show that the performance of NARX model is more accurate and stable with better mean square error.
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Setiawan, Edi, Faizal Ridwan Zamzani, and Nur Fitri Amelia. "CASH POSITION, DEBT TO EQUITY RATIO, RETURN ON ASSET DAN FIRM SIZE TERHADAP DIVIDENT PAYOUT RATIO." JURNAL NUSANTARA APLIKASI MANAJEMEN BISNIS 3, no. 1 (April 18, 2018): 78. http://dx.doi.org/10.29407/nusamba.v3i1.11980.
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This research is aimed to knowing and analyzing the effect of the cash position, debt to equity ratio, return on asset and firm size to divident payout ratio. The type of data used in this study is pool data which is a combination time series, and cross section. panel data regression anlaysis test, election panel data reression estimation techniques, heteroscedasticity test, regression model analysis, and regression model testin and regression coefficients. The result showed that the partial debt to equity ratio and firm size no significantly influence the divident payout ratio, while the cash position, return on asset significantly influence the divident payout ratio. Simultaneously, cash position, debt to equity ratio, return on asset and firm size variable have a significant to divident payout ratio.
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Abdul Halim, Nurfadhlina, Agus Supriatna, and Adhy Prasetyo. "Estimation of the Value-at-Risk (VaR) Using the TARCH Model by Considering the Effects of Long Memory in Stock Investments." Operations Research: International Conference Series 1, no. 1 (February 5, 2020): 34–43. http://dx.doi.org/10.47194/orics.v1i1.22.
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Value at Risk (VaR) is one of the standard methods that can be used in measuring risk in stock investments. VaR is defined as the maximum possible loss for a particular position or portfolio in the known confidence level of a specific time horizon. The main topic discussed in this thesis is to estimate VaR using the TARCH (Threshold Autoregressive Conditional Heteroscedasticity) model in a time series by considering the effect of long memory. The TARCH model is applied to the daily log return data of a company's stock in Indonesia to estimate the amount of quantile that will be used in calculating VaR. Based on the analysis, it was found that with a significance level of 95% and assuming an investment of 200,000,000 IDR, the VaR using the TARCH model approach was 5,110,200 IDR per day.
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Santoso, Michael Alexander, Apriani Dorkas Rambu Atahau, and Robiyanto Robiyanto. "IHSG dan Dinamikanya: Sebuah Analisis atas Pengaruh Variable Makroekonomi." Jurnal Pasar Modal dan Bisnis 1, no. 1 (September 2, 2019): 21–40. http://dx.doi.org/10.37194/jpmb.v1i1.6.
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Purpose- This research aimed to study the effect of macroeconomic variables: Dow Jones Industrial Average, USD/IDR, and World Crude Oil Price towards Jakarta Composite Index (JCI) during the period of 2005-2016.
Methods- This research using the daily closing prices of Dow Jones Industrial Average (JCI), USD/IDR, World Crude Oil Price, and Jakarta Composite Index, the GARCH (1,1) The data analysis technique used in this study is Generalized Autoregressive Conditional Heteroscedasticity (GARCH). The reason for choosing the GARCH analysis technique is that this study uses time series data which is often abnormal and cannot be normalized.
Finding- Analysis show that Dow Jones Industrial Average and world crude oil price has a positive significant effect on the JCI while USD/IDR has a negative significant effect on JCI.
Implication- The findings imply the importance to consider macroeconomic variables when investing at Jakarta Stock Exchange
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Chaerunisak, Uum Helmina, Dewi Kusuma Wardani, and Zara Tri Prihatiningrum. "PENGARUH CAPITAL ADEQUACY RATIO (CAR) DAN BIAYA OPERASIONAL PENDAPATAN OPERASIONAL (BOPO) TERHADAP KINERJA BANK SYARIAH." JURNAL SOSIAL EKONOMI DAN HUMANIORA 5, no. 2 (December 30, 2019): 203–15. http://dx.doi.org/10.29303/jseh.v5i2.62.
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This study aims to determine the effect of capital adequacy ratio, financing to deposite ratio and operating costs of operating income on healthy returns on. This study uses data which is a time series cross section data from sharia banking statistics from 2015-2018 and 2019 (only January to August because the most recent data) is registered with Otoritas Jasa Keuangan (OJK). Data collection methods in this study used purposive sampling. Analysis of the data used is multiple linear regression. The classic assumption tests used in this study are the normality test, the multicollinearity test, the heteroscedasticity test, and the autocorrelation test. The results of this study indicate that the capital adequacy ratio does not affect the return on assets, operational costs of operating income negatively affect the return on assets
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Kunwar, Keshar Bahadur. "Impact of Government Expenditure in Economic Growth of Nepal: ARDL Approach." Contemporary Research: An Interdisciplinary Academic Journal 3, no. 1 (December 31, 2019): 33–40. http://dx.doi.org/10.3126/craiaj.v3i1.27488.
