scholarly journals Tracking COVID-19 Decease Through Rolling Conditional Variance

2021 ◽  
Vol 12 (4) ◽  
pp. 111
Author(s):  
Cesar Gurrola-Rios ◽  
Ana Lorena Jimenez-Preciado
Keyword(s):  
Economic Development ◽  
Moving Average ◽  
Mortality Rates ◽  
Conditional Variance ◽  
Disease Spread ◽  
Autoregressive Integrated Moving Average ◽  
Rolling Window ◽  
Spread Dynamics ◽  
The Face ◽  
Autoregressive Conditional Heteroscedasticity

The effects of COVID-19 have been devastating globally. However, countries have essential asymmetries regarding the disease spread dynamics and the respective mortality rates. In addition to containment strategies and boosting growth and economic development in the face of the COVID-19 pandemic, society calls for solutions that allow the development of vaccines, treatments for the disease, and especially, indicators or early warnings that anticipate the evolution of new infections and deaths. This research aims to track the total deaths caused by COVID-19 in the most affected countries by the pandemics after the approval, distribution, and implementation of vaccines from 2021. We proposed an Autoregressive Integrated Moving Average (ARIMA) specification as a first adjustment. Subsequently, we estimate the conditional variance of total deaths from an Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH). Finally, we compute a rolling density backtesting within a 7-day rolling window to demonstrate the robustness estimation for COVID-19 mortality. The work's main contribution lies in exhibiting a tracking indicator for volatility and COVID-19 direction, including a weekly window to observe its evolution.

scholarly journals A Correcting Note on Forecasting Conditional Variance Using ARIMA vs. GARCH Model

2019 ◽  
Vol 11 (5) ◽  
pp. 145
Author(s):  
Mohammad Naim Azimi ◽  
Seyed Farhad Shahidzada
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Conditional Variance ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Out Of Sample ◽  
Moving Average Model ◽  
Catch Up ◽  
Autoregressive Conditional Heteroscedasticity

In this study, we demonstrate that a common approach in using the Autoregressive Integrated Moving Average model is not efficient to forecast all types of time series data and most specially, the out-of-sample forecasting of the time series that exhibits clustering volatility. This gap leads to introduce a competing model to catch up with the clustering volatility and conditional variance for which, we empirically document the efficient and lower error use of the Generalized Autoregressive Conditional Heteroscedasticity model instead.

scholarly journals STUDI PERBANDINGAN MODEL ARIMA DAN GARCH UNTUK MEMPREDIKSI HARGA SAHAM PADA PERUSAHAAN TAMBANG DI INDONESIA

2020 ◽  
Vol 10 (1) ◽  
pp. 83-98
Author(s):  
Muhammad Tharmizi Junaid ◽  
Ahmad Juliana ◽  
Hardianti Sabrina
Keyword(s):  
Moving Average ◽  
Conditional Heteroscedasticity ◽  
Autoregressive Integrated Moving Average ◽  
Autoregressive Conditional Heteroscedasticity

Dalam berinvestasi para investor menggunakan alat statistik salah satunya adalah peramalan. Peramalan dilakukan oleh investor untuk memperlancar transaksi, meningkatkan keuntungan ataupun meminimalisir terjadinya kerugian. Dengan melakukan peramalan, investor diharapkan dapat membuat keputusan investasi yang tepat. Penelitian ini bertujuan untuk mengetahui model peramalan yang akurat untuk meramalkan harga saham PT. Adaro Energy (ADRO) dan saham PT. Bukit Asam  (PTBA) periode data selama 10 tahun sejak Oktober 2008 sampai dengan Oktober 2018. Keterbaharuan dalam penelitian ini adalah membandingkan dua model Autoregressive Integrated Moving Average (ARIMA) dan Generalized Autoregressive Conditional Heteroscedasticity (GARCH) sehingga dapat diketahui model yang memiliki tingkat keakuratan terbaik untuk meramalkan harga saham pada periode mendatang. Hasil dari penelitian ini menggambarkan bahwa terdapat unsur heterokedastisitas pada saham ADRO sehingga pemodelan tidak berhenti pada model ARIMA namun dilanjutkan sampai model GARCH. Sedangkan data saham PTBA tidak mengandung unsur heterokedastisitas sehingga pemodelan hanya sampai model ARIMA. Pada saham ADRO model ARIMA mempunyai tingkat kesalahan yang lebih kecil dibandingkan model GARCH. Pada saham PTBA model ARIMA juga terpilih sebagai model yang paling akurat. Kata Kunci: ARIMA, GARCH, dan Pertambangan

Detecting Hidden Relations between Time Series of Mortality Rates

1990 ◽  
Vol 29 (01) ◽  
pp. 57-60 ◽  
Author(s):  
U. Helfenstein
Keyword(s):  
Environmental Factor ◽  
Present Report ◽  
Moving Average ◽  
Time Lag ◽  
The Elderly ◽  
Mortality Rates ◽  
Autoregressive Integrated Moving Average ◽  
Moving Average Model ◽  
Crosscorrelation Function ◽  
Yearly Data

