The GARCH-X(1,1) Model with Exponentially Transformed Exogenous Variables
DOI:
https://doi.org/10.23887/jstundiksha.v12i1.50714Keywords:
ARWM, Exponential transformation, GARCH-X, Student-t, VolatilityAbstract
Model Generalized Autoregressive Conditional Heteroskedasticity (GARCH) dengan mempertimbangkan efek dari variabel eksogen pada proses volatilitas, dinamakan GARCH-X(1,1), telah sukses memperbaiki pencocokan dan prediksi volatilitas dari model GARCH. Variabel eksogen yang sering digunakan adalah ukuran Realized Volatility (RV). Untuk mereduksi kemencengan dari RV sehingga mampu memperbaiki pencocokan model, studi ini mengaplikasikan transformasi eksponensial pada variabel eksogen dalam model GARCH-X(1,1). Tujuan tersebut dicapai melalui studi empiris berdasarkan pada data returns dan RV 10 menit (sebagai variabel eksogen) dari indeks harga saham FTSE100 dan SP500 periode harian dari Januari 2000 sampai Desember 2021 yang diambil dari Oxford-man Institute’s “Realized Library”. Analisis didasarkan pada hasil estimasi model dengan error dari returns berdistribusi Normal dan Student-t menggunakan Metode Adaptive Random Walk Metropolis diimplementasikan dalam algoritma Markov Chain Monte Carlo. Interval High Posterior Density pada tingkat kepercayaan 99% mengindikasikan signifikansi dari transformasi eksponensial untuk variabel eksogen pada kedua kasus asumsi distribusi untuk error dari returns. Terlebih lagi, nilai Akaike Information Criterion (AIC) mengindikasikan bahwa model yang diusulkan menggungguli model dasar GARCH-X(1,1), dimana model pencocokan terbaik diberikan oleh model berdistribusi Student-t.
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