ANALISIS VOLATILITAS DAN PERAMALAN KURS JUAL RUPIAH TERHADAP RIYAL ARAB SAUDI MENGGUNAKAN MODEL ARCH/GARCH

Background:Visceral leishmaniasis in human (VLH) also known as kala-azar is a neglected disease of humans that mainly occurs in more than 50 countries mostly located in the Eastern Mediterranean and the Northern America.Objective:The purpose of this study was to determine the temporal patterns and predict of occurrence of VL in Ardabil Province, in northwestern Iran using autoregressive integrated moving average (ARIMA) models.Methods:This descriptive study employed yearly and monthly data of 602 cases of VLH in the province between January 2000 to December 2019, which was provided by the leishmaniasis national surveillance system. The monthly occurrences case constructed the ARIMA model of time-series model. The insignificance of the correlation in the lags of 12, 24 and 36 months, and Chi-square test showed the occurrence of VLH does not have a seasonal pattern. Eleven potential ARIMA models were examined for VLH cases. Finally, the best model was selected with the lower Akaike Information Criteria (AIC) and Bayesian information criterion (BIC) value. Then, the selected model was used to forecast frequency of monthly occurrences case. The forecasting precision was estimated by mean absolute percentage error (MAPE). Data analysis was performed using Stata14 and its package time series analysis.Results:ARIMA (5, 0, 1) model with AIC (25.7) and BIC (43.35) was selected. The MAPE value was 26.89% and the portmanteau test for white noise was (Q = 23.02, P = 0.98) for the residuals of the selected model showed that the data were fully modelled. The total cumulative VLH cases in the next 24 months’ in Ardabil province predicted 14 cases (95% CI: 4-54 case).Conclusion:The ARIMA (5, 0, 1) model can be a useful tool to predict VLH cases as early warning system and the results are helpful for policy makers and primary care physicians in the readiness of public health problems before the outbreak of the disease.

Background: COVID-19 has claimed the lives of millions of people in Nigeria and around the world during the last two years. It is a recognized global health crisis of our day, as well as a persistent threat to the earth. The goal of this study was to examine the trend and fit an Error Trend and Seasonal (ETS) exponential smoothing and Autoregressive Integrated Moving Average (ARIMA) model to Nigeria's COVID-19 daily fatalities.Methods: A dataset of daily COVID-19 confirmed fatality cases was used in the investigation. Data was acquired from the Nigerian Centre for Disease Control (NCDC) web database between the 10th of July 2020 and the 2nd of December 2021. The ARIMA model and twelve (12) ETS exponential smoothing techniques were investigated using a dataset of COVID-19 pandemic deaths in Nigeria. The ARIMA and ETS exponential smoothing algorithms were evaluated using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Hannan Quinn Information Criterion (HQC), and Average Mean Squared Error (AMSE) selection criteria.Result: The ARIMA (0,1,0) model was the best time series modeling for the coronavirus (COVID-19) epidemic in Nigeria since it had the lowest AIC=2863.51, BIC=2866.90, HQ = 2866.90, and AMSE = 0.55471 values.Conclusion: The ARIMA (0,1,0) model is preferred above the other thirteen (13) competing models based on daily confirmed COVID-19 deaths in Nigeria. This research would assist the Nigerian government in better understanding the pestilence's evolution pattern and providing adequate provisions, prompt mediation, and treatment to prevent additional deaths caused by the virus.

The present study has been carried out to study the impact of currency futures on the exchange rates volatility with respect to “euro”. The daily exchange rate values of euro vis-à-vis Indian rupee (INR) have been obtained for a period commencing from 1 January 2006 up to 30 September 2014. The time series data used in the study have been tested for stationarity by applying augmented Dicky and Fuller (ADF) test of unit root. The presence of heteroskedasticity in the residuals of return series of underlying data has been verified with autoregressive conditional heteroskedasticity Lagrange multiplier (ARCH LM) test. The volatility of the exchange rate return has been modelled with the help of generalized autoregressive conditional heteroskedasticity GARCH (1, 1) and Glosten–Jagannathan–Runkle (GJR) GARCH models. The results of GARCH models confirm that volatility is persistent and good news is causing more volatility than bad news. The difference in the volatility in the exchange rate returns during pre- and post-currency futures period has been examined with the help of various statistical tests and the results have been found to be significantly different and volatility has reduced in the post-futures period.

