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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How to decide if the ARCH coefficient is necessary in the GJR-GARCH model?
I did some analysis for CAC 40, the French market benchmark, for the period 2005-2014, and I tried to fit the data with a GJR(1,1) model in MATLAB.
Then some warning showed
Lower bound ...
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EGARCH formulation
I am a bit confused about the formulation of the EGARCH(1,1) model.
First, we have the error term: $\epsilon_t=\sigma_t*\zeta_t$, where $\zeta_t$ is white noise.
Now the EGARCH(1,1) should be:
$$
log(...
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Residuals in the Ljung box test
does anybody know what type of residuals is used in the Ljung box test in R?
raw or standardized?
Because basically when I fit a GARCH model using garchFit, the summary() function gives me all the ...
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GARCH modelling and forecasting
I have a few questions regarding GARCH modelling and forecasting and it would be great if someone could help me. I am modelling the log return of oil spot prices using various GARCH models: GARCH, ...
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The use of GARCH
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 ...
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Explain the unconditional covariance in Dynamic Conditional correlation( DCC ) GARCH model
Confused about the unconditional covariance matrix in a DCC GARCH model. Could anyone help me understand it? My understanding is that we get the unconditional covariance before based on the data sets. ...
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Constant Conditional Correlation GARCH (1,1)
I am a beginner in R and my econometrics background is not very sound either. I want to build a constant conditional correlation GARCH (1,1) model in R and I found the function, the description of ...
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Is there a way to adjust R PerformanceAnalytics function VaR with EWMA or GARCH method?
Is there a way to upgrade R PerformanceAnalytics function VaR with more risk sensitive approaches like EWMA or GARCH? Or is there another R package which can handle the issue?
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Is there any way to easily estimate and forecast seasonal ARIMA-GARCH model in any software?
I use R to estimate a seasonal ARIMA(8,0,0)(5,0,1)[7] model for the seasonal differences of logs of daily electricity prices:
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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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using garch to forecast volatility but getting low persistence model
I am using a GARCH(1, 1) model to try model volatility for a certain stock.
I have a GARCH function in matlab that returns the three parameters, omega, alpha & beta.
I then use this parameters ...
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What kind of errors arise when I fit ARMA(1,1) to data generated from ARMA(1,1)-GARCH(1,1) process?
As far as I know estimates of parameters of ARMA(1,1) are asymptotically optimal when fitted to data from ARMA(1,1)-GARCH(1,1) process, and only their variance increase, so when we assume large ...
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Why random walk Metropolis Hasting algorithm works bad on GARCH(1,1) parameters estimation
I am trying to estimate the parameters of the GARCH(1,1) model with MCMC method, firstly, I read the paper:
http://mpra.ub.uni-muenchen.de/12985/1/MPRA_paper_12985.pdf
Metropolis Hasting method is ...
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How to find the best fitting GARCH model for a portfolio composed of 3 ETFs in R?
I am doing a project for my class Financial Time Series in which I am trying to forecast my portfolio log returns using a GARCH fit. I am having a bit of trouble determining the best way to fit this ...
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Log returns and GARCH models
I try to model currency rates volatility using GARCH models through the RUGARCH package in R.
Starting from the observed currency rate series, I compute the log-return through:
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Intuition behind interest rate models
I am modelling the 3M yield of US Treasuries using an ARMA/ GARCH approach. Most interest rate models (e.g. Vasicek) describe the process as follows:
$r_{t}-r_{t-1} = some ARMA+ \epsilon_t $
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HAR-RV, realized GARCH and HEAVY model for realized volatility
I don't have much experience with volatility modeling using intraday data but I'm in the process of collecting 5mins data. Currently I have ~6 months of data. Is it enough to use these models with ...
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what volatility do we calculate using GARCH model
what volatility do we calculate using GARCH model,
Historical vol or Implied vol or Future Vol or Actual vol.
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Dummy variable and negative estimation in GARCH (1.1)
I am trying to use GARCH model for my research. However, when I am running them, I see negative value for alpha and beta. How I can restrict them so that they do not provide me any negative value. Is ...
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Volatility estimation: sampling frequency and scaling
I have a year long stock data sampled at 5 min frequency and would like to estimate monthly volatility using it. I am thinking using GARCH or TGARCH for volatility estimation. However, I am not sure ...
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R ARMA-GARCH rugarch package doesn't always converge
I'm trying to compute the standard ARMA(1,1)-GARCH(1,1) as shown in this answer for an entire index,just to store in a database to quickly lookup values for back ...
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How to estimate the following model?
Suppose I have the following model:
$$r_t=\sigma_t * \epsilon_t$$
where $r_t$ is the return at time t, $\sigma_t$ is the volatility, the model used to model this volatility is an exponentially ...
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Understanding the conditioning in a GARCH process
In a GARCH model like the following
$$y_t=\sigma_tz_t,\\
\sigma_t^2=\omega(1-\alpha-\beta)+\alpha y_{t-1}^2+\beta \sigma_{t-1}^2$$
where $z_t$ is assumed to be iidN(0,1), we say that conditional on ...
