Ivy_Honeywell_Data Analytics_5.1

Which of the following is a typical characteristic of financial asset return time series?

• Their distributions are thin-tailed.

• They are not weakly stationary.

• They are highly auto-correlated.

• They have no trend.

Which of the following is a disadvantage of using pure- time series models?

• They are not theoretically motivated.

• They cannot produce forecasts easily.

• They cannot be used for very high frequency data

• It is difficult to determine the appropriate explanatory variables for use in pure time series models.

Which of the following conditions are necessary for a series to be classifiable as a weakly stationary process?

(i) It must have a constant mean

(ii) It must have a constant variance

(iii) It must have constant autocovariances for given lags

(iv) It must have a constant probability distribution.

• (ii) and (iv) only

• (i) and (iii) only

• (i) ,(ii) and (iii) only

• (i), (ii), (iii) and (iv)

A white noise process will have:

(i) A zero mean

(ii) A constant variance

(iii) Autocovariances that are constant

(iv) Autocovariances that are zero except at lag zero

• (ii) and (iv) only

• (i) and (iii) only

• (i), (ii) and (iii) only

• (i), (ii), (iii) and (iv)

Consider a series that follows an MA (1) with zero mean and a moving average coefficient of 0.4 .What is the value of autocovariance at lag 1?

• 0.4

• 1

• 0.34

• It is not possible to determine the value of the autocovariances without knowing the disturbance variance

For an autoregressive process to be considered stationary:

• The roots of the characteristic equation must all lie inside the unit circle

• The roots of the characteristic equation must all lie on the unit circle.

• The roots of the characteristic equation must all lie outside the unit circle.

• The roots of the characteristic equation must all be less than one in absolute value

Which of the following statements are true concerning the autocorrelation function (acf) and partial autocorrelation function (pacf)?

(i) The acf and pacf will always be identical at lag one whatever the model

(ii) The pacf for an MA(q) model will in general be non-zero beyond lag q

(iii) The pacf for an AR(p) model will be zero beyond lag q

(iv) The acf and pacf will be the same at lag two for an MA (1) model.

• (ii) and (iv) only

• (i) and (iii) only

• (i) ,(ii) and (iii) only

• (i), (ii), (iii) and (iv)

An ARMA (p, q) (p, q are integers bigger than zero) model will have

• An acf and pacf that both decline geometrically.

• An acf that declines geometrically and a pacf that is zero after p lags

• An acf that declines geometrically and a pacf that is zero after q lags

• An acf that is zero after p lags and a pacf that is zero after q lags

The pacf is necessary for distinguishing between

• An AR and MA model

• An AR and ARMA model

• An MA and an ARMA model

• Different models from within the ARMA family

Which of the following statements are true concerning information criteria?

(i) Adjusted R squared is an information criterion

(ii) If the residual sum of squares falls when an additional term is added, the value of the information criterion will fall.

(iii) Akaike’s information criterion always leads to model orders that are at least as large as those of Schwarz’s information criterion.

(iv) Akaike’s information criterion is consistent.

• (ii) and (iv) only

• (i) and (iii) only

• (i) , (ii) and (iii) only

• (i), (ii), (iii) and (iv)