Precipitation regimes that change with global warming and climate changes affect the countries in environmental, economic, and social dimensions. The Marmara region is an important region located in the northwest of Turkey. The impact of economic, environmental, and social dimensions in the region is high. For this reason, the Marmara region is in a situation that can be affected more by climate change and drought. Precipitation forecasting is the first step for the management of agricultural planning, flood controls, and use of drinking water resources. Time series analysis is an important statistics tool that allows forecasting the amount of future precipitation based on the historical data analysis. Autoregressive Moving Average (ARMA), Autoregressive Integrated Moving Average (ARIMA), and Seasonal Autoregressive Integrated Moving Average (SARIMA) models are the most common statistical methods used to estimate precipitation based on time series. The ARMA, ARIMA, and SARIMA models are based on the assumption that past conditions will remain the same in the future. In this study, precipitation for the 9 cities in Turkey’s Marmara region is examined based on the 51-year (1969–2019) historical data and the ARMA, ARIMA, and SARIMA models are used to predict the precipitation in the next 60 months (up to 2024). While determining the model, the lowest AIC (Akaike information criterion) and AICc (corrected Akaike information criterion) are preferred and, generally, the AICc value is used to select the prediction model. After, the forecast measure errors of the models are checked with mean absolute error (MAE), root mean squared error (RMSE), and mean absolute scaled error (MASE) indicators. Finally, the ARIMA model is chosen as the most suitable model with the lowest estimation error.

Objective: The objective of this study was to forecast the number of critical days, for the period from July 2020 to December 2022, based on time series analysis, to model changes in the number of critical power days in a prospective way. Related Studies: This section provides an overview of the use of forecasting models and methods for measuring electricity demand, applied as decision-making support and as a tool for identifying anomalous phenomena. Method: The methodology for this research involves analysis and forecasting using the Autoregressive Integrated Moving Average (ARIMA) model of order (p,d,q)(p,d,q), applied to the time series of critical days. Model development and performance evaluation followed these steps and strategies: Specification (Pre-processing), Identification and Estimation, Verification, and Forecasting. Data collection was performed by analyzing time series observations from a Brazilian electric utility company responsible for energy supply in Rio Grande do Sul. Results and Discussion: The results indicated that the seasonal ARIMA (2,1,1)(2,1,1) model performed best, with the lowest Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values, as well as the lowest mean square error among the tested models. With the selected ARIMA model, forecasts were made for the following 30 months, estimating the number of monthly critical days. Research Implications: The paper discusses the practical and theoretical implications of forecasting critical days for power quality. Practically, it presents an ARIMA model to estimate these days, aiding power companies in projecting interruptions and improving preventive management and resource allocation. Theoretically, it contributes to the time series forecasting literature in the power sector, confirming the effectiveness of ARIMA models with seasonal components for grid management and adverse event forecasting in complex systems. Originality/Value: The study contributes to the literature by applying the ARIMA model with seasonal components to predict critical days of interruption in the distribution of electricity, an approach that has not yet been explored for specific adverse events. The originality lies in the application of the model in a context of quality and reliability in the supply and detailed seasonal analysis. The methodology proposes a solution for forecasting and managing these events, bringing new perspectives for proactive management in the sector. The study stands out for its potential to improve professional practice, allowing informed decisions for resource allocation and maintenance, increasing reliability and reducing costs and financial compensation.

