Emerging Needs and Tailored Products for Untapped Markets by Luisa Anderloni, Maria Debora Braga and Emanuele Maria Carluccio 9 doc
Emerging Needs and Tailored Products for Untapped Markets by Luisa Anderloni, Maria Debora Braga and Emanuele Maria Carluccio_1 docx
... β>0.
3
GARCH stands for generalized autoregresssive conditional heteroskedasticity, and
was introduced by Bollerslev ( 198 6, 198 7) and Engle ( 198 2). Engle received the Nobel
Prize in 2003 for his work ... parametric and semi-parametric models, and models
that have and do not have closed-form solutions. The typology appears in
Table 2.1.
Both linear and polynomial mode...
Emerging Needs and Tailored Products for Untapped Markets by Luisa Anderloni, Maria Debora Braga and Emanuele Maria Carluccio_3 pot
... given by the probability
pr = .15 + .33/G.
If mutation is to be performed on a vector element, we use the following
nonuniform mutation operation, due to Michalewicz ( 199 6).
Begin by randomly ... particular point forecasts. Granger and
Jeon (2002) have suggested “thick modeling” as a strategy for neural net-
works, particularly for forecasting. The idea is simple and straightfo...
Emerging Needs and Tailored Products for Untapped Markets by Luisa Anderloni, Maria Debora Braga and Emanuele Maria Carluccio_5 pdf
... behave, particularly
in volatile emerging- market economies.
Following Bates ( 199 6) and Craine, Lochester, and Syrtveit ( 199 9), we
present this process in continuous time by the following equations:
dS
S
=(µ ... model is a well-known method
for calculating arbitrage-free prices for options. As Peter Bernstein ( 199 8)
points out, this formula was widely in use by practitioner...
Emerging Needs and Tailored Products for Untapped Markets by Luisa Anderloni, Maria Debora Braga and Emanuele Maria Carluccio_7 pptx
... NNRS model.
182 7. Inflation and Deflation: Hong Kong and Japan
198 6 198 8 199 0 199 2 199 4 199 6 199 8 2000 2002
−0.1
−0.05
0
0.05
0.1
0.15
198 6 198 8 199 0 199 2 199 4 199 6 199 8 2000 2002
0.4
0.45
0.5
0.55
0.6
0.65
Inflation
Transition ... and Hofmann (2003) support the finding of Gerlach and Peng with results
from a wider sample of 12 countries.
180 7. Inflation and Deflation:...
Emerging Needs and Tailored Products for Untapped Markets by Luisa Anderloni, Maria Debora Braga and Emanuele Maria Carluccio_8 ppt
... E. Rossi, and George Tauchen ( 199 2), “Stock
Prices and Volume.” Review of Financial Studies 5: 199 –242.
214 9. Dimensionality Reduction and Implied Volatility Forecasting
199 7 199 8 199 9 2000 2001 ... Fit: Linear
and Nonlinear Components, Multiple Correlation Coefficient
Maturity in Years
2345710
Linear 0 .96 5 0 .98 6 0 .99 0 0 .98 1 0 .92 3 0.751
Nonlinear 0 .98 8 0 .97 8 0 .94...
Emerging Needs and Tailored Products for Untapped Markets by Luisa Anderloni, Maria Debora Braga and Emanuele Maria Carluccio_9 doc
... deflation
model for, 182– 196
data in, 184–1 89
in-sample performance,
1 89 190
interpretation of results,
191 – 196
model specification, 1 89
proposed remedies, 182–184
Jarque-Bera statistic,
89 90 , 94
jump ... information
statistic, 86
Jarque-Bera statistic,
89 90 , 94
Lee-White-Granger test, 32,
90 91 , 94
Ljung-Box statistic, 86–88,
94
MATLAB example of,
93 94
McLeod-Li...
Emerging Needs and Tailored Products for Untapped Markets by Luisa Anderloni, Maria Debora Braga and Emanuele Maria Carluccio_10 pptx
... quarter and summer of 198 5
preceded downward corrections in the stock market.
Figure 4.6 compares the two markets during 198 6 and 198 7. After rising for
almost four years, both markets spent 198 6 ... SEEN
IN
198 0, 198 2, 198 4, 198 6,
AND
198 8. MAJOR TREND SIGNALS
IN THE CRB INDEX SHOULD BE CONFIRMED BY OPPOSITE SIGNALS IN THE BOND MARKET.
(SOURCE: COMMODITY TREND SERVI...
Emerging Needs and Tailored Products for Untapped Markets by Luisa Anderloni, Maria Debora Braga and Emanuele Maria Carluccio_13 doc
... 1),so
P
(
µ − 1 .96 σ ≤ Y ≤ µ + 1 .96 σ
)
= 0 .95 , (6.15)
and hence [µ − 1 .96 σ, µ +1 .96 σ ]isa95% confidence interval. This result is
often expressed along the lines of
for i.i.d. normal samples, 95 times ... numerically. It
is found that for X ∼ N(0, 1),
P
(
|X |≤1 .96
)
= 0 .95 , (6.14)
see Exercise 6.7, so [−1 .96 , 1 .96 ] is a 95 % confidence interval for X. More gen-
erally...
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