# Tag Info

## Hot answers tagged garch

15 votes
Accepted

### Difference between GARCH and Heston Volatility model

Heston gives an expression for the characteristic function, from which option prices can be computed. Therefore it can be calibrated (statically) on a set of vanilla option prices with different ...
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15 votes
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### Correctly applying GARCH in Python

It doesn't matter if you use *100 or just pct_change, as long as you are consistent. However, in practice, due to underlying floating point numerical instabilities in the underlying optimization ...
13 votes

### How to calculate the conditional variance of a time series?

Let’s take a simple example to answer a broad but interesting question: Imagine that we have a daily return serie denoted $r_{t}$ ( which is assumed to be stationary) and let's take a little time to ...
• 2,534
13 votes

### Why is GARCH(1,1) so popular, especially in academia?

Let me start with a disclaimer that I have no interest in promoting GARCH models. However, I am aware of their history, their capabilities and some practical aspects of using them. That helps me come ...
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10 votes
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### Kurtosis in GARCH

You've found parameterizations where fantastically long samples are required for sample 4th moments to converge on population 4th moments. Quick evidence of imprecise estimation Let $k_i$ denote ...
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9 votes

### What are the significant implications of the long-run average variance rate and why Engle won the Nobel Prize for ARCH model development?

The best answer to your question is probably given by the Nobel prize committee itself in "The Prize in Economic Sciences 2003 - Advanced Information" document. You should read it in full. Below is an ...
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9 votes
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### Is there a HAR that deals with the leverage effect?

There exists a modification of the HAR model that accounts for leverage effect (á la GJR-GARCH) in a high-frequency setting. The semi-variance HAR model, termed the SHAR model of Patton and Sheppard (...
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9 votes
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### Realized Variance (realized volatility)

The TLDR; to your question: How can one use realized volatility as a volatility model to do out-of-sample prediction? You extend known models to incorporate additional information procured from high-...
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7 votes

### What is the preferred GARCH method in practice?

I personally use the simple Garch(1,1) for volatility filtering in the risk management area. In fact in most cases I don't even estimate the parameters, I stick 0.94 for mean reversion, 0.04 for the ...
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7 votes
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### Does the unconditional variance implied by a GARCH equal the sample variance?

In this context, unconditional variance refers to the stationary variance level predicted by your GARCH model. This quantity need not coincide with the sample variance of the data on which the latter ...
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7 votes
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4 votes
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### Is there any way to easily estimate and forecast seasonal ARIMA-GARCH model in any software?

You can use Matlab too, that, in my humble opinion, is simpler than R from a syntax point of view. The model you need for is run by the Matlab function arima that ...
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4 votes

### Filtering out AR(1) effects before using stochastic volatility model

Even though it's a straightforward extension, it took me a while (a year? yikes!); but now you can easily incorporate Bayesian ar(1) (or more generally, Bayesian regression) in joint estimation by ...
4 votes
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### R ARMA-GARCH rugarch package doesn't always converge

There is no guarantee that the optimization method always converges! In an introduction the author of the package recommends using the "hybrid" solver, which starts out with the "solnp" and goes ...
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### 2-step estimation of DCC GARCH model in Python

If $\log{(|R_t|)}$ is your first term, I'm not sure why this is a matrix. Modulus (determinant herein) applied to a matrix $R_t$ gives a scalar. If your implementation in python produces a matrix, ...

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