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Public expenditure refers to the expenditure made by public authority, i.e., central government and other local bodies to carter the demand of the people. It is for protecting the citizens and for promoting their economic and social welfare. Public expenditure is one of the instruments through which government influence economic events. The specific objective of this paper is to analyze the long run and short run relationship between public expenditure and economic growth in Nepal and to examine the Causal relationship between the public expenditure and economic growth in Nepal. The study employed quantitative techniques and econometrics methods to analyze the data. This study used time series data. Data analysis begins with the testing of the unit root of the series to confirm whether the data are stationary or not. Augmented Dicky Fuller unit root test, co-integration test is employed to check the relationship of the variables under study. One period lagged LNGE has significant and positive impact on RGDP. If 1 percent increase in GE leads to increase by 34.99 percent in RGDP at 5 percent level of significance. The coefficient of error correction term (-0.782018) is significant at one percent level. Highly significant negative sign of the error correction term strengthens the presence of long-run relationship among the variables. However, the speed of adjustment from previous year’s disequilibrium in RGDP added to current year’s equilibrium is only 78.20 percent. The P-value of Breusch-Godfrey serial Correlation LM Test, Heteroscedasticity test: Breusch-Pagan-Godfrey and normality test is greater than 5 percent which is desirable. So, this model is free from auto correlation and heteroscedasticity. The residual is normally distributed.
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Liu, Pengfei, and Han-Sol Lee. "Foreign direct investment (FDI) and economic growth in China: vector autoregressive (VAR) analysis." SHS Web of Conferences 80 (2020): 01002. http://dx.doi.org/10.1051/shsconf/20208001002.
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This study examines the impact of foreign direct investment (FDI) on economic growth in China based on time series data for the period 1981-2018. For an empirical study, we used vector autoregressive (VAR) analysis. Before building our VAR model, we performed tests for unit root, normality, and heteroscedasticity to certify the data quality. The optimal lag 3 was selected using the Akaike information criterion (AIC), Schwartz (SC), and Hannan-Quinn (HQ) criteria. The Granger causality test is additionally performed. Based on the VAR model, we determined the impulse responses and variance decomposition of log FDI and log GDP in China. The results showed a positive and consistent impact of log FDI on China’s economic growth. The impact in the short-term is insignificant, as it is likely that there are multiple factors drive economic growth of China besides FDI inflows. However, the impact of FDI increases to a significant level in the long-term. Which indicates that FDI is one of the main factors to enhance Chinese economy. In conclusion, we suggest a policy implication how to sustain and promote the existing positive effects of FDI inflows on Chinese economy.
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Yin, Zheng, Conall O’Sullivan, and Anthony Brabazon. "An Analysis of the Performance of Genetic Programming for Realised Volatility Forecasting." Journal of Artificial Intelligence and Soft Computing Research 6, no. 3 (July 1, 2016): 155–72. http://dx.doi.org/10.1515/jaiscr-2016-0012.
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AbstractTraditionally, the volatility of daily returns in financial markets is modeled autoregressively using a time-series of lagged information. These autoregressive models exploit stylised empirical properties of volatility such as strong persistence, mean reversion and asymmetric dependence on lagged returns. While these methods can produce good forecasts, the approach is in essence atheoretical as it provides no insight into the nature of the causal factors and how they affect volatility. Many plausible explanatory variables relating market conditions and volatility have been identified in various studies but despite the volume of research, we lack a clear theoretical framework that links these factors together. This setting of a theory-weak environment suggests a useful role for powerful model induction methodologies such as Genetic Programming (GP). This study forecasts one-day ahead realised volatility (RV) using a GP methodology that incorporates information on market conditions including trading volume, number of transactions, bid-ask spread, average trading duration (waiting time between trades) and implied volatility. The forecasting performance from the evolved GP models is found to be significantly better than those numbers of benchmark forecasting models drawn from the finance literature, namely, the heterogeneous autoregressive (HAR) model, the generalized autoregressive conditional heteroscedasticity (GARCH) model, and a stepwise linear regression model (SR). Given the practical importance of improved forecasting performance for realised volatility this result is of significance for practitioners in financial markets.
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Zolna, Konrad, Phong B. Dao, Wieslaw J. Staszewski, and Tomasz Barszcz. "Nonlinear Cointegration Approach for Condition Monitoring of Wind Turbines." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/978156.
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Monitoring of trends and removal of undesired trends from operational/process parameters in wind turbines is important for their condition monitoring. This paper presents the homoscedastic nonlinear cointegration for the solution to this problem. The cointegration approach used leads to stable variances in cointegration residuals. The adapted Breusch-Pagan test procedure is developed to test for the presence of heteroscedasticity in cointegration residuals obtained from the nonlinear cointegration analysis. Examples using three different time series data sets—that is, one with a nonlinear quadratic deterministic trend, another with a nonlinear exponential deterministic trend, and experimental data from a wind turbine drivetrain—are used to illustrate the method and demonstrate possible practical applications. The results show that the proposed approach can be used for effective removal of nonlinear trends form various types of data, allowing for possible condition monitoring applications.
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Tadesse, Kassahun Birhanu, and Megersa Olumana Dinka. "Application of SARIMA model to forecasting monthly flows in Waterval River, South Africa." Journal of Water and Land Development 35, no. 1 (December 1, 2017): 229–36. http://dx.doi.org/10.1515/jwld-2017-0088.
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AbstractKnowledge of future river flow information is fundamental for development and management of a river system. In this study, Waterval River flow was forecasted by SARIMA model using GRETL statistical software. Mean monthly flows from 1960 to 2016 were used for modelling and forecasting. Different unit root and Mann–Kendall trend analysis proved the stationarity of the observed flow time series. Based on seasonally differenced correlogram characteristics, different SARIMA models were evaluated; their parameters were optimized, and diagnostic check up of forecasts was made using white noise and heteroscedasticity tests. Finally, based on minimum Akaike Information (AI) and Hannan–Quinn (HQ) criteria, SARIMA (3, 0, 2) x (3, 1, 3)12 model was selected for Waterval River flow forecasting. Comparison of forecast performance of SARIMA models with that of computational intelligent forecasting techniques was recommended for future study.
