scholarly journals STL Decomposition and SARIMA Model: The Case for Estimating Value-at-Risk of Covid-19 Increment Rate in DKI Jakarta

2021 ◽  
Vol 7 (2) ◽  
pp. 1-10
Author(s):  
Agnes Zahrani ◽  
Aniq A. Rohmawati ◽  
Siti Sa’adah
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Extreme Values ◽  
Moving Average ◽  
Extreme Value ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Increment Rate ◽  
The Difference ◽  
Parameter Estimators

In this research, we propose an extreme values measure, the Value-at-Risk (VaR) based Seasonal Trend Loess (STL) Decomposition and Seasonal Autoregressive Integrated Moving Average (SARIMA) models, which is more sensitive to the seasonality of extreme value than the conventional VaR. We consider the problem of the seasonality and extreme value for increment rate of Covid-19 forecasting. For stakeholder, government and regulator, VaR estimation can be implemented to face the extreme wave of new positive Covid-19 in the future and minimize the losses that possibly affected in term of financial and human resources. Specifically, the estimation of VaR is developed with the difference lies on parameter estimators of STL and SARIMA model. The VaR has coverage probability as well as close 1-α. Thus, we propose to set α as parameter to estimate VaR. Consequently, the performance of VaR will depend not only on parameter model but also α. Our aim estimates VaR with minimum α based on correct VaR value. Numerical analysis is carried out to illustrate the estimative VaR.

scholarly journals PEMBENTUKAN PORTOFOLIO SAHAM DENGAN METODE MARKOWITZ DAN PENGUKURAN VALUE AT RISK BERDASARKAN GENERALIZED EXTREME VALUE (Studi Kasus: Saham Perusahaan The IDX Top Ten Blue 2017)

2018 ◽  
Vol 7 (2) ◽  
pp. 212-223
Author(s):  
Ria Epelina Situmorang ◽  
Di Asih I Maruddani ◽  
Rukun Santoso
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Extreme Values ◽  
Heavy Tail ◽  
Extreme Value ◽  
Return Rate ◽  
Optimal Portfolio ◽  
Average Risk ◽  
Generalized Extreme Value ◽  
Financial Investment

In financial investment, investors will try to minimize risk and increase returns for portfolio formation. One method of forming an optimal portfolio is the Markowitz method. This method can reduce the risk and increase returns. The performance portfolio is measured using the Sharpe index. Value at Risk (VaR) is an estimate of the maximum loss that will be experienced in a certain time period and level of trust. The characteristics of financial data are the extreme values that are alleged to have heavy tail and cause financial risk to be very large. The existence of extreme values can be modeled with Generalized Extreme Value (GEV). This study uses company stock data of The IDX Top Ten Blue 2017 which forms an optimal portfolio consisting of two stocks, namely a combination of TLKM and BMRI stocks for the best weight of 20%: 80% with the expected return rate of 0.00111 and standard deviation of 0.01057. Portfolio performance as measured by the Sharpe index is 1,06190 indicating the return obtained from investing in the portfolio above the average risk-free investment return rate of -0,01010. Risk calculation is obtained based on Generalized Extreme Value (GEV) if you invest both of these stocks with a 95% confidence level is 0,0206 or 2,06% of the current assets. Keywords: Portfolio, Risk, Heavy Tail, Value at Risk (VaR), Markowitz, Sharpe Index, Generalized Extreme Value (GEV).

scholarly journals Forecasting the COVID-19 Increment Rate in DKI Jakarta Using Non-Robust STL Decomposition and SARIMA Model

2021 ◽  
Vol 7 (1) ◽  
pp. 21-30
Author(s):  
Rosmelina Deliani Satrisna ◽  
Aniq A. Rohmawati ◽  
Siti Sa’adah
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Sarima Model ◽  
Forecasting Accuracy ◽  
Modeling And Analysis ◽  
Autoregressive Integrated Moving Average ◽  
The Public ◽  
Daily Number ◽  
Increment Rate ◽  
Sarima Models

The Corona virus known as COVID-19 was first present in Wuhan, China at this time has troubled many countries and its spread is very fast and wide. Data on daily confirmed COVID-19 cases were collected from the DKI Jakarta province between early May 2020 and late January 2021. The daily increase in confirmed COVID-19 cases has a percentage of the value of increase in total cases. In this study, modeling and analysis of forecasting the increment rate in daily number of new cases COVID-19 DKI Jakarta was carried out using the Seasonal-Trend Loess (STL) Decomposition and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. STL Decomposition is a form of algorithm developed to help decompose a Time Series, and techniques considering seasonal and non-stationary observation. The results of the best forecasting accuracy are proven by STL-ARIMA, there are MAPE and MSE which only have an error value of 0.15. This proposed approach can be used for consideration for the DKI Jakarta government in making policies for handling COVID-19, as well as for the public to adhere to health protocols.

scholarly journals Implications of the COVID-19 Lockdown on Dengue Transmission in Malaysia

