Finance Chapter 10 Threshold Autoregressive And Markov Switching Models A

subject Type Homework Help
subject Pages 3
subject Words 400
subject Authors Chris Brooks

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Multiple Choice Test Bank Questions No Feedback Chapter 10
Correct answers denoted by an asterisk.
To check for seasonality (day-of-the-week effect) in stock returns of South Korea,
Malaysia, the Philippines, Taiwan and Thailand, Brooks and Persand (2001) regress
daily returns in each of these countries’ stock market on five dummy variables D1 to
D5 representing each day of the week i.e. D1 for Mondays, D2 for Tuesdays, D3 for
Wednesdays, D4 for Thursdays and D5 for Fridays:
1 2 3 4 5
1 2 3 4 5
t t t t t t t
r D D D D D u
   
= + + + + +
Their results were:
2. Which market(s) did not display any evidence of day-of-the-week effect?
3. A Markov process can be written mathematically as:
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4. The unknown parameters of a Markov switching model are usually estimated
using:
5. The key difference between threshold autoregressive and Markov switching models
is that:
6. Which of these equations is a self-exciting threshold autoregressive model?
7. To compare the goodness of fit of Markov switching and threshold autoregressive
models with linear models, one can compare the residual sums of squares of the two
types of models using an F-test. Is the statement true?
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8. Suppose that a researcher wishes to test for calendar (seasonal) effects using a
dummy variables approach. Which of the following regressions could be used to
examine this?
(i) A regression containing intercept dummies
(ii) A regression containing slope dummies
(iii) A regression containing intercept and slope dummies
(iv) A regression containing a dummy variable taking the value 1 for one
observation and zero for all others
9. If a series possesses the “Markov property”, what would this imply?
(i) The series is path-dependent
(ii) All that is required to produce forecasts for the series is the current value of
the series plus a transition probability matrix
(iii) The state-determining variable must be observable
(iv) The series can be classified as to whether it is in one regime or another regime,
but it can only be in one regime at any one time

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