I have a conceptual question that I haven't managed to grasp yet and is most likely a econometrics 101 question by here it goes:

If we estimate a GARCH model for a time series, how do we then use this in my model for the returns? For example; I have the return data of an index. I know that I have volatility clustering in this data. I find a suitable GARCH model for the volatility (variance). Now, if I model the returns an a suitable model, i.e. a regression model, and look at the coefficients and the p-values that it spits out, these values are still based on the regular OLS assumptions right? How do I make use of the GARCH in this model so that I can get coefficients and p-values that have accounted for the conditional heteroscedastic variances in the time series?

  • $\begingroup$ You can use an ARMA-GARCH model. Matlab has standard garchfit tool for this. When you normalize the residuals with the estimated variances you get standardized residuals for each data point. $\endgroup$ Mar 23 '15 at 17:50
  • $\begingroup$ What if I do not want to add AR and MA lags? I am running a set of dummies on a vector of data and want to include GARCH terms, $\endgroup$ Apr 9 '15 at 8:32
  • $\begingroup$ ARMA terms are used to estimate the mean. GARCH terms are used to estimate the variance. They help each other to estimate better. i.e. if you don't subtract the mean the variance estimate will not be accurate and if you don't use the error variance estimation of mean is not efficient. If you are estimating the mean via some other exogenous variables that should also do fine. $\endgroup$ Apr 9 '15 at 11:36

Suppose the conditional mean of returns is constant. A GARCH model gives you a fitted value of the conditional variance for each data point. These fitted values can be used to weight the data points to construct an efficient estimate of the mean (e.g. using weighted least squares); data points with high fitted conditional variance would be down-weighted relative to data points with low fitted conditional variance.

Now suppose the conditional mean of returns is not constant. Then you would build a model for the conditional mean simultaneously with a GARCH model for the conditional variance. The effect of the GARCH model would again be similar to the case discussed above. The data points with high fitted conditional variance would be down-weighted relative to the points that have low fitted conditional variance when estimating the model for the conditional mean.

One example given by @CadgasOzgenc is an ARMA-GARCH model. A rich choice of specifications of ARIMAX models and different versions of (G)ARCH models can be implemented using "rugarch" package in R (functions ugarchspec, ugarchfit).

Simultaneous estimation is efficient, but two-stage estimation could be done, too, if you can consistently estimate the conditional mean model in presence of conditionally heteroskedastic errors. First you would estimate the conditional mean model ignoring that the errors have a GARCH structure. Second, you would estimate a GARCH model on the residuals from the conditional mean model. Then you would reestimate the conditional mean model using the fitted conditional variances to weight the data points as discussed above. That could be done iteratively until convergence. For example, an AR-GARCH model could be estimated that way as an AR(p) model can be estimated consistently even in presence of GARCH errors. However, estimating an AR-GARCH model in one stage (simultaneously) would be more efficient.

  • $\begingroup$ Hypothetically, in case I have an AR(3) mean model, which can be estimated as simple OLS, but with long memory errors, can I, according to your suggestion, just run the OLS for the mean and then use ARFIMA model for the residuals and use the fitted values as weights for Weighted least squares? $\endgroup$
    – m3div0
    Mar 27 '15 at 11:29
  • $\begingroup$ Could you be more explicit? Do you want to (1) model your dependent variable as AR(3) and then (2) model the residuals as ARFIMA($p,d,q$)? That does not make sense to me. Later, I did not understand what fitted values you would use, and what model you would estimate using WLS. $\endgroup$ Mar 27 '15 at 11:32
  • $\begingroup$ Well my first choice was ARIMA FIGARCH model, but it can't be done in R, therefore I thought I could found the conditional variances(to weight the original data points as you have written) using the ARFIMA on the squared residuals, which as I understand from the literature is as if I run the Figarch and consequently there wouldn't be heteroscedascity in the WLS $\endgroup$
    – m3div0
    Mar 27 '15 at 12:40
  • 1
    $\begingroup$ No, ARFIMA on squared residuals is not FIGARCH -- just as ARIMA on squared residuals is not GARCH. AR(F)IMA model has an error term so that the modeled relationship is approximate. Meanwhile, (F)IGARCH is deterministic in the sense that there is no error in the (F)IGARCH formula. That is, the conditional variance is supposedly perfectly explained in the (F)IGARCH model. AR(F)IMA on squared residuals is more like a stochastic volatility model (note the name stochastic as opposed to deterministic, which is true for (F)IGARCH). $\endgroup$ Mar 27 '15 at 12:52
  • $\begingroup$ All right, I see it now and it brings me back where I started, but thank you for your explanation $\endgroup$
    – m3div0
    Mar 27 '15 at 13:08

Consider the data generating process (DGP):

$y_{t}= x_{t}^{\prime}\beta+\varepsilon_{t}$ (1)

$\varepsilon_{t}= \sigma_{t}z_{t},\quad z_{t}\sim i.i.d.\, N\left(0,\,1\right)$ (2)

$\sigma_{t}^{2}= \sigma^{2}+\alpha\varepsilon_{t-1}^{2}+\beta\sigma_{t-1}^{2}$ (3)