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Gupta, Mohini, and Sakshi Varshney. "Exchange Rate Volatility and Import Trade Flow Evidence From India-U.S. at Industry Level." International Journal of Asian Business and Information Management 12, no. 3 (July 2021): 1–21. http://dx.doi.org/10.4018/ijabim.20210701.oa25.
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The centre interest of the study is to explore the impact of exchange rate volatility on the India-U.S. trade flow of Import on 6 industries spanned from September 2002 to June 2019. We investigate the relationship at disaggregate level by industry-wise data with monthly frequency. We employ exponential generalized autoregressive conditional heteroscedasticity (E-GARCH) model to gauge volatility and thereafter ARDL bound testing approach to unveil the short and long-run association of real exchange rate volatility and import. The empirical analysis implies the existence of both short-run and long-run effect in 5 importing industries except manufactured (engineering) goods. While real exchange volatility appears to have statistically significant effect in short-run, but also estimated short-run lasts onto long-run effect in only three industries. The results confirm the information of import in time-series analysis. The finding of the study helps to undertake the view of invariability and considering the industry before policy making.
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Mulyanah, Sri Nurul, and Abitur Asianto. "Value at Risk Analysis towards Automotive Sub Sector Shares and its Components at Indonesia Stock Exchange." Volume 5 - 2020, Issue 8 - August 5, no. 8 (August 30, 2020): 799–807. http://dx.doi.org/10.38124/ijisrt20aug429.
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The purpose from this research was to analyzed those optimum model with Autoregressive Conditional Heteroscsedasticity - Generalized Autoregressive Conditional Heteroscedasticity (ARCH-GARCH) from automotive sector shares and estimated the calculation investment risk analysis the Value at Risk method approach used 95% confidence level and holding period which provides information on maximum potential loss towards stock return value. Data From these research was secondary data for time series in form of monthly Shares return value from Astra Internasional, Astra Otoparts, Goodyear Indonesia, Gajah Tunggal, Indomobil Sukses Internasional, and Prima Alloy Steel Universal. Data was obtained from www.idx.co.id, yahoo.finance.com and other sources from December 2013 to August 2019.The risk analysis tool for calculating Value at Risk with Variance-covariance type. The Conclusion from these research results Was the data was stationary which does not had normal distribution and the longer the investment takes, the higher the loss rate. This research was expected to be useful for policy makers to consider decisions regarding investment decisions in automotive sector or related companies to develop Indonesian economy and this research was expected to broaden knowledge, views and information and could provide empirical evidence about Value At risk analysis Through ARCH–GARCH model
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Lumban Gaol, Elisabeth, Armen Mara, and Riri Oktari Ulma. "ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI PRODUKSI BOKAR (BAHAN OLAH KARET) DI KABUPATEN BATANGHARI." JALOW | Journal of Agribusiness and Local Wisdom 3, no. 1 (June 29, 2020): 38–49. http://dx.doi.org/10.22437/jalow.v3i1.9790.
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This study aims to (1) the progress bokar production, the land area to produce crops, the land area of old plants, the amount of labor, rainfall and number of days of rain in Batanghari regency during the period 2001 to 2015 (2) Determine how much influence hectarage produce, the land area of old plants, the amount of labor, rainfall and number of days of rain to the production bokar in Batanghari regency during the period 2001-2015. The data used in this research is secondary data time series (time series) for 5 years (2001-2015). Test data is stationary using the unit root test Phillip Perron (PP). The analysis model is a linear regression. The test model using normality test, multicollinearity, heteroscedasticity test and autocorrelation test. The results showed that the area of cultivated land, the area of old plantation, the amount of labor, rainfall and amount of rain days together significantly affect the production of bokar in Batanghari regency. Partially, the factors that have a positive and significant effect on bokar production in Batanghari Regency are the area of cultivated land and the amount of labor. Factor area of old crop land have negative and significant effect, while rainfall factor and rainy day partially have no significant effect on bokar production.
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Ayele, Amare Wubishet, Emmanuel Gabreyohannes, and Yohannes Yebabe Tesfay. "Macroeconomic Determinants of Volatility for the Gold Price in Ethiopia: The Application of GARCH and EWMA Volatility Models." Global Business Review 18, no. 2 (March 30, 2017): 308–26. http://dx.doi.org/10.1177/0972150916668601.
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Modelling and forecasting of commodity price volatility has important applications for asset management, portfolio analysis and risk assessment due to the simple fact that volatility has informational content and contains signals of the market information flow. This article models and forecasts the gold price volatility using the exponentially weighted moving average (EWMA) and the generalized autoregressive conditional heteroscedasticity (GARCH) models for the period from 1998 to 2014. The gold series shows the classical characteristics of financial time series, such as leptokurtic distributions, data dependence and strong serial correlation in squared returns. Hence, the series can be modelled using both EWMA and GARCH-type models. Among the GARCH-type models, GARCH-M(2,2) with Student’s t distribution for the residuals was found to be the best-fit model. Moreover, the manuscript finds that interest rates, exchange rates and crude oil prices have a significant impact on gold volatility. The risk premium effect is found to be positive and statistically significant, suggesting increased volatility is followed by a higher mean. Finally, a comparison is made between the GARCH and the EWMA models. Using the relative mean squared error and mean absolute error measures, the empirical result suggests that GARCH models with explanatory variables are superior for volatility forecasting.