AbstractIn the present report a method is described which may help to decide if a disease is influenced by an environmental factor which fluctuates in time: For each of two naturally arising subgroups of a population (such as males and females) an ARI MA model (autoregressive integrated moving average model) is identified. These models are used as filters to remove the autocorrelation in each series. If the resulting crosscorrelation function between the two filtered series shows a marked peak at time lag 0 this may indicate that such an environmental factor is present. The procedure is demonstrated using yearly data of mortality rates among the elderly.

scholarly journals Time series prediction of under-five mortality rates for Nigeria: comparative analysis of artificial neural networks, Holt-Winters exponential smoothing and autoregressive integrated moving average models

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Daniel Adedayo Adeyinka ◽  
Nazeem Muhajarine
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Mean Absolute Error ◽  
Moving Average ◽  
Absolute Error ◽  
Mortality Rates ◽  
Childhood Mortality ◽  
Autoregressive Integrated Moving Average ◽  
Under Five ◽  
Deming Regression

Abstract Background Accurate forecasting model for under-five mortality rate (U5MR) is essential for policy actions and planning. While studies have used traditional time series modeling techniques (e.g., autoregressive integrated moving average (ARIMA) and Holt-Winters smoothing exponential methods), their appropriateness to predict noisy and non-linear data (such as childhood mortality) has been debated. The objective of this study was to model long-term U5MR with group method of data handling (GMDH)-type artificial neural network (ANN), and compare the forecasts with the commonly used conventional statistical methods—ARIMA regression and Holt-Winters exponential smoothing models. Methods The historical dataset of annual U5MR in Nigeria from 1964 to 2017 was obtained from the official website of World Bank. The optimal models for each forecasting methods were used for forecasting mortality rates to 2030 (ending of Sustainable Development Goal era). The predictive performances of the three methods were evaluated, based on root mean squared errors (RMSE), root mean absolute error (RMAE) and modified Nash-Sutcliffe efficiency (NSE) coefficient. Statistically significant differences in loss function between forecasts of GMDH-type ANN model compared to each of the ARIMA and Holt-Winters models were assessed with Diebold-Mariano (DM) test and Deming regression. Results The modified NSE coefficient was slightly lower for Holt-Winters methods (96.7%), compared to GMDH-type ANN (99.8%) and ARIMA (99.6%). The RMSE of GMDH-type ANN (0.09) was lower than ARIMA (0.23) and Holt-Winters (2.87). Similarly, RMAE was lowest for GMDH-type ANN (0.25), compared with ARIMA (0.41) and Holt-Winters (1.20). From the DM test, the mean absolute error (MAE) was significantly lower for GMDH-type ANN, compared with ARIMA (difference = 0.11, p-value = 0.0003), and Holt-Winters model (difference = 0.62, p-value< 0.001). Based on the intercepts from Deming regression, the predictions from GMDH-type ANN were more accurate (β0 = 0.004 ± standard error: 0.06; 95% confidence interval: − 0.113 to 0.122). Conclusions GMDH-type neural network performed better in predicting and forecasting of under-five mortality rates for Nigeria, compared to the ARIMA and Holt-Winters models. Therefore, GMDH-type ANN might be more suitable for data with non-linear or unknown distribution, such as childhood mortality. GMDH-type ANN increases forecasting accuracy of childhood mortalities in order to inform policy actions in Nigeria.

scholarly journals Time Series Modelling of Inflation in Botswana Using Monthly Consumer Price Indices

2016 ◽  
Vol 8 (3) ◽  
pp. 15
Author(s):  
Kesaobaka Molebatsi ◽  
Mpho Raboloko
Keyword(s):  
Time Series ◽  
Confidence Intervals ◽  
Price Index ◽  
Moving Average ◽  
Consumer Price Index ◽  
Conditional Heteroscedasticity ◽  
Autoregressive Integrated Moving Average ◽  
Time Series Modelling ◽  
Error Terms ◽  
Autoregressive Conditional Heteroscedasticity

<p>This paper identifies an autoregressive integrated moving average (ARIMA (1,1,1)) model that can be used to model inflation measured by the consumer price index (CPI) for Botswana. The paper proceeds to improve the model by incorporating the generalized autoregressive conditional heteroscedasticity (ARCH/GARCH) model that takes into consideration volatility in the series. Ultimately, CPI is forecast using the two models, ARIMA (1, 1, 1) and ARIMA (1, 1, 1) + GARCH (1, 2) and compared with the actual CPI. Both models perform well in terms of forecasting as their 95 percent confidence intervals cover the actual CPI. Marginal differences that favour the inclusion of the ARCH/GARCH components were observed when testing for normality among error terms. The paper also reveals that volatility for Botswana’s CPI is low as shown by small values of ARCH/GARCH components.</p>

scholarly journals Econometric Models for Forecasting Remittances of Bangladesh

2017 ◽  
Vol 4 (1) ◽  
pp. 1
Author(s):  
Tamanna Islam ◽  
Ashfaque A. Mohib ◽  
Shahnaz Zarin Haque
Keyword(s):  
Foreign Exchange ◽  
Migrant Workers ◽  
Industrial Development ◽  
Raw Materials ◽  
Moving Average ◽  
Critical Role ◽  
Foreign Exchange Earning ◽  
Autoregressive Integrated Moving Average ◽  
Future Situation ◽  
Autoregressive Conditional Heteroscedasticity

