New Frontiers in Banking Services Emerging Needs and Tailored Products for Untapped Markets 2 ppt
New Frontiers in Banking Services Emerging Needs and Tailored Products for Untapped Markets_2 ppt
... parameters in
state 1, and α
2
is the parameter vector in state 2. The transition function Ψ,
44 2. What Are Neural Networks?
x1
x2
x3
x4
Inputs
x2
x4
x1
x3
Inputs
c11
c 22
c21
c 12
H-Units
FIGURE 2. 12. ... p
2
, p
3
for each observation i:
P
1,i
=
1
1+e
−[x
i
β
1
+β
10
]
(2. 86)
P
2, i
=
1
1+e
−[x
i
β
2
+β
20
]
(2. 87)
P
3,i
=
1
1+e
−[x
i
β
3
+β
30
]
(2. 88)
2. 6 N...
New Frontiers in Banking Services Emerging Needs and Tailored Products for Untapped Markets_3 doc
... bootstrapping the initial training set. As we discuss in Section 4 .2. 8,
bootstrapping involves resampling the original training set with replace-
ment, and then taking repeated forecasts. Bagging is ... annealing solution, and the local quasi-
Newton based on the BFGS algorithm appear, is given by the following
sets of commands:
% Define simple function
z = inline(’.5 * x(1) 2 + .5 *...
New Frontiers in Banking Services Emerging Needs and Tailored Products for Untapped Markets_4 pptx
... estimation an expanding window. The sample size, of
course, becomes larger as we move forward in time.
An alternative to the expanding window is the moving window. In this
case, for the first forecast ... Estimation
4 .2. 5 Out-of-Sample Comparison with Nested Models
Clark and McCracken (20 01), Corradi and Swanson (20 02) , and Clark
and West (20 04) have proposed tests for co...
New Frontiers in Banking Services Emerging Needs and Tailored Products for Untapped Markets_5 doc
... are forecast
onevar scmodel new1 .m (for the stochastic
1 32 5. Estimating and Forecasting with Artificial Data
0 50 100 150 20 0 25 0 300 350 400 450 500
−6
−4
2
0
2
4
6
8
0 50 100 150 20 0 25 0 300 ... .27
DM-1
∗
— .25
DM -2
∗
— .15
DM-3
∗
— .22
DM-4
∗
— .24
SR .77 . 72
B-Ratio — .9 82
∗
marginal significance levels
net is slightly higher and the Hannan-Quinn information...
New Frontiers in Banking Services Emerging Needs and Tailored Products for Untapped Markets_7 doc
... model, outlined in
McKibbin and Wilcoxen (1998).
1 92 7. In ation and Deflation: Hong Kong and Japan
1988 1990 19 92 1994 1996 1998 20 00 20 02
−0.04
−0.03
−0. 02
−0.01
0
0.01
0. 02
0.03
0.04
0.05
Linear
NNRS ... forthcoming. Chen, Racine,
and Swanson (20 01) have used a ridgelet neural network for forecasting
in ation in the United States. McNelis and McAdam (20 04) u...
New Frontiers in Banking Services Emerging Needs and Tailored Products for Untapped Markets_8 pptx
... Hong Kong 21 5
20 02. 2 20 02. 4 20 02. 6 20 02. 8 20 03 20 03 .2 2003.4 20 03.6 20 03.8
20 02. 2 20 02. 4 20 02. 6 20 02. 8 20 03 20 03 .2 2003.4 20 03.6 20 03.8
−15
−10
−5
0
5
10
15
−10
−5
0
5
10
15
Linear Principal Component
Nonlinear ... 1997–
July 20 03
Statistic Maturity in Years
23 45710
Mean 28 .581 26 .1 92 24 .28 6 22 .951 21 .29 5 19.936
Median 27 .500...
Emerging Needs and Tailored Products for Untapped Markets by Luisa Anderloni, Maria Debora Braga and Emanuele Maria Carluccio_1 docx
... form:
L
t
=
T
t=1
1
2 σ
2
t
exp
−
(y
t
− y
t
)
2
2σ
2
t
(2. 10)
y
t
= α +
βσ
t
(2. 11)
t
= y
t
− y
t
(2. 12)
σ
2
t
=
δ
0
+
δ
1
σ
2
t−1
+
δ
2
2
t−1
(2. 13)
where the symbols ... or forecast well sharp turning points in data. For
this reason, we turn to nonlinear forecasting techniques.
2. 2 GARCH Nonlinear Models
Obviously, there are many...
Emerging Needs and Tailored Products for Untapped Markets by Luisa Anderloni, Maria Debora Braga and Emanuele Maria Carluccio_3 pot
... bootstrapping the initial training set. As we discuss in Section 4 .2. 8,
bootstrapping involves resampling the original training set with replace-
ment, and then taking repeated forecasts. Bagging is ... faith in particular point forecasts. Granger and
Jeon (20 02) have suggested “thick modeling” as a strategy for neural net-
works, particularly for forecasting. The idea is simpl...