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LUX, THOMAS, and MICHELE MARCHESI. "VOLATILITY CLUSTERING IN FINANCIAL MARKETS: A MICROSIMULATION OF INTERACTING AGENTS." International Journal of Theoretical and Applied Finance 03, no. 04 (October 2000): 675–702. http://dx.doi.org/10.1142/s0219024900000826.
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The finding of clustered volatility and ARCH effects is ubiquitous in financial data. This paper presents a possible explanation for this phenomenon within a multi-agent framework of speculative activity. In the model, both chartist and fundamentalist strategies are considered with agents switching between both behavioural variants according to observed differences in pay-offs. Price changes are brought about by a market maker reacting to imbalances between demand and supply. Most of the time, a stable and efficient market results. However, its usual tranquil performance is interspersed by sudden transient phases of destabilisation. An outbreak of volatility occurs if the fraction of agents using chartist techniques surpasses a certain threshold value, but such phases are quickly brought to an end by stabilising tendencies. Formally, this pattern can be understood as an example of a new type of dynamic behaviour known as "on-off intermittency" in physics literature. Statistical analysis of simulated time series shows that the main stylised facts (unit roots in levels together with heteroscedasticity and leptokurtosis of returns) can be found in this "artificial" market.
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Afdal, Afyana, and Mike Triani. "ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI PERTUMBUHAN EKONOMI DI KAB/KOTA SUMATERA BARAT." Jurnal Ecogen 1, no. 3 (February 7, 2019): 616. http://dx.doi.org/10.24036/jmpe.v1i3.5035.
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the government in the economic sector towards economic growth in West Sumatra. This type of research is descriptive and associative, namely research that describes research variables and finds the presence or absence of the influence of variables with independent variables. Data type is secondary data (Pool Time Series). The data writing technique in this research is literature study and documentation from 2012 to 2016. Descriptive and inductive data analysis are: Classical Assumption Test (Heteroscedasticity Test), Panel Regression Model, T Test and F Test.The results of this study conclude that a significant workload on economic growth in West Sumatra, one of the factors that influence economic growth in West Sumatra, and also the sector economy does not significantly influence poverty in West Sumatra. In connection with the results of the study, the suggestions needed are important to improve the quality, quality of employment opportunities, in order to reduce poverty and improve the economy in West Sumatra. Keywords: Job Opportunities, Poverty, Government Expenditures, Economic Growth
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Falianty, Telisa Aulia. "Tinjauan terhadap Metode Ekonometrika Lanjutan." Jurnal Ekonomi dan Pembangunan Indonesia 4, no. 1 (July 1, 2003): 59–74. http://dx.doi.org/10.21002/jepi.v4i1.133.
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Econometric models have been played an increasingly important role in empirical analysis in economics. This paper provides an overview on some advanced econometric methods that increasingly used in empirical studies.A panel data combines features of both time series and cross section data. Because of increasing availability of panel data in economic sciences, panel data regression models are being increasingly used by researcher. Related to panel data model, there are some methods that will be discussed here such as fixed effect and random effect. A new approach to panel data that developed by Im, Shin, and Pesaran (2002) for testing unit root in heterogenous panel is included in this overview.When we work with time series data, there are many problems that we must handle, most of them are unit root test, cointegration among non stationary variables, and autoregressive conditional heteroscedasticity. Provided these problems, author also review about ADF and Philips-Perron test. An approch to cointegration analysis developed by Pesaran (1999), ARCH and GARCH model are also interesting to be discussed here.Bayesian econometric, that less known than classical econometric, is includcd in this overview. The genctic algorithm, a relatively new method in econometric, has bcen increasingly employed the behavior of economic agents in macroeconomic models. The genetic algorithm is based on thc process of Darwin’s Theory of Evolution. By starting with a set of potential solutions and changing them during several iterations, the Genetic Algorithm hopes to converge on the most ‘fit’ solutions.
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Firdhania, Riza, and Fivien Muslihatinningsih. "Faktor-Faktor yang Mempengaruhi Tingkat Pengangguran di Kabupaten Jember." e-Journal Ekonomi Bisnis dan Akuntansi 4, no. 1 (June 13, 2017): 117. http://dx.doi.org/10.19184/ejeba.v4i1.4746.
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This research describes the relation between variables of population, inflation, minimum wage, economic growth, and humandevelopment index toward the unemployment rate in Jember. The type of data used in this research was secondary data in theform of ‘time series’ obtained from Jember Department of Labor and Central Bureau of Statistics in the year of 2002-2013.The research method was a kind of statistical descriptive analysis and multiple linear regression analysis. Moreover, theresearcher used partial test (T-test), simultaneous test (F-test), and coefficients determination test (R2) for the hypothesis.Whereas the assumption test was conducted in the use of normality, multicollinearity, heteroscedasticity, and autocorrelationtest. From the result of the data analysis, it confirmed that the population positively and significantly affected theunemployment rate in Jember. The variables of inflation, minimum wage, and human development index negatively andsignificantly affected the unemployment rate in Jember. Whereas the variables of economic growth positively and significantlyaffected unemployment rate in Jember. Finally, the result of the data analysis highlighted the variables of population,inflation, minimum wage,economic growth, and human development index that simultaneously and significantly affectedunemployment rate in Jember.
