binh, p.t. 2013. lecture 6. arch models
DESCRIPTION
ARCHTRANSCRIPT
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AutoRegressive Conditional
Heteroscedasticity (ARCH)
Dự báo và Phân tích dữ liệu
(14/11/2013)
Phùng Thanh Bình
ptbinh[a-còng]ueh.edu.vn
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NỘI DUNG
Giới thiệu
Mô hình ARCH
Kiểm định ảnh hưởng ARCH
Mô hình GARCH
Mô hình GARCH-M
Mô hình TGARCH
Mô hình hóa các nhân tố ảnh
hưởng phương trình phương sai
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8.0
8.1
8.2
8.3
8.4
8.5
8.6
8.7
8.8
8.9
03 04 05 06 07 08 09 10 11 12 13
LOG(FTSE)
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-.100
-.075
-.050
-.025
.000
.025
.050
.075
.100
03 04 05 06 07 08 09 10 11 12 13
LOG(FTSE/FTSE(-1))
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1
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01 02 03 04 05 06 07 08 09 10 11 12 13
LOG(SAM)
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-.7
-.6
-.5
-.4
-.3
-.2
-.1
.0
.1
01 02 03 04 05 06 07 08 09 10 11 12 13
LOG(SAM/SAM(-1))
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2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
2007 2008 2009 2010 2011 2012 2013
LOG(CLOSE_ACB)
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-.5
-.4
-.3
-.2
-.1
.0
.1
.2
2007 2008 2009 2010 2011 2012 2013
LOG(CLOSE_ACB/CLOSE_ACB(-1))
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MÔ HÌNH ARCH
Mô hình ARCH(1)
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MÔ HÌNH ARCH
Mô hình ARCH(2)
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MÔ HÌNH ARCH
Mô hình ARCH(3)
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MÔ HÌNH ARCH
Mô hình ARCH(q)
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ẢNH HƯỞNG ARCH
ARCH effect là gì?
Các bước kiểm định:
• Ước lượng phương trình
Yt = b1 + b2Xt + et
• Ước lượng PT hồi quy phụ
• Kiểm định giả thiết
2
ptp
2
2t2
2
1t10
2
t eˆ...eˆeˆˆe
0...:H p210
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VÍ DỤ (ARCH.wk1):
ƯỚC LƯỢNG MÔ HÌNH ARCH
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.0000
.0001
.0002
.0003
.0004
.0005
.0006
.0007
90 91 92 93 94 95 96 97 98 99
ARCH1 ARCH8
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Mô hình GARCH(p,q) có dạng:
MÔ HÌNH GARCH
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Mô hình GARCH(1,1) có dạng:
(7)
Lưu ý: Mô hình GARCH(1,1) ~
ARCH( )!!!
MÔ HÌNH GARCH
21t11t10t uh h
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GARCH(1,1)
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GARCH(2,1)
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GARCH(1,2)
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GARCH(2,2)
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GARCH(3,2)
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GARCH(4,2)
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GARCH(4,1)
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GARCH(5,1)
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.0000
.0001
.0002
.0003
.0004
.0005
.0006
.0007
90 91 92 93 94 95 96 97 98 99
ARCH8 GARCH1_1
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.0000
.0001
.0002
.0003
.0004
.0005
90 91 92 93 94 95 96 97 98 99
GARCH1_1 GARCH4_1
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Mô hình GARCH-M có dạng:
MÔ HÌNH GARCH-M
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Mô hình GARCH-M có dạng:
MÔ HÌNH GARCH-M
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GARCH(1,1)-M
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1t2
1t12
1t11t10t duuh h
Mô hình GARCH-M có dạng:
= 1 if et < 0 (bad news)
= 0 if et > 0 (good news)
MÔ HÌNH TGARCH
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