Questions tagged [garch]
Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used for time series in which the conditional variance is time-varying and autocorrelated. The conditional variance is a linear combination of lagged conditional variances and lagged squared errors. The conditional variance equation in GARCH models is deterministic, in contrast to Stochastic Volatility (SV) models.
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Algorithm to fit AR(1)/GARCH(1,1) model of log-returns
I am fitting numerically an AR(1)/GARCH(1,1) process to index and stock log-returns, $r_t=\log(P_t/P_{t-1})$, where $P_t$ is the price at time $t$, and thus far am not clear on where the observed log ...
user6430
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Does the unconditional variance implied by a GARCH equal the sample variance?
In the MATLAB default settings for GARCH estimation they say "presample conditional variance is the sample average of the squared disturbances of the offset-adjusted response data y". Am I right in ...
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Why is the GARCH intercept supposed to be strictly positive?
Maybe it's a simple question but I don't really understand why it is theoretically required. Let's take the standard GARCH(1,1)
$$\sigma^2_{t+1}=\omega+\alpha\epsilon^2_{t}+\beta\sigma^2_{t}$$
In most ...
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Realized Variance (realized volatility)
I'm confused about realized variance. I roughly know the theory around Ito Calculus and quadratic variation and integrated volatility so I understand what realized variance measures (even though as ...
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Is there a HAR that deals with the leverage effect?
The EGARCH is a special GARCH model that treats the leverage effect of the volatility. The HARV does not make a distinction between negative and positive returns. Is there a special HARV that deals ...
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So many volatility models. Any comparisons of them?
Are there any papers that make an explicit contrast/comparison of the following (or other) vol models in terms of the suitability for addressing some empirical problem?
Wavelet multiresolution ...
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Forecasting using rugarch package
I want to do one step ahead in-sample forecasts. My data can be found here. This is just a data frame with the date as the rownames.
I specify my model and do the fit and show the plots with
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How to calculate the conditional variance of a time series?
I am reading a paper where the term conditional variance is mentioned, but I am not really sure what is meant by this and how this can be calculated:
Fig. 2 shows the conditional variances of the ...
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GARCH model and prediction
I have a question about the prediction of volatility and returns of a time series. Basically it is a question about predict in the ...
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Is this the correct way to forecast stock price volatility using GARCH
I am attempting to make a forecast of a stock's volatility some time into the future (say 90 days). It seems that GARCH is a traditionally used model for this.
I have implemented this below using ...
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GARCH model using high frequency price return
I would like to forecast variance at time length $k\delta$ based on a price (return) time series of time step length $\delta$. I will apply a GARCH(1,1) model to subsamples at time intervals length $k\...
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negative gamma value for gjr-garch output
I was wondering if anyone could tell me if my model is completely incorrect as I haven't been able to find anything online for this. I am running a Gjr Garch model to measure volatility in gold ...
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How to obtain one-step ahead forecast in Python based on GARCH?
I am trying to produce one-step ahead forecast using GARCH in Python using a fixed windows method. I ultimately want to put the code below in a for loop, but this code snippet does not perform as I ...
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Correctly applying GARCH in Python
Problem: Correct usage of GARCH(1,1)
Aim of research: Forecasting volatility/variance.
Tools used: Python
Instrument: SPX (specifically adjusted close prices)
Reference material: On Estimation of ...
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Why is GARCH(1,1) so popular, especially in academia?
What makes GARCH(1,1) so prevalent in modeling volatility, especially in academia?
What does this model offer that makes it significantly better than the others?
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Is a linear combination of GARCH processes also a GARCH process?
If two time series follow a GARCH process, and a third is a linear combination of them, is the third also GARCH process?
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GARCH model, expectation of volatility?
Consider a time series $\{r_t\}$ following a standard GARCH(1,1) model, i.e.,
$$ r_t = \sigma_t \epsilon_t,$$ where $\epsilon_t \sim N(0,1)$ and are i.i.d, and
$$\sigma_t^2 = \omega + \alpha_1 r_{t-1}^...
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Difference between GARCH and Heston Volatility model
I know that the difference between the GARCH and the Heston model is volatility vs variance in the stochastic part of the volatility sde. However,from my solutions, there is only ever a 2 - 10 cent ...
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Problems with dealing with GARCH models and intra-day data
A Short question would be "Which type of model from GARCH family is most suitable for modeling 5-minute data returns ?" but I've added some story to it.
A Long time ago I was preparing my thesis, one ...
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Does GARCH derived variance explain the autocorrelation in a time series?