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Rani Wulantari, Meidy Haviz, and Ade Yunita Mafruhat. "Pengaruh Pendapatan Asli Daerah (PAD), Dana Alokasi Umum (DAU), dan Penanaman Modal Dalam Negeri (PMDN) terhadap Produk Domestik Regional Bruto (PDRB) Provinsi Jawa Barat 2003-2017." Jurnal Riset Ilmu Ekonomi dan Bisnis 1, no. 1 (July 6, 2021): 34–41. http://dx.doi.org/10.29313/jrieb.v1i1.62.
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Abstract. This study aims to identify and analyze how and how much influence PAD, DAU, and PMDN on West Java Province GRDP 2003-2017. The research method used is quantitative and qualitative methods. The type and source of data used are secondary data obtained from BPS based on time series and cross sections, which is 15 years. The analytical method used is the Ordinary Least Square (OLS) method using the Eviews version 9.0 program. The tests performed are classical assumptions (Multicollinearity, Autocorrelation, Heteroscedasticity, and Normality) and statistical tests are then performed economic analysis. The results showed that PAD and DAU influence and significant effect on GRDP while PMDN has an effect but not significantly on GRDP.
Abstrak. Penelitian ini bertujuan untuk mengidentifikasi dan menganalisis bagaimana dan seberapa besar pengaruh PAD, DAU, dan PMDN terhadap PDRB Provinsi Jawa Barat 2003-2017. Metode penelitian yang digunakan adalah metode kuantitatif dan kualitatif. Jenis dan sumber data yang digunakan adalah data sekunder yang di peroleh dari BPS berdasarkan waktu time series dan cross section yaitu selama 15 tahun. Metode analisis yang digunakan adalah metode Ordinary Least Square (OLS) dengan menggunakan program Eviews versi 9.0. pengujian yang dilakukan yaitu asumsi klasik (Multikolinear, Autokorelasi, Heteroskedastis, dan Normalitas) dan uji statistik kemudian dilakukan analisis ekonomi. Hasil penelitian menunjukkan bahwa PAD dan DAU berpengaruh dan signifikan terhadap PDRB sedangkan PMDN berpengaruh tetapi tidak signifikan terhadap PDRB
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Alanya, Willy, and Gabriel Rodríguez. "Stochastic Volatility in the Peruvian Stock Market and Exchange Rate Returns: A Bayesian Approximation." Journal of Emerging Market Finance 17, no. 3 (October 10, 2018): 354–85. http://dx.doi.org/10.1177/0972652718800560.
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This study is one of the first to utilize the stochastic volatility (SV) model to modelling the Peruvian financial times series. We estimate and compare this model with generalized autoregressive conditional heteroscedasticity (GARCH) models with normal and t-student errors. The analysis in this study corresponds to Peru’s stock market and exchange rate returns. The importance of this methodology is that the adjustment of the data is better than the GARCH models, using the assumptions of normality in both models. In the case of the SV model, three Bayesian algorithms have been employed where we evaluate their respective inefficiencies in the estimation of the model’s parameters—the most efficient being the integration sampler. The estimated parameters in the SV model under the various algorithms are consistent, as they display little inefficiency. The figures of the correlations of the iterations suggest that there are no problems at the time of Markov chaining in all estimations. We find that the volatilities in the exchange rate and stock market volatilities follow similar patterns over time. That is, when economic turbulence caused by the economic circumstances occurred, for example, the Asian crisis and the recent crisis in the USA, considerable volatility was generated in both markets. JEL Classification: C22
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Muslihatinningsih, Fivien, Juan Palem Sinaga, and Nanik Istiyani. "Migrasi Migrasi Internasional Penduduk Pulau Jawa Menjadi Pekerja Migran Indonesia di Luar Negeri." Jurnal Ekonomi Pembangunan 9, no. 2 (July 22, 2020): 106–15. http://dx.doi.org/10.23960/jep.v9i2.100.
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International migration by Indonesian Migrant Workers (PMI) not only has a positive impact on improving the economic conditions of PMI families but also contributes to developing the country's economy through PMI remittances. This study aims to determine the effect of Unemployment, minimum wages, poverty, and human development index on international migration of Indonesian Migrant Workers (PMI) on the island of Java. This study uses secondary data in the form of panel data, with time-series data (2010 - 2019) and cross-section data (6 provinces on Java island). The data analysis method uses a panel data regression with the Common Effect Model (CEM) approach. Statistical tests use simultaneous tests, partial tests, and the coefficient of determination. This study uses the classic assumption test, the multicollinearity test, the heteroscedasticity test, and the normality test. The study results concluded that simultaneously Unemployment, minimum wages, poverty, and the human development index had a significant effect on international migration. Partially, Unemployment and poverty have a positive and significant impact, minimum wages have a negative and significant effect, while the human development index has a positive and not significant effect on international migration.
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Billah Dar, Arif, Aasif Shah, Niyati Bhanja, and Amaresh Samantaraya. "The relationship between stock prices and exchange rates in Asian markets." South Asian Journal of Global Business Research 3, no. 2 (July 29, 2014): 209–24. http://dx.doi.org/10.1108/sajgbr-07-2013-0061.