At present, the remittance of Bangladesh (RB) is the largest source of foreign exchange earning of the country. The RB plays a critical role in alleviating the foreign-exchange constraint and supporting the balance of payments, enabling imports of capital goods and raw materials for industrial development. Remittance from overseas migrant workers certainly increases the income disparity between classes of the rural society. Therefore forecasting plays an important role to know the future situation of economic condition. This paper employed the prospective data on RB to derive a unique and suitable forecasting model. The data were collected from Bangladesh Bank (BB) during January, 1998 to December, 2003. The Autoregressive Integrated Moving Average (ARIMA) and the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models were used to find out the best one. The findings indicated that the ARIMA (0,1,1) (0,2,1)12 and the GARCH (2,1) models were appropriate for our data and the GARCH (2,1) model appeared to be the best one between these.

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

2021 ◽  
Vol 3 (3) ◽  
pp. 171-177
Author(s):  
Yulvia Fitri Rahmawati ◽  
Etik Zukhronah ◽  
Hasih Pratiwi
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Moving Average ◽  
Arima Model ◽  
Training Data ◽  
Series Data ◽  
Arch Model ◽  
Conditional Heteroscedasticity ◽  
Autoregressive Integrated Moving Average ◽  
Autoregressive Conditional Heteroscedasticity

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

scholarly journals Assessing the Recovery Rate of Inbound Tourist Arrivals Amid COVID-19: Evidence from the Maldives

2021 ◽  
Vol 7 (6) ◽  
pp. 7-15
Author(s):  
Ahmed Rabeeu ◽  
Chen Shouming ◽  
Md Abid Hasan ◽  
Disney Leite Ramos ◽  
Abdul Basit Abdul Rahim
Keyword(s):  
Recovery Rate ◽  
Moving Average ◽  
Vital Role ◽  
Tourism Industry ◽  
Policy Implications ◽  
Autoregressive Integrated Moving Average ◽  
Tourism Management ◽  
The Face ◽  
The Government ◽  
The Impact

The present study examines the impact of COVID-19 on Maldivian tourism, highlighting the loss of tourists and tourism earnings for the period 2020Q1 to 2021Q2 and analyses the recovery rate of inbound tourists’ arrivals post border re-opening (i.e., 2020Q3 – 2021Q2). Seasonal Autoregressive Integrated Moving Average (SARIMA) model was employed to generate monthly forecasts for 2020 and 2021. The results indicate an estimated loss of 1.9 million tourists between 2020Q1 and 2021Q2. A massive drop in tourist arrivals caused an estimated loss of USD 3.5 billion in tourism earnings by June 2021. Results further indicate that with an average monthly recovery rate of 3%, inbound arrivals have recovered 34% of forecasted levels and 40% of 2019 levels by June 2021. The measures implemented by the government of Maldives played a vital role in the recovery of inbound tourism. However, the rebound of tourists has not reached the desired levels except for the arrivals from Russia. Therefore, additional strategies must be implemented for the quick revival of the Maldivian tourism industry. This study expands and enriches tourism management knowledge in the face of a massive crisis highlighting important managerial and policy implications for reviving the tourism industry of the Maldives.

scholarly journals Persistence effect determination of variability in forecasting of agricultural and road machinery national production

2020 ◽  
Vol 50 (6) ◽  
Author(s):  
Tailon Martins ◽  
Alisson Castro Barreto ◽  
Daniel Arruda Coronel ◽  
Luciane Flores Jacobi ◽  
Valentina Wolff Lirio ◽  
...  
Keyword(s):  
Moving Average ◽  
National Association ◽  
Short Term ◽  
Conditional Heteroscedasticity ◽  
Autoregressive Integrated Moving Average ◽  
Effect Data ◽  
Long Period ◽  
National Production ◽  
Autoregressive Conditional Heteroscedasticity

ABSTRACT: The objective of this research was to forecast the Brazilian national production of agricultural and road machinery in the short term by BOX & JENKINS methodology and determine the persistence effect. Data were obtained at National Association of Automotive Vehicle Manufacturers (ANFAVEA) from January 1960 to October 2019, totaling 718 monthly observations. The Autoregressive Integrated Moving Average (ARIMA) and Autoregressive Conditional Heteroscedasticity (ARCH) methodology were used. The ARIMA (2,1,1)-ARCH (2) model was fitted and persistence of 0.60 was determined, showing that the instability in the series will be for a long period of time.

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