Given a time series $u_i$ of returns (where $i=1,\dotsc,t$), $\sigma_i$ is calculated from GARCH(1,1) as
$$
\sigma_i^2=\omega+\alpha u_{i-1}^2 +\beta \sigma_{i-1}^2.
$$
What is the mathematical ...
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How GARCH/ARCH models are useful to check the volatility?
Below a R code wrote by the moderator @richardh (whom I want to thank again) about ARCH/GARCH models.
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GARCH(1,1)-M MLE optimization with fmincon in R
I've searched thru dozens of papers and did not find in any of them satisfying and enough theoretical answers to my concerns. So I've combined everything what I found below. Please indicate if my ...
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GJR-GARCH Model In R
Any idea how to estimate GJR-GARCH models in R? Is there any particular library like fGarch that supports such models?
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ruGarch - Interpret test results
I'm working on a R project, trying to calibrate a GARCH (so far, (1,1) ) model to the yields of the STOXX50 index over the last 2 years.
I've tried the garch function of the tseries package, but it ...
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2-step estimation of DCC GARCH model in Python
Embedded in this thread are multiple questions. I'm currently im the process of implementing a DCC GARCH forecast model on quantopian (a python-powered trading platform).
The two step consists of ...
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Correct procedure for modelling GARCH for forecasting volatility of stock Index returns
I will be using Eviews and am looking to forecast volatility of stock index returns using ARCH/GARCH models. I've generated the logarithmic returns and done the unit root tests. I then proceeded to ...
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garchOxFit in R
Could someone please help me with trying to get the Ox interface to work in R. I followed the steps outlined in this paper (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1752095), but I get the ...
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Negative signs in GARCH equation
When one try to fit a GARCH on a time series it may happen that one or more coefficients in the estimation output have negative sign. In these cases:
all the negative coefficients (and relative ...
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Problem with the maximum likelihood for a GARCH-type of model
I'm currently working with the following GARCH process from Heston and Nandi (2000):
\begin{align*}
r_{t+1} - r_f &= \lambda h_{t+1} - \frac{h_{t+1}}{2} + \sqrt{h_{t+1}}z_{t+1} \\
h_{t+1} ...
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distribution of AR, MA coefficients estimation in ARMA-GARCH models
could anyone give me an information about distributions of AR and MA coefficients via estimation?
So, for example, I have ARMA(1,1)-GARCH(1,1) model with the same AR(1) and MA(1) parameters ...
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GARCH(1,1) parameter estimation optimization method
In estimating a GARCH(1,1) model,
$$\sigma_{t+1}^2 = \omega+\alpha \epsilon_t^2+\beta\sigma_t^2$$
Usually the parameter tuple $(\omega,\alpha,\beta)$ is estimated by the quasi-maximal likelihood. ...
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To calculate the Hedge Efficiency and Optimal Hedge Ratio with BEKK in R
I estimated an MGARCH-BEKK model (using the R package BEKK, i.e. Baba, Engle, Kraft and Kroner; see Engle and Kroner (1995)) on time series of spot and futures ...
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GARCH(1,1) variance forecast in one-step or multi-step?
I would like to forecast the daily variance of a stock using GARCH(1,1) model while I have high frequency data of 5 minute returns. What is the difference between applying GARCH(1,1) in one-step ...
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Constant decreasing volatility, GARCH forecasting
I am trying to forecast the volatility using GARCH modelling in R.
I fit an ARMA(1,1)-GARCH(1,1) model, but my sigma predictions are constantly decreasing. Anybody know why?
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HNGARCHFIT in R (No standard deviations or P values printed)
When I estimate an HN-GARCH model using the hngarchfit() from the fOptions package in R, only the coefficient estimates are printed. There are no standard deviations or P-values printed. Does anyone ...
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Can I do a GARCH model to forecast a time series?
I read this paper
https://research.aston.ac.uk/portal/files/240393/AURA_2_unmarked_Energy_demand_and_price_forecasting_using_wavelet_transform_and_adaptive_forecasting_models.pdf
the two authors ...
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Is there any way to estimate a multivariate GARCH-MIDAS model in R?
I'm writing my master thesis in economics, and would like to research the impact of both financial and macroeconomic variables on the S&P500 index. My plan was to use a GARCH model. I've stumbled ...
anders
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Accuracy for GARCH models
How does one calculate the accuracy of forecasts given by GARCH models considering GARCH is run on returns. Assuming GARCH is a derivative of a regression based prediction model, would regular ...
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Rolling forecast using GARCH model
EDIT
This is not a duplicate of my original question linked, since I have since overcome that problem and have posted an answer. Since solving the previous problem, I have run into the problem ...
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