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Purpose – The purpose of this paper is to estimate the relationship between stock prices and exchange rates of eight Asian countries. The analysis is based on methodologies that possess the ability to provide a complete representation of data series from both time and frequency perspectives simultaneously. In addition, instead of limiting the analysis to focus on the conditional mean of the response variable y in the regression equation, the authors investigate the extremes of distribution to reveal a range of hidden relationships between these variables. Design/methodology/approach – Given the limitations of classical methodology of Pearson correlation and least-squares regression, this study estimates the relationship between stock prices and exchange rates through wavelet correlation and cross-correlation to serve as a protocol for different traders who view the market with different time resolutions. In addition, quantile regression technique robust to heteroscedasticity, skewness and leptokurtosis is used to understand the relationship between stock prices and a specified quantile of the exchange rates. Findings – In accordance with the portfolio balance effect, it is observed that stock prices and exchange rates are negatively correlated at all frequencies. In particular, the negative correlation grows with higher time scales (lower frequency intervals). The findings from quantile regression also suggest that the coefficients are more inclined to be negative when exchange rates are extremely high. Originality value – The paper contributes to the literature by focussing on the multi-scale relationship between stock prices and exchange rates. In addition, it also analyzes the relationship between stock prices and a specified quantile of the exchange rates.
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Wu, Edward Ming-Yang, and Shu-Lung Kuo. "VARMA-EGARCH Model for Air-Quality Analyses and Application in Southern Taiwan." Atmosphere 11, no. 10 (October 14, 2020): 1096. http://dx.doi.org/10.3390/atmos11101096.
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This study adopted the Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model to analyze seven air pollutants (or the seven variables in this study) from ten air quality monitoring stations in the Kaohsiung–Pingtung Air Pollutant Control Area located in southern Taiwan. Before the verification analysis of the EGARCH model is conducted, the air quality data collected at the ten air quality monitoring stations in the Kaohsiung–Pingtung area are classified into three major factors using the factor analyses in multiple statistical analyses. The factors with the most significance are then selected as the targets for conducting investigations; they are termed “photochemical pollution factors”, or factors related to pollution caused by air pollutants, including particulate matter with particles below 10 microns (PM10), ozone (O3) and nitrogen dioxide (NO2). Then, we applied the Vector Autoregressive Moving Average-EGARCH (VARMA-EGARCH) model under the condition where the standardized residual existed in order to study the relationships among three air pollutants and how their concentration changed in the time series. By simulating the optimal model, namely VARMA (1,1)-EGARCH (1,1), we found that when O3 was the dependent variable, the concentration of O3 was not affected by the concentration of PM10 and NO2 in the same term. In terms of the impact response analysis on the predictive power of the three air pollutants in the time series, we found that the asymmetry effect of NO2 was the most significant, meaning that NO2 influenced the GARCH effect the least when the change of seasons caused the NO2 concentration to fluctuate; it also suggested that the concentration of NO2 produced in this area and the degree of change are lower than those of the other two air pollutants. This research is the first of its kind in the world to adopt a VARMA-EGARCH model to explore the interplay among various air pollutants and reactions triggered by it over time. The results of this study can be referenced by authorities for planning air quality total quantity control, applying and examining various air quality models, simulating the allowable increase in air quality limits, and evaluating the benefit of air quality improvement.
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Diao, Xundi, Hongyang Qiu, and Bin Tong. "Does a unique “T+1 trading rule” in China incur return difference between daytime and overnight periods?" China Finance Review International 8, no. 1 (February 19, 2018): 2–20. http://dx.doi.org/10.1108/cfri-12-2016-0130.
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Purpose The purpose of this paper is to examine the difference between the daytime (open-to-close) and overnight (close-to-open) returns of CSI 300 index and its derivative futures. Design/methodology/approach The paper explores the difference between the daytime and overnight time returns by using nonparametric techniques. Moreover, investigation on some factors such as short selling, trading rules, risks are made to seek the sources of the day and night effects based on a large number of empirical analysis. In the end, further analyses on daytime and overnight returns are given by the use of high-frequency data and linear regression technique. Findings The authors show that the daytime returns of CSI 300 index are no less than its overnight returns, while the daytime returns of CSI 300 index futures are no more than its overnight returns, even after removing the heteroscedasticity of the researched time series. Specifically, the PM returns (13:05 to close) play a quite important role in the intra-day time. The findings also suggest that the unique “T+1 trading rule” in China may be a reason that incurs the lower opening price in the morning and the higher closing price in the afternoon, resulting in the statistically significant differences between the daytime and overnight returns. Practical implications The findings are of great importance for investors to decide when to buy and sell stock and futures portfolios in Chinese financial markets. Originality/value This study empirically analyzes why there the higher daytime returns and the lower overnight returns exist in the Chinese stock markets from different aspects and contributes the existing literature on day and night effects because of periodic market closures.
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Kurniawan, Arif, and Yulhendri Yulhendri. "Pengaruh Jumlah Anggota, Modal Sendiri dan Modal Pinjaman Terhadap Sisa Hasil Usah (SHU) Koperasi Unit Desa (KUD) Dikabupaten/Kota Provinsi Sumatera Barat." Jurnal Ecogen 3, no. 2 (June 5, 2020): 299. http://dx.doi.org/10.24036/jmpe.v3i2.8956.
