Identifikasi Model I-Garch (Integrated Generalized Autoregressive Conditionally Heterocedastic) Untuk Peramalan Value At Risk

Dwipa, Nendra Mursetya Somasih

doi:10.31316/j.derivat.v3i1.626

Cited by 2 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(3) The GARCH equation above shows that the conditional variance is the volatility (ARCH) and the previous variance (GARCH) as seen from the squared residual ( ) and previous residual variance ( ) (Olowe, 2010). The things that characterize the GARCH model are the GARCH model in forecasting volatility with low accuracy and in many stock data, stock returns have an asymmetric effect that is not detected by the GARCH model (Dwipa, 2016).…”

Section: Volatility Modelmentioning

confidence: 99%

“…VaR can be defined as the maximum loss in a particular period with a certain level of trust. VaR estimates usually use the standard method assuming that the return has one variable and is a normal distribution with μ being the average and σ being the standard deviation (Dwipa, 2016). The equation for determining the var value is as follows:…”

Section: Value-at-riskmentioning

confidence: 99%

“…In financial markets, the measure of risk that determines the potential for loss can be Value-at-Risk. VaR is defined as the maximum potential loss in a certain period with a certain level of confidence under normal (market) conditions (Dwipa, 2016). VaR risk determination can be done using a time series model with historical data.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Determination of Risk Value Using the ARMA-GJR-GARCH Model on BCA Stocks and BNI Stocks

Hidayana¹,

Napitupulu²,

Saputra³

2021

Orics

View full text Add to dashboard Cite

Stocks are common investments that are in great demand by investors. Stocks are also an investment instrument that provides returns but tends to be riskier. The return time series is easier to handle than the price time series. In investment activities, there are the most important components, namely volatility and risk. All financial evaluations require accurate volatility predictions. Volatility is identical to the conditional standard deviation of stock price returns. The most frequently used risk calculation is Value-at-Risk (VaR). Mathematical models can be used to predict future stock prices, the model that will be used is the Glosten Jagannathan Runkle-generalized autoregressive conditional heteroscedastic (GJR-GARCH) model. The purpose of this study was to determine the value of the risk obtained by using the time series model. GJR-GARCH is a development of GARCH by including the leverage effect. The effect of leverage is related to the concept of asymmetry. Asymmetry generally arises because of the difference between price changes and value volatility. The method used in this study is a literature and experimental study through secondary data simulations in the form of daily data from BCA shares and BNI shares. Data processing by looking at the heteroscedasticity of the data, then continued by using the GARCH model and seeing whether there is an asymmetry in the data. If there is an asymmetric effect on the processed data, then it is continued by using the GJR-GARCH model. The results obtained on the two stocks can be explained that the analyzed stock has a stock return volatility value for the leverage effect because the GJR-GARCH coefficient value is > 0. So, the risk value obtained by using VaR measurements on BCA stocks is 0.047247 and on BNI stocks. is 0.037355. Therefore, the ARMA-GJR-GARCH model is good for determining the value of stock risk using VaR.

show abstract

Section: Volatility Modelmentioning

confidence: 99%

Section: Value-at-riskmentioning

confidence: 99%

See 1 more Smart Citation

Determination of Risk Value Using the ARMA-GJR-GARCH Model on BCA Stocks and BNI Stocks

Hidayana¹,

Napitupulu²,

Saputra³

2021

Orics

View full text Add to dashboard Cite

show abstract

“…(3) The GARCH equation above shows that the conditional si variant is volatility (ARCH) and the previous variance (GARCH) is seen from residual squares ( ) and the previous residual variant ( ) (Olowe, 2010). The things that are nature of the GARCH model are the GARCH model in its low-accuracy volatility forecasting and on many stock data, stock returns have an asymmetric influence that is not detected by the GARCH model (Dwipa, 2016).…”

Section: Volatility Modelmentioning

confidence: 99%

Determination of Value-at-Risk in UNVR Stocks Using ARIMA-GJR-GA RCH Model

Hidayana¹,

Napitupulu²

2021

Orics

View full text Add to dashboard Cite

Stocks are investment instruments that are in great demand by investors as a basis for storing finances. The most important thing in investing is the return and risk of loss obtained from investing in stocks. Risk measurement is carried out using Value-at-Risk and Conditional Value-at-Risk. The stock movements used are historical data and in the form of time series, so that a model can be formed to predict the next movement of stocks and risk measurements can be carried out. The purpose of this study is to determine the value of risk obtained by investors using time series analysis. The data used in this study is the daily closing price of stocks for 3 years. The stages of the analysis carried out to predict stock movements are to determine the ARIMA model for the mean model and the GJR-GARCH model for the volatility model. The mean value and variance are used to calculate the risk value of VaR. Based on the results of the Value-at-Risk calculation obtained, UNVR shares have a risk value of 0.01217. This means that if an investment is made in UNVR shares of IDR 100,000,000.00, the estimated maximum loss of potential loss that occurs is estimated to reach IDR 1,217,000.

show abstract

Identifikasi Model I-Garch (Integrated Generalized Autoregressive Conditionally Heterocedastic) Untuk Peramalan Value At Risk

Cited by 2 publications

References 0 publications

Determination of Risk Value Using the ARMA-GJR-GARCH Model on BCA Stocks and BNI Stocks

Determination of Risk Value Using the ARMA-GJR-GARCH Model on BCA Stocks and BNI Stocks

Determination of Value-at-Risk in UNVR Stocks Using ARIMA-GJR-GA RCH Model

Contact Info

Product

Resources

About