Stock price data often exhibit nonlinear patterns and dynamics in nature. The parameter selection in generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive integrated moving average (ARIMA) models is challenging due to stock price volatility. Most studies examined the manual method for parameter selection in GARCH and ARIMA models. These procedures are time-consuming and based on trial and error. To overcome this, we considered a GWO method for finding the optimal parameters in GARCH and ARIMA models. The motivation behind considering the grey wolf optimizer (GWO) is one of the popular methods for parameter optimization. The novel GWO-based parameters selection approach for GARCH and ARIMA models aims to improve stock price prediction accuracy by optimizing the parameters of ARIMA and GARCH models. The hierarchical structure of GWO comprises four distinct categories: alpha (α), beta (β), delta (δ) and omega (ω). The predatory conduct of wolves primarily encompasses the act of pursuing and closing in on the prey, tracing the movements of the prey, and ultimately launching an attack on the prey. In the proposed context, attacking prey is a selection of the best parameters for GARCH and ARIMA models. The GWO algorithm iteratively updates the positions of wolves to provide potential solutions in the search space in GARCH and ARIMA models. The proposed model is evaluated using root mean squared error (RMSE), mean squared error (MSE), and mean absolute error (MAE). The GWO-based parameter selection for GARCH and ARIMA improves the performance of the model by 5% to 8% compared to existing traditional GARCH and ARIMA models.

In order to model and forecast exchange rates in both developed and emerging countries, majority of time series analysts have employed various technical and fundamental approaches, the forecast outcome differs depending on the approach chosen or implemented. In this view, this study is about hybridization of Autoregressive Integrated Moving Average (ARIMA) with Generalized Autoregressive Conditional Heteroscedastic (GARCH) model in forecasting exchange rate using monthly data of the Nigerian Naira against the U.S. Dollar for the period of January 2002 to February 2020. The stationarity of the exchange rate series is examined using unit root test of Augmented Dickey Fuller (ADF) test and Kwaitkowski-Philips-Schmidt-Shin (KPSS) which showed that the series is non stationary. To make the exchange rate series stationary, the data was transformed by first differencing and appropriate ARIMA models were obtained using Box-Jenkins method. ARIMA (0,1,1) and ARIMA(0,1,2) models were selected using AIC criteria and the residuals of these models were found to be serially correlated and heteroscedastic; hence the need for the hybridization of ARIMA with GARCH model. Therefore ARIMA models were hybridized with GARCH(1,1) to form ARIMA(0,1,1)-GARCH(1,1) and ARIMA(0,1,2)-GARCH(1,1). The results of forecast performance indicates that the best model is ARIMA(0,1,1)–GARCH(1,1) which has the lowest Root Means Square Error (RMSE) and Mean Absolute Error( MAE).

PurposeThis paper aims to explore the empirical determinants of exchange-rate volatility (ERV) in selected Asian economies, namely, Bangladesh, China, India, Indonesia, Malaysia and Pakistan. Specifically, it examines how the volatility of foreign reserves, government spending, industrial production, gold prices and terms of trade affect monthly ERV during the examined period.Design/methodology/approachThe authors carry out the empirical analysis by using monthly data for the period January 1997–March 2019. First, the volatility of the underlying variables is measured based on the conditional variances obtained by estimating the univariate (generalized) autoregressive conditional heteroskedasticity [(G)ARCH] model for each variable during the study period. Next, the autoregressive conditional heteroscedasticity (ARCH)-Lagrange multiplier test is applied to ensure that there are no remaining ARCH effects in the residuals. Finally, the multivariate autoregressive-moving average-GARCH (1, 1) models are estimated to examine whether and how the volatility of the underlying variables affects ERV.FindingsThe results reveal that the current period volatility of exchange rates is significantly affected by ERV in the previous period in all selected countries. The results also indicate that the volatilities of the underlying macroeconomic variables are quite differently related to ERV in examined Asian countries. Foreign-reserve volatility (VFXRES) has negative and significant impacts on ERV in Bangladesh, China and Malaysia. Government-spending volatility is negatively related to ERV in India, whereas it is positively related to ERV in all other examined countries. The results also suggest that although terms-of-trade volatility reduces ERV in both Bangladesh and Pakistan, it amplifies ERV in the remaining examined countries. However, gold-price volatility (VGOLDP) significantly, positively contributes to ERV in Bangladesh, Indonesia and Malaysia. On the contrary, the higher volatility in industrial production (VIPI) results in lower ERV in Indonesia and Pakistan, whereas it increases ERV in China, India and Malaysia.Practical implicationsThe findings have several important policy implications. First, the findings suggest that both Bangladesh and Malaysia should keep an adequate level of foreign reserves to stabilize their foreign exchange rates. Second, as government-spending volatility has a vital role in determining ERV, it is necessary to bring sustainability and continuity in government expenditures. Bangladesh and Pakistan can stabilize their foreign exchange rates by making exports more competitive, viable and accessible.Originality/valueThis paper significantly contributes to the existing literature by exploring how the behavior of unexpected variations in the factors determining exchange rates affects ERV in selected Asia countries. Most of the published studies have examined the determinants of exchange rates by considering the macroeconomic variables at their levels. Departing from the existing studies, this paper significantly relates the volatility (second moment) of exchange rate determinants to the behavior of ERV. Further, this paper provides firsthand empirical evidence on this issue for the selected Asian economies.