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This study aims to find out 1) analyse the effect of the number of members on the remainder of the results of the business unit (SHU) village unit cooperatives (KUD), 2) To analyse the effect of their own capital on the residual results of the business unit (SHU) village unit cooperatives (KUD), 3) To analyse the effect of loan capital on the results of the remaining business units (SHU) of village unit cooperatives (KUD) of West Sumatra Province. The data used in this study are secondary data obtained directly from the statistical centre of West Sumatra Province. This study uses a time series method, namely from 2011-2017 and cross section totalling 19 districts / cities in West Sumatra Province. Data analysis method used in this study is panel data regression by testing the classical assumptions, including normality test, multicollinearity test, heteroscedasticity test, autocorrelation test with probability value (α) = 0.05. And statistical tests include t test, F test and testing the coefficient of determination (R2) with the value. The results of this study indicate that the number of members and their own capital have a significant influence on the residual income of village unit cooperatives (KUD), while loan capital does not significantly influence the residual income of village unit cooperatives (KUD) of West Sumatra Province.Keywords : SHU, number of members, own capital, loan capital.
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Chiew, Eng Woo, and Siok Kun Sek. "Examining the Effects of Domestic versus Global Prices Uncertainty on Sectoral Price Inflation in Malaysia." MATEMATIKA 35, no. 4 (December 31, 2019): 99–122. http://dx.doi.org/10.11113/matematika.v35.n4.1266.
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Price stability is one of the main policy objectives that is targeted by policymakers in many countries. Price uncertainty occurs due to the changes in market structure and consumer preference and expectation, which may affect price stability. In this study, the researchers aimed to examine the effects of price uncertainty of consumer price disaggregation in Malaysian sectors. To be specific, the researchers were seeking to discover on how domestic and global commodity prices can affect sectoral Consumer Price Index (CPI) on price inflation in Malaysia and most importantly, whether the effect is different for economic sectors in Malaysia. In addition, the effects of other factors (i.e., internal and external factors) on Malaysian sectoral CPI inflation were also studied. The threshold generalized autoregressive conditional heteroscedasticity (TGARCH) model was used to generate the price uncertainties. For the purpose of analysis, the threshold regression approach was applied based on time series of each single sector, followed by a combination of panel data of all sectors. The results differed across sectors, revealing that the impact of price uncertainties was determined by the sensitivity of each sector towards the price uncertainties. The effect of price increase is larger than the effect of price decrease. Price fluctuations were obvious in sectors that were dependent on consumer price or commodity price. Exchange rate and oil price inflation had also greatly influenced the CPI inflation.
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Darajati, Tuntun Sriwahyuni, and Deny Dwi Hartomo. "STRUKTUR MODAL SEKTOR PERBANKAN PADA SAAT KRISIS KEUANGAN." Jurnal Bisnis dan Manajemen 15, no. 1 (January 10, 2017): 17. http://dx.doi.org/10.20961/jbm.v15i1.4110.
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<p><em>Capital structure is a balance between the use of the capital itself and the debt. It means how much equity and debt to be used, so that it can produce an optimal capital structure. This study aims to determine the effect of internal and external factors on the capital structure of the banking sector during the financial crisis of 2008-2009. Independent variables used are profitability, growth, asset structure, risk, size, liquidity and IHSG. Beside that, IHSG also has the function as a moderating variable.</em></p><p><em>The method that is used for sample research is purposive sampling. It is based on certain criteria. The research was conducted on 72 samples of listed banks in Bank Indonesia, by using the data pooling. It is the combination between time series and the cross section data. Data analysis uses the analysis tools of regression test that was preceded by the classical assumptions, they are normality, multicollinearity, autocorrelation, and heteroscedasticity. Hypothesis test is done by using the F and T test.</em></p><p><em>The result of the data analysis shows that significant profitability, growth, asset structure, risk, size and IHSG that gives the affect to the capital structure of the bank.While liquidity does not affect the bank's capital structure. IHSG has given the proof that it does not moderate the internal variables of the bank's capital structure. </em></p>
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Massa, Ricardo, and Gustavo Fondevila. "Police crackdowns in Mexico City." Policing: An International Journal 42, no. 5 (October 10, 2019): 798–813. http://dx.doi.org/10.1108/pijpsm-11-2018-0165.
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Purpose The purpose of this paper is to analyze the design and implementation of the police crackdown strategy employed in Mexico City and to discuss its limitations toward a medium-to-long-term reduction of crime rates for six types of robberies. Design/methodology/approach The present work employs generalized autoregressive conditional heteroscedasticity (GARCH) models to estimate the effect of police operations on the volatility of the rates of six types of robberies in Mexico City, as well as their persistence over time. Findings Results suggest that the concentration of policing in certain high-criminality spaces reduces crime rates in the immediate term; however, its permanence is contingent on policing design and behavioral characteristics of the targeted crime. Specifically, the Mexico City police crackdown strategy seems to be better suited for combating crimes of a “non-static” nature than those of a “static” nature. Research limitations/implications Due to the nature of the data used for this research, the performed analysis does not enable a precise determination of whether the crime rates respond to temporal or spatial displacement. Practical implications Considering the obtained results, a re-design of Mexico City’s police crackdown strategy is suggested for the sustained reduction of the number of reported cases of robberies of a static nature. Originality/value Despite their importance, few studies have measured the impact of police crackdowns on city-level crime rates and whether their effect is temporary or permanent. The present study proposes the use of GARCH models in order to integrate the study of this phenomenon into criminal time series models.
