Autoregressive Conditional Heteroskedasticity Flashcards

1
Q

What is the ARCH process?

A

The Autoregressive Conditional Heteroskedastic process that allows the conditional variance to change over time as a function of past errors.

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2
Q

Who introduced the ARCH process?

A

Engle in 1982.

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3
Q

What does GARCH stand for?

A

Generalized Autoregressive Conditional Heteroskedasticity.

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4
Q

What is the main advantage of the GARCH process over the ARCH process?

A

It allows for a more flexible lag structure in the conditional variance equation.

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5
Q

What is the GARCH(1, 1) process?

A

A specific case of the GARCH process that is often used for modeling.

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6
Q

What is the condition for wide-sense stationarity in the GARCH(p, q) process?

A

A(1) + B(1) < 1.

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7
Q

What does the GARCH(p, q) process allow that the ARCH(q) process does not?

A

It allows lagged conditional variances to enter the conditional variance equation.

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8
Q

What is a necessary condition for the existence of the 2mth moment in the GARCH(1, 1) process?

A

a_1 + b_1 < 1.

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9
Q

Fill in the blank: The GARCH(p, q) process can be interpreted as an ______ process.

A

autoregressive moving average.

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10
Q

True or False: The GARCH process can be justified through Wald’s decomposition.

A

True.

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11
Q

What is the role of autocorrelation and partial autocorrelation functions in GARCH models?

A

They are useful for identifying and checking time series behavior in the conditional variance equation.

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12
Q

What is the relationship between the GARCH process and the ARMA process?

A

The extension of the ARCH process to the GARCH process resembles the extension of the AR process to the ARMA process.

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13
Q

What type of economic phenomena has the ARCH process proven useful in modeling?

A

Uncertainty of inflation, among others.

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14
Q

What does the term ‘leptokurtic’ refer to in the context of GARCH(1, 1)?

A

It indicates that the distribution has heavy tails.

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15
Q

What is the significance of the parameterization in the GARCH process?

A

It facilitates easier practical implementation compared to other representations.

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16
Q

What empirical example is discussed in relation to GARCH models?

A

The uncertainty of the inflation rate.

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17
Q

What happens when the roots of 1 - B(z) = 0 lie outside the unit circle?

A

The GARCH process can be rewritten as a distributed lag of past innovations.

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18
Q

What is the impact of a long memory in the GARCH process?

A

It allows for more accurate modeling of volatility over time.

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19
Q

What is often a problem with negative variance parameter estimates in ARCH models?

A

It leads to the imposition of a fixed lag structure.

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20
Q

What does the notation E(e_t) = 0 imply in the context of GARCH?

A

It indicates that the expected value of the innovations is zero.

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21
Q

What is the conditional variance in the GARCH process dependent on?

A

It is a function of past errors and past conditional variances.

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22
Q

What is the implication of a non-negativity constraint in GARCH models?

A

It ensures that variance estimates remain valid.

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23
Q

How does the GARCH process relate to empirical work in economics?

A

It captures the time-varying nature of volatility in economic time series.

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24
Q

What is the significance of the appendix in the paper?

A

It contains proofs for the theorems presented in the main text.

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25
Q

What is the condition for the inverse of a qXq matrix in the context of GARCH models?

A

3/(I - ¢) < 1

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26
Q

What does the notation q’ represent in GARCH models?

A

q’ = (a, …., ,), where i = 1, …, q

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27
Q

In the context of GARCH processes, what is the maximum value of m?

A

m = max{p, q}

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28
Q

What does the notation a_i = 0 signify for i > q in GARCH models?

A

It indicates that the parameters a are zero for indices greater than q.

29
Q

What do the Yule-Walker equations relate to in GARCH processes?

A

They relate the first p autocorrelations for e to the parameters a and B.

30
Q

True or False: The autocorrelations for an ARCH(q) process depend only on the parameters a and B.

31
Q

What is the behavior of the partial autocorrelation function for a GARCH(p,q) process?

A

It is generally non-zero but dies out.

32
Q

What does the notation s_k represent in the context of partial autocorrelation?

A

It denotes the kth partial autocorrelation.

33
Q

Fill in the blank: The maximum likelihood estimate for the parameters in a GARCH model is denoted as _____.

34
Q

What is the purpose of the Lagrange multiplier test in GARCH models?

A

To test for the presence of GARCH by evaluating the null hypothesis a_i = 0.

35
Q

What is the asymptotic distribution of the Lagrange multiplier test statistic under the null hypothesis?

A

Chi-square with degrees of freedom equal to the number of elements in a.

