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发表于 2010-4-8 16:02 | 只看该作者

13: Time-Series Analysis-LOS h习题精选

contrast, center, process, 2010

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

LOS h: Describe the characteristics of random walk processes and contrast them to covariance stationary processes.

Given an AR(1) process represented by x_t+1 = b₀ + b₁×x_t + e_t, the process would not be a random walk if:

b₁ = 1.

E(e_t)=0.

C)	the long run mean is b₀ + b₁.

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发表于 2010-4-8 16:03 | 只看该作者

Given an AR(1) process represented by x_t+1 = b₀ + b₁×x_t + e_t, the process would not be a random walk if:

b₁ = 1.

E(e_t)=0.

C)	the long run mean is b₀ + b₁.

For a random walk, the long-run mean is undefined. The slope coefficient is one, b₁=1, and that is what makes the long-run mean undefined: mean = b₀/(1-b₁).

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发表于 2010-4-8 16:03 | 只看该作者

David Brice, CFA, has tried to use an AR(1) model to predict a given exchange rate. Brice has concluded the exchange rate follows a random walk without a drift. The current value of the exchange rate is 2.2. Under these conditions, which of the following would be least likely?

A)	The forecast for next period is 2.2.

B)	The residuals of the forecasting model are autocorrelated.

C)	The process is not covariance stationary.

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发表于 2010-4-8 16:03 | 只看该作者

A)	The forecast for next period is 2.2.

B)	The residuals of the forecasting model are autocorrelated.

C)	The process is not covariance stationary.

The one-period forecast of a random walk model without drift is E(x_t+1) = E(x_{t + et} ) = x_t + 0, so the forecast is simply x_t = 2.2. For a random walk process, the variance changes with the value of the observation. However, the error term e_t = x_t - x_t-1 is not autocorrelated.

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发表于 2010-4-8 16:03 | 只看该作者

Which of the following statements regarding time series analysis is FALSE?

A)	We cannot use an AR(1) model on a time series that consists of a random walk.

B)	An autoregressive model with two lags is equivalent to a moving-average model with two lags.

C)	If a time series is a random walk, first differencing will result in covariance stationarity.

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发表于 2010-4-8 16:06 | 只看该作者

Which of the following statements regarding time series analysis is FALSE?

A)	We cannot use an AR(1) model on a time series that consists of a random walk.

B)	An autoregressive model with two lags is equivalent to a moving-average model with two lags.

C)	If a time series is a random walk, first differencing will result in covariance stationarity.

An autoregression model regresses a dependent variable against one or more lagged values of itself whereas a moving average is an average of successive observations in a time series. A moving average model can have lagged terms but these are lagged values of the residual.

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发表于 2010-4-8 16:06 | 只看该作者

A time series x that is a random walk with a drift is best described as:

A)	x_t = b₀ + b₁ x_{t ? 1}.

B)	x_t = b₀ + b₁x_{t ? 1} + ε_t.

C)	x_t = x_{t ? 1} + ε_t.

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发表于 2010-4-8 16:07 | 只看该作者

A time series x that is a random walk with a drift is best described as:

A)	x_t = b₀ + b₁ x_{t ? 1}.

B)	x_t = b₀ + b₁x_{t ? 1} + ε_t.

C)	x_t = x_{t ? 1} + ε_t.

The best estimate of random walk for period t is the value of the series at (t ? 1). If the random walk has a drift component, this drift is added to the previous period’s value of the time series to produce the forecast.

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