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Summary Momentum Profit

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Submitted By hadian
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Tabel 3 melaporkan estimasi pasar dan model Fama and Franch yang mencakup GJR-GARCH(1,1)-M seperti persamaan di bawah ini

[pic]

1. Menggunakan teknik estimasi maximum likelihood dan standar error Bollerslave-Wooldridge robust

2. Menganalisis bagaimanakah berita memberikan pengaruh terhadap volatilitas dari winner dan loser.

3. Nilai rata-rata dari γ+ɳ/2 dari conditional market model winner adalah 0.2254

Nilai rata-rata dari γ+ɳ/2 dari conditional market model loser adalah 0.1262

4. Nilai rata-rata dari γ+ɳ/2 dari Fama n French model winner adalah 0.2867

Nilai rata-rata dari γ+ɳ/2 dari Fama n French model loser adalah 0.1551

5. Koefisien θ pada persamaan di atas menggambarkan efek dari lagged conditional variance, serta gambaran dari efek “old news” pada volatilitas.

6. Koefisien asimetri (ɳ) mengunakan uji signifikansi pada level 5%

7. Loser memiliki ɳ dan θ yang lebih besar, serta γ yang lebih rendah dari pada winner

8. Persistance volatility dari winner dan loser diukur dengan menggunakan γ+ɳ/2+θ.

9. Volatility loser lebih persisten dibandingkan dengan winner , hal tersebut di tunjukkan dengan loser(winner), MM 0.9172(0.8339), FF 0.9623(0.7885).

10. Peningkatan pada conditional volatility akan menyebakan penurunan retur winner dan loser, tapi meningkatkan return momentum.

11. Penambahan stuktur GJR-GARCH(1,1)-M ke dalam model tradisional untuk mengukur kinerja, sangat krusial untuk menjelaskan abnormal return dari strategi momentum.

Tabel 4

1. Nilai rata-rata koefisien θ pada market model, untuk winner (0.5785) lebih rendah dibandingkan dengan loser (0.7911).

2. Nilai rata-rata koefisien θ pada Fama n French model, untuk winner (0.5017) lebih rendah dibandingkan dengan loser (0.8072).

3. Hal tersebut menunjukkan bahwa “old news” lebih memberikan efek terhadap volatilitas lebih besar pada FF model dibandingkan dengan Market model.

4. [pic]

5. Return dan conditional volatility dari momentum portfolio lebih baik dijelaskan dengan model GARCH(1,1)-M model.

6. Volatility dari winner lebih sensitive terhadap “old news” dari pada loser, γw>γL.

7. Akibat yang ditimbulkan terhadap “old news” dari loser lebih kuat dibandingkan dengan winner, θL>θW

8. Tabel 3 dan 4 menunjukkan bahwa loser lebih persisten dibandingkan winner.

9. Rata-rata abnormal return dari momentum strategi 0.0125 (MM), dan 0.0079 (FF), GARCH(1,1)-M.

10. Rata-rata koefisien α melebihi dari -0.0016 dan 0.0004 dari rata-rata abnormal return untuk MM dan FF GJR-GARCH(1,1)-M secara berurutan.

11. Adanya asimetrik respon dari loser terhadap good and bad news, serta terdapat conditional risk premium yang menjelaskan keuntungan dari momentum strategis.

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