# Dcc garch explained

In econometricsthe autoregressive conditional heteroscedasticity ARCH model is a statistical model for time series data that describes the variance of the current error term or innovation as a function of the actual sizes of the previous time periods' error terms; [1] often the variance is related to the squares of the previous innovations. The ARCH model is appropriate when the error variance in a time series follows an autoregressive AR model; if an autoregressive moving average ARMA model is assumed for the error variance, the model is a generalized autoregressive conditional heteroskedasticity GARCH model.

ARCH models are commonly employed in modeling financial time series that exhibit time-varying volatility and volatility clusteringi.

ARCH-type models are sometimes considered to be in the family of stochastic volatility models, although this is strictly incorrect since at time t the volatility is completely pre-determined deterministic given previous values. An ARCH q model can be estimated using ordinary least squares. A methodology to test for the lag length of ARCH errors using the Lagrange multiplier test was proposed by Engle This procedure is as follows:. If an autoregressive moving average model ARMA model is assumed for the error variance, the model is a generalized autoregressive conditional heteroskedasticity GARCH model.

Generally, when testing for heteroskedasticity in econometric models, the best test is the White test. Exponentially weighted moving average EWMA is an alternative model in a separate class of exponential smoothing models. As an alternative to GARCH modelling it has some attractive properties such as a greater weight upon more recent observations, but also drawbacks such as an arbitrary decay factor that introduces subjectivity into the estimation.

The condition for this is. This is particularly useful in an asset pricing context. It has the specification:. The specification is one on conditional standard deviation instead of conditional variance :. The result is the following system of stochastic differential equations :.

In contrast to the temporal ARCH model, in which the distribution is known given the full information set for the prior periods, the distribution is not straightforward in the spatial and spatiotemporal setting due to the interdependence between neighboring spatial locations. The spatial weight matrix defines which locations are considered to be adjacent.

You can help by adding to it. October Journal of Econometrics. Introductory Econometrics for Finance 3rd ed.Volatility clustering — the phenomenon of there being periods of relative calm and periods of high volatility — is a seemingly universal attribute of market data.

There is no universally accepted explanation of it. It does not explain it. Figure 1 is an example of a garch model of volatility. Clearly the volatility moves around through time. Figure 1 is a model of volatility, not the true volatility. But if we had a picture of the true volatility, it would look remarkably like Figure 1.

The natural frequency of data to feed a garch estimator is daily data. You can use weekly or monthly data, but that smooths some of the garch-iness out of the data. You can use garch with intraday data, but this gets complicated. There is seasonality of volatility throughout the day.

The seasonality highly depends on the particular market where the trading happens, and possibly on the specific asset.

One particular example of such messiness looks at intraday Value at Risk. How much daily data should you give garch? Figure 1 does not show true volatility because we never observe volatility. Volatility ever only indirectly exposes itself to us. So we are trying to estimate something that we never see. The garch view is that volatility spikes upwards and then decays away until there is another spike. It is hard to see that behavior in Figure 1 because time is so compressed, it is more visible in Figure 3.

Figure 3: Volatility of MMM as estimated by a garch 1,1 model. Note that volatility from announcements as opposed to shocks goes the other way around — volatility builds up as the announcement time approaches, and then goes away when the results of the announcement are known.

The estimation of a garch model is mostly about estimating how fast the decay is. The decay that it sees is very noisy, so it wants to see a lot of data. Lots of data as in it would like tens of thousands of daily observations.

If you have less than about daily observations, then the estimation is unlikely to give you much real information about the parameters. That is going to be one with about the right persistence see belowwith the alpha1 parameter somewhere between 0 and 0. We are staying with it because it is the most commonly available, the most commonly used, and sometimes good enough. Garch models are almost always estimated via maximum likelihood.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. It only takes a minute to sign up. I have got clarifications about almost all the aspects of interpretation a DCC model from a post from But I have a doubt regarding the interpretation of dcca1 and dccb1. The answer there mentions only about the joint in significance of the model.

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But I have obtained some results where dcca1 is insignificant but dccb1 is highly significant. Does that imply that DCC is inappropriate for my analysis? Failing the first test would imply the model is not appropriate.

Failing the second test would imply the same. Sign up to join this community. The best answers are voted up and rise to the top.

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Active 2 years ago. Viewed 1k times. One of my results is attached below for reference. Series : 2 No. Log-Likelihood : What is the post you are referring to? Could you include a link? Active Oldest Votes. Some other comments: If the conditional correlation were actually constant, you would expect dcca1 to be approximately zero insignificantly different from zero and dccb1 to be approximately 1 insignificantly different from 1, but significantly different from zero.

In your case you have the first but not the second. Hence, the estimates of statistical significance are questionable.

See also Chang et al. Richard Hardy Richard Hardy What a coincidence. I have now included it in the answer.GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.

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Quantitative Finance Stack Exchange is a question and answer site for finance professionals and academics. It only takes a minute to sign up. Could anyone help me understand it? My understanding is that we get the unconditional covariance before based on the data sets.

For example, two data sets, A and B, then the unconditional covariance matrix is built by the variance of A and B respectively and covariance of them, is that true? Sign up to join this community. The best answers are voted up and rise to the top.

Home Questions Tags Users Unanswered. Asked 5 years, 6 months ago. Active 5 years, 5 months ago. Viewed times.

## Autoregressive conditional heteroskedasticity

Words to explain the unconditional covariance is on the page 5 under equation 2. Thanks for you reply. Active Oldest Votes.

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