ANALISIS VOLATILITAS DAN FORECAST SAHAM PT ANTAM (PERSERO) Tbk. DAN PT ADARO ENERGY Tbk. DENGAN GARCH, EGARCH DAN GJR

An extraordinary event that causes shock can affect volatility which causes asymmetric variance and error or commonly called asimetric shock / effect. This paper aims to analyze the volatility of stock returns of PT ANTAM (Persero) Tbk and PT Adaro Energy Tbk in the period of 2008 to 2016. The research results show that ANTM and ADRO have a GARCH effect and also have a leverage effect where the optimal model is found in the GJR model (0,1,1) for ANTM and GJR (1,1,1) for ADRO. Forecasting results shows that ADRO has higher volatility but in a relatively low percentage of volatility about 0.001 while ANTM have a tendency to decrease volatility with a fairly large percentage of volatility about 0.0025.

Analisis Volatilitas Return Saham PT Antam (Persero) Tbk dan PT Adaro Energy Tbk Dengan Garch, Egarch Dan GJR

An extraordinary event that causes shock can affect volatility which causes asymmetric variance and error or commonly called asimetric shock / effect. This paper aims to analyze the volatility of stock returns of PT ANTAM (Persero) Tbk and PT Adaro Energy Tbk in the period of 2008 to 2016. The research results show that ANTM and ADRO have a GARCH effect and also have a leverage effect where the optimal model is found in the GJR model (0,1,1) for ANTM and GJR (1,1,1) for ADRO. Forecasting results shows that ADRO has higher volatility but in a relatively low percentage of volatility about 0.001 while ANTM have a tendency to decrease volatility with a fairly large percentage of volatility about 0.0025. Keywords: Volatility, GARCH, EGARCH, GJR

ANALISIS VOLATILITAS DAN FORECAST SAHAM PERUSAHAAN SEKTOR INDUSTRI OTOMOTIF DAN KOMPONEN PADA KOMPAS 100 YANG LISTING DI BURSA EFEK INDONESIA

ABSTRACTThe purpose of this study is to analyze volatility, choose the most optimal model andforecast of stock data on companies in various industrial sectors with the automotiveindustry and components sub-sector listed on the Stock Exchange during the period2011-2015. The return of stock data in the automotive sub-sector is modeled by theGARCH model. To see the effect of leverage, the data is re-modeled with the EGARCHand GJR models. Based on the information and probability criteria, it appears that themore optimal models are the GARCH model for AUTO, and GJR for ASII and GJTL.After the leverage effect is seen in the GJR model, then forecasting is done. Forecastingresults are in accordance with their respective optimal models in a 5% confidenceinterval, so it is expected that this model can forecast the price of future stock data.Keywords : Volatiliy, Forecast, GARCH, EGARCH, GJR�ABSTRAKTujuan penelitian ini adalah menganalisis volatilitas, memilih model yang palingoptimal dan melakukan forecast data saham pada perusahaan dalam sektor anekaindustri dengan sub sektor industri otomotif dan komponen yang listing di BEI selamaperiode 2011-2015. Data return saham sub sektor otomotif dimodelkan dengan modelGARCH. Untuk melihat adanya leverage effect, data dimodelkan kembali dengan modelEGARCH dan GJR. Berdasarkan information criteria dan likelihood, terlihat bahwamodel yang lebih optimal adalah model GARCH untuk AUTO, dan GJR untuk ASIIdan GJTL. Setelah leverage effect terlihat pada model GJR, kemudian dilakukanforecasting. Hasil forecasting sesuai dengan model optimalnya masing-masing beradadalam confidence interval 5%, sehingga diharapkan model tersebut dapatmenggambarkan harga data saham di masa yang akan datang.Kata Kunci : Volatilitas, Forecast, GARCH, EGARCH, GJR

Research on the Effect of Chinese Margin Trading on Market Risk: Based on GJR Model and Filtered Historical Simulation

In this paper, we address the issue that the financial institutes need to identify the risk of margin trading, and we analyze the volatility and value at risk of China’s Shanghai-Shenzhen 300 Index before and since the inception of margin trading policy. We first analyze the statistical attributes of the logarithmic return series. Then we build the GJR-GARCH to model the difference of volatility and leverage effect of the two sample time series. After that, we calculate the dynamic value at risk based on the parametric method. Moreover, we apply the filtered historical simulation with the help of Bootstrap technique to obtain the pathway of return and finally calculate the value at risk under the two circumstances. In the end, we propose some reasonable policies to financial risk management department.

Estimating and Forecasting Volatility of Financial Time Series in Pakistan with GARCH-type Models

In this paper we compare the performance of different GARCH models such as GARCH, EGARCH, GJR and APARCH models, to characterize and forecast financial time series volatility in Pakistan. The comparison is carried out by comparing symmetric and asymmetric GARCH models with normal and fat-tailed distributions for the innovations, over short and long forecast horizons. The forecasts are evaluated according to a set of statistical loss functions. Daily data on the Karachi Stock Exchange (KSE) 100 index are analyzed. The empirical results demonstrate that the use of asymmetry in the GARCH models and the assumption of fat-tail distributions for the innovations improve the volatility forecasts. Overall, EGARCH fits the best while the GJR model, with both normal and non-normal innovations, seems to provide superior forecasting ability over short and long horizons.