Purpose - Exchange rate volatility, which is defined as continuous fluctuations in exchange rates, has been frequently discussed in the literature recently due to its effects on developing economies. Exchange rate volatility is costly to the domestic economy through its direct and indirect effects on households and firms. Turkey implied different exchange rate regimes between 1980 and 2019. Also the use of exchange rate as a policy tool for fighting against inflation or current account deficit has increased exchange rate volatility in Turkey. The review of literature on the impact of exchange rate volatility on economic growth provides mixed results. The impact differs from developed to developing countries. The purpose of this study is to examine the impact of exchange rate volatility on economic growth in Turkey between 1998:Q1 and 2019:Q3. Methodology - This paper uses an Autoregressive Distributed Lag (ARDL) Model to analyze the effect of exchange rate volatility on economic growth in Turkey. Volatility of exchange rate is calculated from the real effective exchange rate by using the GARCH (1,1) model. ARDL model and the bounds testing approach has some advantages over other conventional cointegration approaches. Lagrange Multiplier (LM) test for autocorrelation and Ramsey RESET test for specification error were applied. One last diagnostic test of CUSUM and CUSUMSQ are used to check the stability of the short run and long run coefficient estimates. Findings- Estimation results of ARDL model show that real effective exchange rate volatility has a negative and highly statistically significant effect on economic growth in Turkey. From the long run coefficients export and investment have a significant positive effect on real GDP, import and exchange rate volatility have significant negative effect on real GDP. Conclusion- In order to ensure sustainable economic growth, it is necessary to strengthen the fiscal and financial structure and reduce the volatility in exchange rates. Financial deepening and fiscal discipline are very important in this respect. Changing the production structure and investing in education and high technology, increasing the domestic production of intermediate goods are also required for achieving high growth rates.

Forecasting is a major branch of statistics with several applications, particularly in econometrics. Many governments utilize it to develop long-term goals and make future decisions. The two main forecasting approaches are examined in this paper to discover the best forecasting model for the monthly amount of dairy products exported from Turkey to Iraq. The Autoregressive Integrated Moving Average (ARIMA) model is used in the first technique, known as Box-Jenkins, while the Feed Forward Neural Network (FFNN) model is used in the second. The data, which comes from the official websites of the UN Comtrade and the Turkish Statistical Institute (TUIK), contains the monthly volume of dairy products exported between 2010 and 2019. For analysis, three software tools Alyuda NeuroIntelligence, R, and SPSS were used. This comparison also included Akaike Information Criteria (AIC), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R2. According to the results, the FFNN model fits better than the ARIMA model. Furthermore, the FFNN model exhibits less errors than the ARIMA model and is much better in terms of goodness of fit due to lower MAE, RMSE, and AIC values.

In the global energy context, renewable energy sources such as wind is considered as a credible candidate for meeting new energy demands and partly substituting fossil fuels. Modelling and forecasting wind speed are noteworthy to predict the potential location for wind power generation. An accurate forecasting of wind speed will improve the value of renewable energy by enhancing the reliability of this natural resource. In this paper, the wind speed data from year 1990 to 2014 in 18 meteorological stations throughout Peninsular Malaysia were modelled using the Autoregressive Integrated Moving Average (ARIMA) to forecast future wind speed series. The Ljung-Box test was used to determine the presence of serial autocorrelation, while the Engle’s Lagrange Multiplier (LM) test was used to investigate the presence of Autoregressive Conditional Heteroscedasticity (ARCH) effect in the residual of the ARIMA model. In this study, three stations showed good fit using the ARIMA modelling since no serial correlation and ARCH effect were present in the residuals of the ARIMA model, while the ARIMA-GARCH had proven to precisely capture the nonlinear characteristic of the wind speed daily series for the remaining stations. The forecasting accuracy measure used was based on the value of root mean square error (RMSE) and mean absolute percentage error (MAPE). Both ARIMA and ARIMA-GARCH model proposed provided good forecast accuracy measure of wind speed series in Peninsular Malaysia. These results will help in providing a quantitative measure of wind energy available in the potential location for renewable energy conversion.

