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

Yasir Maulana

Abstract


ABSTRACT
The purpose of this study is to analyze volatility, choose the most optimal model and
forecast of stock data on companies in various industrial sectors with the automotive
industry and components sub-sector listed on the Stock Exchange during the period
2011-2015. The return of stock data in the automotive sub-sector is modeled by the
GARCH model. To see the effect of leverage, the data is re-modeled with the EGARCH
and GJR models. Based on the information and probability criteria, it appears that the
more 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. Forecasting
results are in accordance with their respective optimal models in a 5% confidence
interval, so it is expected that this model can forecast the price of future stock data.
Keywords : Volatiliy, Forecast, GARCH, EGARCH, GJR

 

ABSTRAK
Tujuan penelitian ini adalah menganalisis volatilitas, memilih model yang paling
optimal dan melakukan forecast data saham pada perusahaan dalam sektor aneka
industri dengan sub sektor industri otomotif dan komponen yang listing di BEI selama
periode 2011-2015. Data return saham sub sektor otomotif dimodelkan dengan model
GARCH. Untuk melihat adanya leverage effect, data dimodelkan kembali dengan model
EGARCH dan GJR. Berdasarkan information criteria dan likelihood, terlihat bahwa
model yang lebih optimal adalah model GARCH untuk AUTO, dan GJR untuk ASII
dan GJTL. Setelah leverage effect terlihat pada model GJR, kemudian dilakukan
forecasting. Hasil forecasting sesuai dengan model optimalnya masing-masing berada
dalam confidence interval 5%, sehingga diharapkan model tersebut dapat
menggambarkan harga data saham di masa yang akan datang.
Kata Kunci : Volatilitas, Forecast, GARCH, EGARCH, GJR


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References


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DOI: https://doi.org/10.25134/ijsm.v2i2.1967

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