ARIMA-GARCH Model and ARIMA-GARCH Ensemble for Value-at-Risk Prediction on Stocks Portfolio

Tarno, Tarno; Maruddani, Di Asih I; Rahmawati, Rita; Hoyyi, Abdul; Trimono, Trimono; Munawar, Munawar

doi:10.20944/preprints202010.0191.v1

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Often on stock investments, the return data obtained do not follow the normal distribution but tend to have a thick tail distribution (leptokurtic). This condition causes the parametric method not easy to apply to any stock data [24]. Therefore, a nonparametric approach was introduced to overcome this problem.…”

Section: Historical Simulation Approach For Var Predictionmentioning

confidence: 99%

Comparison Between Value at Risk and Adjusted Expected Shortfall: A Numerical Analysis

Trimono,

Maruddani

2023

BAREKENG: J. Math. & App.

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Loss risk is one of the variable that always appears in every kind of investment. On stock asset investments, the characteristics of the risk of loss is uncertain, this means that losses can occur at any time with a value that cannot be determined certainly. From this condition, investors must manage the loss risk appropriately in order to retain investment stability and get optimal profits. One of the important processes in risk management is loss risk forecast. Risk forecast can be done using risk measures. In stock investment, Value at Risk (VaR) is the most widely used risk measure because has a simple model and can be applied to many types of stocks. However, VaR does not satisfy the axiom of subadditivity, thus VaR is not a coherent risk measure. Another risk measure that is coherent and can be used as an alternative to predict loss risk is the Adjusted-Expected Shortfall (Adj-ES). This study aims to compare VaR and Adj-ES through numerical analysis and backtesting test. So we can get reference to conclude the best risk measure for predicting losses on stock investments. The data used in this study are 2022 IDX blue chip i. e EXCL.JK and ICBP.JK from 09/01/21 to 09/09/22. Based on the backtesting test, the violation ratio value for Adj-ES in every violation probability is less than 1 is less than 1. Then, for VaR at 1% violation probability, the violation ratio value is > 1.

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Section: Historical Simulation Approach For Var Predictionmentioning

confidence: 99%

Comparison Between Value at Risk and Adjusted Expected Shortfall: A Numerical Analysis

Trimono,

Maruddani

2023

BAREKENG: J. Math. & App.

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“…The best regression model selection is chosen based on the model by the smallest value of AIC. The formula of AIC described as follows [21]:…”

Section: The Best Regression Model Selectionmentioning

confidence: 99%

Time Series Regression: Prediction of Electricity Consumption Based on Number of Consumers at National Electricity Supply Company

Idhom,

Fauzi,

Trimono

et al. 2023

TEM Journal

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Electrical energy is one of the components of Gross Domestic Product that is able to encourage the economy because it has become a basic need of the community. To meet the increasing demand for electrical energy, the Indonesia National Electricity Providers (PLN) need to predict the amount of electrical power required based on the customer numbers to meet the demand for adequate electricity supply. This study aims to predict electric power based on electricity user customers using a time series regression model. The data used in this study are secondary data which get from PLN annual report in 2021. This study resulted in a finding of the best prediction model based on the Akaike Information Criterion (AIC) value, namely the time series regression model with the error value modeled by the AR(1) model, while the forecasting accuracy measure used the value MAPE of 9.77%. This means that the result of model prediction is highly accurate.

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Modelling and Estimating of VaR Through the GARCH Model

Kannan,

Parimyndhan

2023

Communications in Computer and Information Science

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ARIMA-GARCH Model and ARIMA-GARCH Ensemble for Value-at-Risk Prediction on Stocks Portfolio

Cited by 3 publications

References 7 publications

Comparison Between Value at Risk and Adjusted Expected Shortfall: A Numerical Analysis

Comparison Between Value at Risk and Adjusted Expected Shortfall: A Numerical Analysis

Time Series Regression: Prediction of Electricity Consumption Based on Number of Consumers at National Electricity Supply Company

Modelling and Estimating of VaR Through the GARCH Model

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