Equation (1) is a model for the conditional mean of the process Eq. (2) and (3) define a model for the conditional variance of the process (in this case the residuals are Gaussian). You could estimate these equations step-by-step using OLS (same as testing for ARCH(k) effect) since OLS is consistent but OLS will be inefficient and there will be non-linear estimators such as the Maximum Likelihood estimator (ML) which will produce a lower variance. The likelihood function for the model above would look like:



You could drop the first term when maximizing since its constant. In practice you would start by estimating Eq. (1) and saving the residuals. If all misspecification tests are okay and if you have do have ARCH effect then you would estimate Eq. (3) (or a similar ARCH/GARCH family model) on your saved residuals. Furthermore you could calculate s.e.'s from the negaative of the expected value of the Hessian matrix although robust s.e.'s are recommended in case your distributional assumption does not hold (QMLE).

To illustrate I have simulated an AR-GARCH process:

$y_{t}=\mu+\theta y_{t-1}+\varepsilon_{t}$ (4)

$\varepsilon_{t}=\sigma_{t}z_{t},\quad z_{t}\sim i.i.d.\, N\left(0,\,1\right)$ (5)

$\sigma_{t}^{2}=\sigma^{2}+\alpha\varepsilon_{t-1}^{2}+\beta\sigma_{t-1}^{2}$ (6)

with parameter values $\mu=0.01 $, $\theta=0.6 $, $\sigma^{2}=0.04 $, $\alpha=0.2 $ and $\beta=0.5 $. First I fit an AR(1) model to the simulated series:

Figure 1

I estimate the model using ML and get estimated values of: $y_{t}=0.0013538+0.59745y_{t-1}+\varepsilon_{t}$

Then I save the residuals an dplot the residuals and squared residuals. We see there are ARCH effects as indicated by Residual2 although I perhaps should have simulated a more persistent series.

Figure 2

Then I estimate a GARCH(1,1) model, Eq. (6) on the series called Residual. This gives us estimated values of: $\sigma_{t}^{2}=0.03720+0.23753\varepsilon_{t-1}^{2}+0.50754\sigma_{t-1}^{2}$

Which we can use to make forecasts of the conditional variance. Note that when dealing with financial series you will often end up with a model for the conditional mean corresponding with: $y_{t}=\mu+\varepsilon_{t} $, i.e. only a constant which correspond with the efficient market hypothesis since if it was possible to forecast the conditional mean of some givens share price it would be easy to make a profit.

Another way to estimate the model is to estimate in one go. Doing that I get a model: $y_{t}=0.003444+0.605931+\varepsilon_{t}$



Which is very similar to the estimated model above which was estimated in two stages. Forecasting 10 periods into the future gives us the figure below.

Figure 3

We see that the first graph shows the simulated series, AR(1), and the fitted model while the second graph depicts the fitted values from the GARCH(1,1) model on the residuals from the conditional mean model.

Usually you would first estimate the mean of the model and then when you would have well specified model for the conditional mean you would proceed to test for ARCH effects. The reason for this is that if your DGP follows an AR(2) process but you estimate an AR(1) model then your residuals will exhibit autocorrelation. If your residuals exhibit autocorrealtion this implies that your squared residuals will exhibit autocorrelation but in general the converse is not true. Therefore the ARCH test will also have power against residual autocorrelation and this is the reason to make sure that your model is well specified before testing for ARCH effects.

If this did not answer your question then let me know and I will amend my answer.

  • $\begingroup$ When you estimated equation 4 with ML, I suppose you mean you assumed constant variance for the innovation. Yes? $\endgroup$ Mar 25 '15 at 11:28
  • $\begingroup$ Yes since I have no knowledge (or so I assume) whether or not there are any ARCH effect in the residuals when estimating (4). I maximize the following likelihood function: $l_{T}\left(\theta\right)=-\frac{T}{2}\log\left(2\pi\right)-\frac{1}{2}\sum_{t=1}^{T}\left(\log\left(\sigma^{2}\right)+\frac{\varepsilon_{t}^{2}}{\sigma^{2}}\right)$ $\endgroup$
    – Plissken
    Mar 25 '15 at 12:09
  • $\begingroup$ On another note. How do I get LaTex symbols in the comment box? $\endgroup$
    – Plissken
    Mar 25 '15 at 12:17
  • $\begingroup$ The likelihood function from the previous comment should read: $l_{T}\left(\theta\right)=-\frac{T}{2}\log\left(2\pi\right)-\frac{1}{2}\sum_{t=1}^{T}\left(\log\left(\sigma^{2}\right)+\frac{\varepsilon_{t}^{2}}{\sigma^{2}}\right)$ A bit further down I have written: "Another way to estimate the model is to estimate in one go. Doing that I get a model: ... ". There I estimate the full model in one go, i.e. Eq. (4) and (6) at the same time. As you can see the difference between estimates is not that big (I do have 2000 obs.!). I wanted to show that there are different ways to estimate the model. $\endgroup$
    – Plissken
    Mar 25 '15 at 16:00
  • $\begingroup$ ctd. and furthermore it is quite important to have a well specified model before testing for ARCH as explained in my answer. $\endgroup$
    – Plissken
    Mar 25 '15 at 16:01

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