Pemodelan dan Peramalan Volatilitas Memori Panjang pada Return Saham ANTM Studi Komparatif Model GARCH dan FIGARCH
DOI:
https://doi.org/10.30983/lattice.v5i1.9525Keywords:
Pemodelan volatilitas , Return saham, GARCH, FIGARCH, Long memoryAbstract
This study aims to model and forecast the volatility of ANTM stock returns using FIGARCH and GARCH models to capture both short- and long-memory dynamics. Daily return data spanning from January 1, 2014, to December 31, 2024, were analyzed after stationarity confirmation via ADF test. A mean model was estimated using MA (4), followed by conditional variance modeling with GARCH (1,1) and FIGARCH (1, d,1). Diagnostic tests confirmed the presence of heteroskedasticity and long memory, justifying FIGARCH usage. The FIGARCH (1, d,1) model indicated significant long-memory effects (d = 0.461007), while GARCH (1,1) effectively captured short-term volatility clustering. Forecast performance comparison showed that although both models yielded equal RMSE (0.029000), GARCH (1,1) performed better in terms of MAE (0.019531 vs. 0.019529) and MAPE (192.0809 vs. 192.3617). However, FIGARCH demonstrated superior ability in modeling persistent volatility patterns with smoother conditional variance distribution and better long-term uncertainty estimation. These findings suggest that while GARCH is preferable for short-term predictive accuracy, FIGARCH offers more robust insights into long-term volatility persistence, making it suitable for strategic financial risk management.
Penelitian ini bertujuan untuk memodelkan dan meramalkan volatilitas return saham ANTM menggunakan model GARCH dan FIGARCH guna menangkap dinamika volatilitas jangka pendek dan panjang. Data return harian dari 1 Januari 2014 hingga 31 Desember 2024 dianalisis setelah melalui uji stasioneritas ADF. Model rata-rata ditentukan menggunakan MA (4), dilanjutkan dengan pemodelan varian bersyarat menggunakan GARCH (1,1) dan FIGARCH (1, d,1). Uji diagnostik menunjukkan adanya heteroskedastisitas dan efek memori panjang, mendukung penggunaan model FIGARCH. Hasil estimasi menunjukkan bahwa model FIGARCH (1, d,1) memiliki nilai d = 0,461007, mengindikasikan adanya efek long memory yang signifikan, sedangkan GARCH (1,1) efektif dalam menangkap klaster volatilitas jangka pendek. Evaluasi kinerja peramalan menunjukkan kedua model memiliki nilai RMSE yang sama (0,029000), namun GARCH (1,1) lebih unggul dalam MAE (0,019531 vs. 0,019529) dan MAPE (192,0809 vs. 192,3617). Meskipun demikian, FIGARCH menunjukkan keunggulan dalam menangkap pola volatilitas jangka panjang yang stabil. Dengan demikian, GARCH cocok untuk akurasi prediksi jangka pendek, sementara FIGARCH lebih direkomendasikan untuk estimasi risiko jangka panjang dalam pengelolaan keuangan strategis.
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