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Stavytskyy, Andriy, and Daria Martynovych. "THE ECONOMETRIC MODELING OF UKRAINIAN MACROECONOMIC TENDENCIES." Ekonomika 91, no. 1 (January 1, 2012): 79–92. http://dx.doi.org/10.15388/ekon.2012.0.906.
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Econometric models are widely used in economic policies of many states. They help to build a great variety of econometric systems for every country and take into account the specifics of each economy.In this article, the structural macroeconomic models that describe the main aspects of the economic policy were applied. The interdependence between the level of inflation, the value of investment, savings, consumption, export and import transactions, taxes on the foreign trade were defined based on the analysis of the key macroeconomic parameters of Ukraine. After investigating all economic indicators, they were transformed into stationary time series for a correct use in the model. In addition, heteroscedasticity and autocorrelation of residuals were excluded in all econometric equations.As a result, the research shows that a large share of black economy leads to a rather high level of inflation in the state, because its value is primarily determined by expectations of the population under such circumstances. The paper indicates that the further export growth leads to a lower consumption growth and also to a lower growth of savings. Such a situation indicates an insufficient development of the domestic market. Investment growth has been fund not to be directly linked to consumption increase and economic development in general. Unfortunately, the main sources of investment in Ukraine are the funds of enterprises and foreign sources. The analysis shows a need to encourage public involvement into investment processes. For example, the creation of public–private partnerships is especially useful while implementing infrastructural projects.
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Oberst, Sebastian, Johanna Baetz, Graeme Campbell, Frank Lampe, Joseph C. S. Lai, Norbert Hoffmann, and Michael Morlock. "Vibro-acoustic and nonlinear analysis of cadavric femoral bone impaction in cavity preparations." MATEC Web of Conferences 148 (2018): 14007. http://dx.doi.org/10.1051/matecconf/201814814007.
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Owing to an ageing population, the impact of unhealthy lifestyle, or simply congenital or gender specific issues (dysplasia), degenerative bone and joint disease (osteoarthritis) at the hip pose an increasing problem in many countries. Osteoarthritis is painful and causes mobility restrictions; amelioration is often only achieved by replacing the complete hip joint in a total hip arthroplasty (THA). Despite significant orthopaedic progress related to THA, the success of the surgical process relies heavily on the judgement, experience, skills and techniques used of the surgeon. One common way of implanting the stem into the femur is press fitting uncemented stem designs into a prepared cavity. By using a range of compaction broaches, which are impacted into the femur, the cavity for the implant is formed. However, the surgeon decides whether to change the size of the broach, how hard and fast it is impacted or when to stop the excavation process, merely based on acoustic, haptic or visual cues which are subjective. It is known that non-ideal cavity preparations increase the risk of peri-prosthetic fractures especially in elderly people. This study reports on a simulated hip replacement surgery on a cadaver and the analysis of impaction forces and the microphone signals during compaction. The recorded transient signals of impaction forces and acoustic pressures (≈ 80 μs - 2 ms) are statistically analysed for their trend, which shows increasing heteroscedasticity in the force-pressure relationship between broach sizes. Tikhonov regularisation, as inverse deconvolution technique, is applied to calculate the acoustic transfer functions from the acoustic responses and their mechanical impacts. The extracted spectra highlight that system characteristics altered during the cavity preparation process: in the high-frequency range the number of resonances increased with impacts and broach size. By applying nonlinear time series analysis the system dynamics increase in complexity and demand for a larger minimum embedding dimension. The growing number of resonances with similar level of the transfer function indicates a higher propensity to dissipate energy over sound; the change in embedding dimension indicates a decrease in linearity. The spectral changes as well as the altered dimension requirements indicate either an improved coupling between the bone and the broach or the onset of micro-fractures caused by growing stress levels within the bone.
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Fatihudin, Didin, Sjamsul Hidajat, and Ma�ruf Sya�ban. "Implementation of investment and working capital financing allocated by banks towards the added GDP, labors, and welfare in four regencies in Madura." Journal of Economics, Business & Accountancy Ventura 18, no. 1 (June 1, 2015): 29. http://dx.doi.org/10.14414/jebav.v18i1.379.
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This study investigates the implementation of investment financing absorption and private bank sectors working capital to increase GDP, employment, and welfare of the four counties in Madura island (Bangkalan, Sampang, Pamekasan, Sumenep). This is the development of a previous study. This explanatory study is based on the model devel-opment concept or theory with Path Analysis through the data normality, multicolli-nearity, and heteroscedasticity test as well as causality. The data were taken from Bank Indonesia, Investment Coordinating Board, and the Central Bureau of Statistics. This is a time series data of 2002 to 2006. It shows that the financing of investment to GDP has significant and negative effect, financing of investment to labor absorption has signifi-cant and negative effect; financing working capital to GDP has significant and positive effect; financing of working capital to labor absorption has significant and negative effect; GDP in the labor market has no significant nor positive effect; GDP for the welfare effect, it has positive but not significant effect; employment in the welfare has a significant and positive effect. The direct effect or indirect implementation of financing from banks to finance investments and working capital to the entrepreneurs has increasingly a significant and positive effect. Absorption has dominated world finance working capital financing, following the least consumption and investment. Thus, it was natural that the implementa-tion of the investment credit and working capital has a significant and positive effect on economic growth, absorption of labor, and welfare in all four counties in Madura.