This research analyzes the patterns and trends of reorder points in inventory management over a two-year period (2023-2024), utilizing weekly time series data generated from daily data resampling. The ARIMA (Autoregressive Integrated Moving Average) method was applied to forecast future reorder point values. An Augmented Dickey-Fuller (ADF) stationarity test revealed that the initial data was non-stationary but became stationary after a single differencing operation. Parameter identification using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots indicated that the ARIMA(1,1,1) model was the best choice, based on the lowest Akaike Information Criterion (AIC). Model accuracy was evaluated using Mean Absolute Percentage Error (MAPE), yielding a value of 0.02%, signifying an excellent level of prediction accuracy. Consequently, the ARIMA model is demonstrated to be reliable for forecasting reorder points, supporting more precise decision-making in inventory management.

Fossil fuels continue to be a critical component of the global energy landscape in the 21st century, meeting a large portion of the world's energy needs. Despite the growing awareness of their environmental impacts, fossil fuels—namely coal, gas, and oil—are still widely used due to their availability and efficiency. These fuels are essential for powering industries, transportation, and supporting modern lifestyles. However, the significant carbon dioxide (CO2) emissions from fossil fuel combustion present a serious environmental challenge, contributing to global climate change. Understanding the ongoing importance of fossil fuels emphasizes the urgent need to innovate and transition to sustainable energy solutions. Such a shift is crucial for reducing CO2 emissions and ensuring a more environmentally responsible and resilient future. This study examines the consumption patterns of fossil fuels—specifically coal, gas, and oil—in Bangladesh, India, and Pakistan. To predict future demand for these non-renewable resources, the study utilized Autoregressive Integrated Moving Average (ARIMA) models with different (p, d, q) parameters. By using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots, the study identified suitable ARIMA models for each fuel type, selecting the best models based on the lowest Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values. The study successfully forecasted the demand for coal, gas, and oil over the next decade in the three countries. These insights are crucial for planning the production of renewable energy, reducing fossil fuel consumption, and promoting a sustainable environment for future generations.

This study examined the determinants of exchange rate volatility basing evidence on 7 African countries; Niger, Sudan, Cameron, Equatorial Guinea, Tunisia, Congo, and Cote D’Ivoire from 1990-2023. The study conducted the Autoregressive Distributive Lag (ARDL) bounds testing for co-integration and also estimated the error correction model. Furthermore, ARCH and GARCH models were analyzed to measure the volatility of a time series by fitting an autoregressive model to the squared residuals of the time series. The ARCH and GARCH results suggest the volatility of the exchange rate markets in the aforementioned countries is not random. The speed of adjustment of the volatility in the exchange rate of the Sudanese economy is 39%, in Niger Republic it is 50%, in Cameroon it is 52%, in Tunisia it is 55%, in Congo the speed is 32%, in Equatorial Guinea, the speed of adjustment is 58% and in Côte D’Ivoire the speed is 45%, respectively. The study found that the determinants of exchange rate volatility among African countries vary depending on the specific country. The observed volatility in the Sudanese exchange rate was anchored by the significant positive influence of inflation and income differentials as well as the significant negative influence of interest rate differential. In Niger Republic, exchange rate volatility was driven by the significant positive influence of productivity growth and money supply as well as the significant variation in oil prices and interest rate differentials. The observed short-run volatility in Cameroon's exchange rate was significantly and positively influenced by inflation differential and money supply variation whereas it was significantly but negatively propelled by interest rate differential and oil price shock. In Tunisia, exchange rate volatility was stimulated by the significant positive influence of inflation differential, productivity growth, oil price shock, and the significant negative role played by trade balance. The observed short-run volatility in the Congolese exchange rate was induced by the significant positive impact of inflation differential, income differential, trade balance, variation in money supply, and the significant negative impact of interest rate differential. In Equatorial Guinea, the observed exchange rate volatility was determined based on the significant and positive impact of differential in the inflation rate, oil price shock, changes in the money stock, and the foreign balance of trade. The observed volatility in the Côte D’Ivoire exchange rate was significantly and positively driven by the differentials in inflation rate, interest rate, and income level, the foreign trade balance but significantly stimulated by the negative influence of oil price shock. The general policy advice is that governments of all the countries covered by the study should implement exchange rate controls to limit the volatility of their currency fluctuation by imposing a limit on the amount of foreign currency that can be traded in the country. African governments should monitor the inflation differential between their own country and their trading partners to see if it is becoming too large. If it is, the government might raise interest rates to make its currency more attractive to investors.