36
Q

What does the notation R^2 represent in the context of the GARCH model?

A

The squared multiple correlation coefficient between the residuals.

37
Q

What is the significance of the block diagonality in the information matrix for GARCH models?

A

It allows for separate estimation of parameters without loss of asymptotic efficiency.

38
Q

What is the expected behavior of the LM test statistic for GARCH alternatives when the null is an ARCH process?

A

It is singular if both p > 0 and q > 0.

39
Q

What does the notation h_t signify in GARCH models?

A

It represents the conditional variance.

40
Q

True or False: The maximum likelihood estimation for GARCH is independent of the recursive terms in the model.

41
Q

What algorithm is mentioned as convenient for maximum likelihood estimation in GARCH models?

A

Berndt, Hall, Hall and Hausman (BHHH) algorithm.

42
Q

What is the significance of the fourth-order moment of e_t in GARCH models?

A

It is necessary for the existence of certain statistical properties.

43
Q

What is the implication of the LM test statistic for the inclusion of additional parameters in GARCH models?

A

It assesses the significance of those parameters in improving model fit.

44
Q

In the empirical example, what economic variable is examined using the ARCH framework?

A

The rate of growth in the implicit GNP deflator.

45
Q

What does the notation m_t represent in the empirical example?

A

m_t = 100·In(GD_t/GD_{t-1})

46
Q

Fill in the blank: The model estimation is based on ____ observations from 1948.2 to 1983.4.

47
Q

What is indicated by the high significance of the LM test for ARCH models in the empirical example?

A

The presence of autoregressive conditional heteroskedasticity.

48
Q

What does the notation h_t = 0.282 signify in the context of the empirical model?

A

It represents the estimated conditional variance.

49
Q

What is the LM test statistic for the inclusion of the eighth-order linear declining lag structure?

A

2.33

Corresponds to the 0.87 fractile in the distribution.

50
Q

What is the LM test statistic for GARCH(1,2) or locally equivalent GARCH(2,1)?

A

3.80

Not significant at the 5% level.

51
Q

What is the value of the LM test statistic for the inclusion of e; ,s…,e;_,?

A

5.58

Equal to the 0.77 fractile in the distribution.

52
Q

What is the sample coefficient of kurtosis for e,h, / from model (31)?

A

3.81

Differs from the ‘normal’ value of 3.00 by slightly less than two asymptotic standard errors.

53
Q

What are the coefficients of kurtosis for models (29) and (30)?

A
  • Model (29): 6.90
  • Model (30): 4.07
54
Q

What are the sample coefficients of skewness for e,h, / from the three models?

A
  • Model (31): -0.13
  • Model (29): 0.18
  • Model (30): 0.11

All are within one asymptotic standard error.

55
Q

What are the estimated mean and median lag in the conditional variance equation in (31)?

A
  • Mean lag: 5.848
  • Median lag: 3.696
56
Q

What are the forced mean and median lags in model (30)?

A
  • Mean lag: 3
  • Median lag: 2
57
Q

What does the lag structure in the GARCH(1,1) model suggest?

A

It can be rationalized by some sort of adaptive learning mechanism.

58
Q

What do figures 3 and 4 illustrate regarding the actual inflation rate?

A

They graph the actual inflation rate together with 95% asymptotic confidence intervals for the one-step-ahead forecast errors.

59
Q

How was the inflation rate characterized from the late forties until the mid-fifties?

A

Very volatile and hard to predict.

60
Q

What characterized the inflation rate during the sixties and early seventies?

A

Stable and predictable.

61
Q

What happened to the uncertainty of the inflation rate starting with the second oil crisis in 1974?

A

There was a slight increase in uncertainty.

62
Q

What is the basic idea of the proof of Theorem I?

A

It follows that of Theorem 1 in Milhaj (1984).

63
Q

What is the condition for convergence in the context of the proof of Theorem I?

A

e; converges almost surely.

64
Q

What does the binomial formula yield in the proof of Theorem 2?

A

A specific expression involving a, and p.

65
Q

What is the significance of the eigenvalues of matrix C in the proof of Theorem 2?

A

All eigenvalues must lie inside the unit circle for the limit to exist.

66
Q

What does the condition (a,,B,,i) < 1 imply?

A

(a,,B,,i-1) < 1 for a, + < 1.

67
Q

What does the reference by Engle (1982) focus on?

A

Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. inflation.

68
Q

What is the significance of the GARCH model in relation to the ARCH model?

A

It provides a slightly better fit and exhibits a more reasonable lag structure.