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标题: 13: Time-Series Analysis-LOS l习题精选 [打印本页]

作者: 土豆妮 时间: 2010-4-8 16:14 标题: [2010]Session 3:-Reading 13: Time-Series Analysis-LOS l习题精选

Session 3: Quantitative Methods: Quantitative
Methods for Valuation
Reading 13: Time-Series Analysis

LOS l: Explain autoregressive conditional heteroskedasticity (ARCH) and discuss how ARCH models can be applied to predict the variance of a time series.

Which of the following is least likely a consequence of a model containing ARCH(1) errors? The:

A)
variance of the errors can be predicted.

B)
model's specification can be corrected by adding an additional lag variable.

C)
regression parameters will be incorrect.

作者: 土豆妮 时间: 2010-4-8 16:14

Which of the following is least likely a consequence of a model containing ARCH(1) errors? The:

A)
variance of the errors can be predicted.

B)
model's specification can be corrected by adding an additional lag variable.

C)
regression parameters will be incorrect.

The presence of autoregressive conditional heteroskedasticity (ARCH) indicates that the variance of the error terms is not constant. This is a violation of the regression assumptions upon which time series models are based. The addition of another lag variable to a model is not a means for correcting for ARCH (1) errors.

作者: 土豆妮 时间: 2010-4-8 16:14

Suppose you estimate the following model of residuals from an autoregressive model:

ε_t² = 0.25 + 0.6ε²_t-1 + μ_t, where ε = ε^{^}

If the residual at time t is 0.9, the forecasted variance for time t+1 is:

A)

0.790.

B)

0.736.

C)

0.850.

作者: 土豆妮 时间: 2010-4-8 16:14

Suppose you estimate the following model of residuals from an autoregressive model:

ε_t² = 0.25 + 0.6ε²_t-1 + μ_t, where ε = ε^{^}

If the residual at time t is 0.9, the forecasted variance for time t+1 is:

A)

0.790.

B)

0.736.

C)

0.850.

The variance at t=t+1 is 0.25 + [0.60 (0.81)] = 0.25 + 0.486 = 0.736.

作者: 土豆妮 时间: 2010-4-8 16:15

Suppose you estimate the following model of residuals from an autoregressive model:

ε_t² = 0.4 + 0.80ε_t-1² + μ_t, where ε = ε^{^}

If the residual at time t is 2.0, the forecasted variance for time t+1 is:

A)

3.6.

B)

3.2.

C)

2.0.

作者: 土豆妮 时间: 2010-4-8 16:15

Suppose you estimate the following model of residuals from an autoregressive model:

ε_t² = 0.4 + 0.80ε_t-1² + μ_t, where ε = ε^{^}

If the residual at time t is 2.0, the forecasted variance for time t+1 is:

A)

3.6.

B)

3.2.

C)

2.0.

The variance at t=t+1 is 0.4 + [0.80 (4.0)] = 0.4 + 3.2. = 3.6.

作者: luqian55 时间: 2010-5-30 17:44

thanks

作者: annyyu 时间: 2010-11-30 05:48

re

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