Background Breast cancer represents a significant public health concern in India, accounting for 28% of all cancer diagnoses and imposing a substantial economic burden. This study introduces a novel approach to forecasting the number of breast cancer cases (based on prevalence rates) and estimating the associated economic impact in India using the autoregressive integrated moving average (ARIMA) model. Methods Data on the prevalence of breast cancer in India from 2000 to 2021 were obtained from the Global Burden of Disease (GBD) database. This dataset provided annual estimates of the number of patients with breast cancer, serving as the basis for modeling future prevalence and estimating the economic burden. The ARIMA (Auto-Regressive Integrated Moving Average) model was employed to analyze and predict breast cancer prevalence in India up to the year 2030 (time-series forecasting). Data were visualized and checked for stationarity using the Augmented Dickey-Fuller (ADF) test. Using the autocorrelation function (ACF) and partial autocorrelation function (PACF) plots, the appropriate parameters (p, d, q) were determined. Several ARIMA configurations were tested to identify the model with the best fit. The goodness-of-fit of the model was assessed using standard metrics such as the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The residuals were tested using the Box-Ljung test to confirm the absence of autocorrelation and verify that they followed a white noise distribution. Using the fitted ARIMA model, prevalence rates were forecasted from 2022 to 2030, with 95% confidence intervals to capture prediction uncertainty. Direct costs were calculated based on medical expenses for breast cancer patients, such as hospital visits, diagnostic tests, treatment costs, and follow-up care. A bottom-up approach was applied, which involves aggregating individual cost components from each stage of care to estimate the total direct burden of disease. A bottom-up approach was applied, which involves aggregating individual cost components from each stage of care to estimate the total direct burden of disease. Indirect costs were estimated using the human capital approach, which assesses productivity losses due to morbidity and premature mortality. The Disability-Adjusted Life Years (DALY) associated with breast cancer were also predicted using the ARIMA model. Results The results of coefficient of determination (0.99), mean absolute percentage error (69%), mean absolute error (5229), and root mean squared error (6451.2) showed that the ARIMA (0,2,0) model fitted well. Coefficient of determination (0.99) indicated that 99% of the variance in the data was explained by the model. Akaike information criterion (411.54) and Bayesian information criterion (412.53) indicated the ARIMA (0,2,0) model was reliable when analysing our data. The result of the relative error of prediction (2.76%) also suggested that the model predicted well. The number of patients with breast cancer from 2021 to 2030 was predicted to be about 1.25 million, 1.1.29 million,, 1.34 million, 1.39 million, 1.44 million, 1.48 million, 1.53 million, 1.58 million, 1.63 million, 1.68 million, and respectively. The total economic burden of breast cancer from 2021 to 2030 was estimated to be $8 billion, $8.72 billion, $9.05 billion, $9.84 billion, $10.20 billion, $11.07 billion, $11.49 billion, $12.44 billion, $12.91 billion, $13.95 billion, respectively is estimated to rise significantly. Conclusion Breast cancer prevalence and its economic impact are projected to grow substantially in India. Between 2021 and 2030, the number of breast cancer patients is expected to increase by approximately 0.05 million annually, with an annual increase rate of about 5.6%. The associated economic burden will also rise, averaging an additional $19.55 billion per year, underscoring the need for intensified healthcare and economic strategies to manage this growing challenge.