WorksheetsGARCH - ECM
Total questions: 15
Worksheet time: 8mins
Which of the following features of financial asset return time-series could be captured using a standard GARCH(1,1) model?
i) Fat tails in the return distribution
ii) Leverage effects
iii) Volatility clustering
iv) Volatility affecting returns
(ii) and (iv) only
(i) and (iii) only
(i), (ii), and (iii) only
(i), (ii), (iii), and (iv)
If there were a leverage effect in practice, what would be the shape of the news impact curve for as model that accounted for that leverage?
It would rise more quickly for negative disturbances than for positive ones of the same magnitude
It would be symmetrical about zero
It would rise less quickly for negative disturbances than for positive ones of the same magnitude
It would be zero for all positive disturbances
A
B
C
AR(1)
MA(1)
ARMA(1,1)
ARMA(0,0)
No restrictions
α ≥ 0 and β ≥ 0
α + β ≥ 0
0 ≤ α < 1 and 0 ≤ β < 1
0 ≤ α + β < 1
GARCH(1,1)
GARCH(2,2)
GARCH(3,3)
GARCH(2,3)
GARCH(3,2)
yt = 0.003 + ut , ut ~ N(0,ht)
ht = 0.01 + 0.1ut-12 + 0.6ht-12
What is the most appropriate value of a forecast (to 2 decimal places) of the conditional variance for time t+1 if the conditional variance and residual at time t are 0.02 and -0.465 respectively and the model is as follows?
0.01
0.04
0.10
0.05
Suppose that you were asked to provide a guess at a 20-step ahead forecast for the model in the previous question. What would the most appropriate guess be (to 2 decimal places)?
0.03
0.01
0.10
0.05
Consider the estimation of a standard GARCH-M model. If the data employed were a time-series of daily corporate bond percentage returns, which of the following would you expect the value of the GARCH-in-mean parameter estimate to be?
Less than -1
Between -1 and 0
Between 0 and 1
Bigger than 1
Which of the following statements are true concerning a comparison between ARCH(q) and GARCH(1,1) models?
i) The ARCH(q) model is likely to be the more parsimonious
ii) The ARCH(q) model is the more likely to violate non-negativity constraints
iii) The ARCH(q) model can allow for an infinite number of previous lags of squared returns to affect the current conditional variance
iv) The GARCH(1,1) model will usually be sufficient to capture all of the dependence in the conditional variance
(ii) and (iv) only
(i) and (iii) only
(i), (ii), and (iii) only
(i), (ii), (iii), and (iv)
Which of the following criticisms of standard ("plain vanilla") GARCH models can be overcome by EGARCH models?
i) Estimated coefficient values from GARCH models may be negative
ii) GARCH models cannot account for leverage effects
iii) The responsiveness of future volatility to positive and negative shocks is symmetric under a GARCH formulation
iv) GARCH models cannot allow for a feedback from the volatility to the returns
(ii) and (iv) only
(i) and (iii) only
(i), (ii), and (iii) only
(i), (ii), (iii), and (iv)
Apakah ada leverage effect?
Ya
Tidak
Tidak bisa dipastikan
Berdasarkan uji stasioner data pada level dan first difference pada gambar, apakah mungkin digunakan model ECM?
Ya
Tidak
Apakah variabel dependen dan variabel independen terkointegrasi?
Ya
Tidak
Berdasarkan persamaan jangka pendek pada gambar, apakah model ECM yang dihasilkan valid?
Ya
Tidak