2021 ◽  
Vol 13 (1) ◽  
pp. 148-160
Author(s):  
Song-Quan Ong ◽  
Hamdan Ahmad ◽  
Ahmad Mohiddin Mohd Ngesom
Keyword(s):  
Large Scale ◽  
Moving Average ◽  
Human Movement ◽  
Population Movement ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Predominant Species ◽  
Lower Confidence ◽  
Dengue Transmission ◽  
Stage 1

We aim to investigate the effect of large-scale human movement restrictions during the COVID-19 lockdown on both the dengue transmission and vector occurrences. This study compared the weekly dengue incidences during the period of lockdown to the previous years (2015 to 2019) and a Seasonal Autoregressive Integrated Moving Average (SARIMA) model that expected no movement restrictions. We found that the trend of dengue incidence during the first two weeks (stage 1) of lockdown decreased significantly with the incidences lower than the lower confidence level (LCL) of SARIMA. By comparing the magnitude of the gradient of decrease, the trend is 319% steeper than the trend observed in previous years and 650% steeper than the simulated model, indicating that the control of population movement did reduce dengue transmission. However, starting from stage 2 of lockdown, the dengue incidences demonstrated an elevation and earlier rebound by four weeks and grew with an exponential pattern. We revealed that Aedes albopictus is the predominant species and demonstrated a strong correlation with the locally reported dengue incidences, and therefore we proposed the possible diffusive effect of the vector that led to a higher acceleration of incidence rate.

scholarly journals Time-series modelling and forecasting of hand, foot and mouth disease cases in China from 2008 to 2018

2019 ◽  
Vol 147 ◽  
Author(s):  
C. W. Tian ◽  
H. Wang ◽  
X. M. Luo
Keyword(s):  
Time Series ◽  
Seasonal Variation ◽  
Moving Average ◽  
Foot And Mouth Disease ◽  
Mouth Disease ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Annual Peak ◽  
Time Series Modelling ◽  
Foot And Mouth

AbstractSeasonal autoregressive-integrated moving average (SARIMA) has been widely used to model and forecast incidence of infectious diseases in time-series analysis. This study aimed to model and forecast monthly cases of hand, foot and mouth disease (HFMD) in China. Monthly incidence HFMD cases in China from May 2008 to August 2018 were analysed with the SARIMA model. A seasonal variation of HFMD incidence was found from May 2008 to August 2018 in China, with a predominant peak from April to July and a trough from January to March. In addition, the annual peak occurred periodically with a large annual peak followed by a relatively small annual peak. A SARIMA model of SARIMA (1, 1, 2) (0, 1, 1)12 was identified, and the mean error rate and determination coefficient were 16.86% and 94.27%, respectively. There was an annual periodicity and seasonal variation of HFMD incidence in China, which could be predicted well by a SARIMA (1, 1, 2) (0, 1, 1)12 model.

Application of SARIMAX Model to Forecast Daily Sales in Food Retail Industry

2016 ◽  
Vol 7 (2) ◽  
pp. 1-21 ◽  
Author(s):  
Nari Sivanandam Arunraj ◽  
Diane Ahrens ◽  
Michael Fernandes
Keyword(s):  
Influencing Factors ◽  
Performance Measures ◽  
Moving Average ◽  
Food Product ◽  
Retail Store ◽  
Food Supply Chain ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Fresh Food ◽  
External Variables

During retail stage of food supply chain (FSC), food waste and stock-outs occur mainly due to inaccurate sales forecasting which leads to inappropriate ordering of products. The daily demand for a fresh food product is affected by external factors, such as seasonality, price reductions and holidays. In order to overcome this complexity and inaccuracy, the sales forecasting should try to consider all the possible demand influencing factors. The objective of this study is to develop a Seasonal Autoregressive Integrated Moving Average with external variables (SARIMAX) model which tries to account all the effects due to the demand influencing factors, to forecast the daily sales of perishable foods in a retail store. With respect to performance measures, it is found that the proposed SARIMAX model improves the traditional Seasonal Autoregressive Integrated Moving Average (SARIMA) model.

scholarly journals Value-at-risk modeling and forecasting with range-based volatility models: empirical evidence

2017 ◽  
Vol 28 (75) ◽  
pp. 361-376 ◽  
Author(s):  
Leandro dos Santos Maciel ◽  
Rosangela Ballini
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Garch Models ◽  
Time Interval ◽  
Risk Modeling ◽  
Additional Information ◽  
Volatility Models ◽  
Modeling And Forecasting ◽  
The Difference ◽  
Market Indexes

ABSTRACT This article considers range-based volatility modeling for identifying and forecasting conditional volatility models based on returns. It suggests the inclusion of range measuring, defined as the difference between the maximum and minimum price of an asset within a time interval, as an exogenous variable in generalized autoregressive conditional heteroscedasticity (GARCH) models. The motivation is evaluating whether range provides additional information to the volatility process (intraday variability) and improves forecasting, when compared to GARCH-type approaches and the conditional autoregressive range (CARR) model. The empirical analysis uses data from the main stock market indexes for the U.S. and Brazilian economies, i.e. S&P 500 and IBOVESPA, respectively, within the period from January 2004 to December 2014. Performance is compared in terms of accuracy, by means of value-at-risk (VaR) modeling and forecasting. The out-of-sample results indicate that range-based volatility models provide more accurate VaR forecasts than GARCH models.

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