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volume augmented garch(1,1) model in matlab
Actually I want to add volume traded of a stock in my Garch(1,1) model to forecast the volatility.In Matlab I can specify the model as garch(1,1) and then use estimate and forecast commands.But I am ...
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How would you correct a GARCH model to deal with non mean reverting volatility?
I am currently attempting to model and forecast volatility of bitcoin but have not been able to find a GARCH model that fits the data appropriately. I've used tick data sampled at 1 hour intervals ...
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What impact does arbitrage have on realised volatility estimates?
Doing some research modeling/estimating volatility in the bitcoin market. There is quite a bit of scope for arbitrage within crypto-currency markets. Wonder if this has any impact on my volatility ...
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Cross validation of a garch model
Suppose I divide a time series into 10 sequential time windows, where each window contains 1000 data points.
I want to do test 5 different garch models using cross validation.
So for each model, I ...
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Optimal lag length selection criterion in GARCH(p,q) model using MATLAB
As assessed by the title, I'm trying to estimate a GARCH(p,q) model to forecast stock market volatility and, in order to be able to do that, I've to identify the optimal number of lags, p and q, to ...
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How come the existence of ARCH effect is not a violation of Random Walk Hypothesis 3?
An ARCH (autoregressive conditional heteroscedastic) (1) model is:
$r_t=\mu +a_t$, where $a_t=$return residual, and $\mu$ is the drift of the stock return
$a_t=\sigma_t\epsilon_t$, where $\sigma_t=$...
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What return equation is Engle referring to in his Nobel lecture?
Engle comments in "Risk and Volatility: Econometric models and Financial Practice" that
If the price of risk were constant over time, then rising conditional
variances would translate linearly ...
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Time-Varying Volatility and Conditional Likelihood
Engle's comment in his seminal paper "Risk and Volatility: Econometric models and Financial Practice" mentions that
I had recently worked extensively with the Kalman Filter and
knew that a ...
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Can we model components in a set of multivariate multi-period time-series data?
There are N data sets in periods occurring weekly/monthly, across a 10-year historical timeline.
In each period, five dates are observed (labelled a to e), where a denotes the day the period starts/...
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Evaluation volatility with Garch model
I want to forecast the volatility (with Garch) of a canadian stock in 5 months with daily returns. How many data do I have to collect ?
Thanks.
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Garch modelling on Stata
I would like to ask "how to do GARCH modelling on stata".
Basically I want to estimate stock market volatility using daily data. I have one variable as return series, $r_t=\ln(\frac{P_t}{P_{t-1}})$.
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GARCH(1,1) prediction in R - Basic Questions
Background to question:
Hi, I was trying to fit a GARCH(1,1) model to the variance of log returns of a series, and ARMA(0,0) for the mean. I was using the fGarch package to do this. The aim of the ...
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How to compare volatility models?
What are the most popular ways to compare volatility models?
Suppose I wanted to compare the forecasting accuracy of a GARCH(1,1) model with the historic 30 day volatility. What method should I use?
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Fitting a GARCH BEKK model
I am trying to find whether there is significant volatility transmission between two price series (t=1000). A literature review learned me that the GARCH BEKK model is suitable for this.
The SAS ...
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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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rugarch: Joint estimation leads to different results
I want to fit an ARMA-GARCH model to my data using rugarch package in R.
First of all, I look at the acf and pacf:
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Volatility Return Distribution/Garch Modeling
For simplicity sake, if stock returns are normally distrusted, would that imply that second moment, variance/volatility, is chi-squared distrusted? If so wouldn't that imply the statistics(employed to ...
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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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Improving GARCH modeling approach
Modeling Exchange Rate Using GARCH
Let's consider the following exchange rate : USD/JPY
For each sequence, we consider changes in the daily difference between the highest price and the open price of ...
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Conditional or unconditional volatility?
I am reading a paper (reference below) that states "The conditional volatility for each underlying security (or for a market index) can be estimated using the standard deviation of the stock’s ...
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Volatility models using Rugarch
I have estimated sGARCH, EGARCH and TGARCH, which some for particular models are significant. For others, the alpha remain insignificant using various innovations such as the skewed variants of the ...
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Should I use GARCH volatility or standard deviation in cross-sectional regression?
I want to do a cross-sectional study where the historical, medium-long run volatility of some return series (call it $R_t$) is included as a regressor. Which of the following two estimates of ...
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Rolling window Kendall's tau against APARCH(1,1) correlation
Assume you want to forecast the correlation matrix of a stocks' basket (say 15 ~ 20 stocks from different sectors); assume you need to forecast at $T$ days because you will use the forecast ouput with ...
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Does the correlation amongst stocks rise when stock values decline?
Is there any research on whether the correlations among stocks rise when stock indices decline? Which model could account and test for that effect ? Maybe GARCH-BEKK, or some models using copulas?
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