Questions tagged [garch]
A model for time series in which the conditional variance is time-varying and autocorrelated.
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GARCH and need (or lack there of) for weak-stationarity of underlying data
For most applied examples I see they fit GARCH models directly to time series datasets with no underlying tests of stationarity beforehand.
For example, a GARCH(1,1) has the assumption that the ...
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Interpret the conditional Sigma (vs Realized Absolute Returns) from DCC [duplicate]
I have estimated the DCC model and then plotted the model. Can someone help me understand how can I interpret the Conditional Sigma (vs Realized Absolute Returns) of the DCC model?
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Interpret the Conditional Sigma (vs Realized Absolute Returns) of the DCC model
I have estimated a DCC model and now I have plot the dcc.fit of the model and now I would like to know if someone can help me to interpret this graph as I am new to ...
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Recreating Time Series by Simulating ARMA-GARCH Process + Fitted Residuals, using rugarch package in R
I was trying to use rugarch to fit an ARMA(1,1)-GARCH(1,1) model to the log-returns of the S&P500, extract the standardized residuals, and use them to simulate ...
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Residuals from GARCH(1,2) model look almost identical with the starting dataset (financial market returns) -- is this normal?
Caveat emptor: I'm relatively new to (G)ARCH models - here using financial markets as dataset just for convenience. If my question is known topic / logical fallacy please point me in the right ...
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How to evaluate GARCH volatility prediction performance with Python's arch package?
I have a question regarding the evaluation of volatility forecasts using Python's arch_model class. I am very new, so I sincerely apologise if this is obvious, or ...
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How do I find the marginal distributions of a GARCH(p, q) process?
Let $X_t$ be a stationary GARCH(p, q) process. Given the coefficients for the process, how do I derive the stationary/marginal distribution? What about the distribution after only marginalizing out ...
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R and Time Series Analysis; Suggestions for forecasting a series with a shock
I believe that the reprex below is self-explanatory.
I would like to extend a monthly time series by forecasting the next 3 data points.
The series is rather volatile and it spikes during the last ...
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Help on GARCH-X model theory
I need to understand how a GARCH-X model (GARCH with explanatory variable) works.
What I've understood so far is:
we have a simple GARCH(1,1) model:
If I add to the conditional variance equation an ...
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Volatility forecast using ARIMA GARCH
I am trying to forecast the volatility of the pair of currency USD/GBP. I am using python ans I used a GARCH model on the returns, but later on I found that I can fit an ARIMA-GARCH model to forecast ...
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Updating the Posterior Psi parameter for an Inverse Wishart Distribution
I am fitting a Mixed Multivariate Normal Distribution where the mixing occurs over the mean $\mu_j$ and the covariance matrix $H$ with mixing parameter $B_j$. The number of mixing elements is denotes ...
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Unconditional variance of AR(2) + GARCH(1, 1)
I am being asked to derive the unconditional variance for stochastic process $\{Y_t\}$, where:
$$Y_t = \phi_1 Y_{t-1} + \phi_2 Y_{t-2} + \varepsilon_t$$
$$\varepsilon_t = V_t \sigma_t$$
$$\sigma_t^2 = ...
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How to model a GARCH(1,1) with covariate?
The purpose of my study is to understand if changes in environment policy or changes in people concerns about climate change affects volatility or if they can help in the prediction of volatility. In ...
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Should a covariate be lagged in a GARCH-X model?
I am modelling Dow Jones returns using a GARCH(1,1) model but I also want to estimate a GARCH(1,1) by inserting a covariate to check if this covariate affects the volatility in some ways. The ...
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Parametrizing and Sampling Multivariate Garch Parameters Metropolis-Hastings MCMC
My question is how to sample multivariate GARCH parameters from a proposal distribution (multivariate normal) for a Metropolis-Hastings algorithm. Considering the different dimensions of the parameter ...
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Is this a white noise? Can I use ARCH/GARCH models on this?
I am trying to find out if I can use ARCH/GARCH models. To my knowledge, to use ARCH/GARCH models you should have autocorrelation and this correlogram should not be a white noise. How can I know if ...
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GARCH model: convergence of the conditional variance to the unconditional variance
Suppose a monthly, stationary time series. The series seems to some ARCH effects and I model its variance as a GARCH process. I obtain the following output of a GARCH(1,1) model:
alpha ($\alpha$): 0,...
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HAC estimator in GARCH models
In my time-series class we learned that the HAC estimator is only applicable to correct the standard error (SE) if the underlying series is stationary. Now, GARCH models are unconditionally stationary ...
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How to model volatility spillovers between some financial time series?
I am doing research to study volatility spillover effects between several financial time series $\{x_{1,t}\}, \dots, \{x_{k,t}\}$ (in my case, $k=4$). What would be the best model to study the ...
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Interpret high p and q orders of GARCH models
I am currently working with GARCH models (sGARCH, E-GARCH and GJR-GARCH). My question is very general. I chose my p and q orders with the help of AIC criterion. The best models are sGARCH(2,3),
E-...
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Obtain minimum-variance hedge ratio from a copula-GARCH model
Let $r_{s, t}$ and $r_{f, t}$ be the return rates of the spot and futures of a commodity at time $t$. The hedging ratio based on variance minimization is calculated by finding the minimum of the ...
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Model selection in simultaneous ARMA-GARCH modeling without AIC [closed]
How does one determine the mean model and the variance model in simultaneous ARMA-GARCH modeling without using AIC?
Rather than two step look at ACF/PACF of residuals squared of ARMA to specify the ...
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Estimate GARCH model in EViews after removing missing data
I want to estimate daily stock prices and natural gas data with using GARCH in EViews. There are some missing days (like holidays etc.). When I extract them from the data set, ARCH is not working. ...
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Are nested models of ARIMA, GARCH, or VAR individually comparable with likelihood ratio tests?
Are nested models of ARIMA, GARCH, or VAR individually comparable with likelihood ratio tests whose null follows the chi-squared distribution?
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How to calculate the Fisher Information Matrix in GARCH?
How to calculate the Fisher Information Matrix in GARCH?
I want to know how to calculate the standard errors in them and without the empirical fisher information, empirical likelihood, I have no idea ...
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How is the objective function of the different flavors of GARCH different?
How does the objective function/likelihood function of these different GARCH variations differ? Is it convex in all cases? Knowing convexity tells me whether some are not possible to find a globally ...
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If a time-series achieves max-likelihood at GARCH(1,1), would EGARCH, or other GARCH variations achieve global maximum likelihood at p=1, q=1?
If I find that a time-series fits GARCH(1,1), would EGARCH, or other GARCH variations still be X-GARCH(1,1)?
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Aggregating Multiperiod DCC-GARCH Forecast Covariance Matrices
Say I fit a $DCC$-$GARCH(1,1)$ model to a dataset of weekly returns for four assets.
I forecast the covariance matrix for the next month (so four weekly steps ahead). This gives me four $4 \times 4$ ...
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ARIMA - GARCH or AR-GARCH
I am looking at equity returns and they are not stationary at level. So i take the 1st difference to make them stationary.
My GARCH(1,1) model is modeled using an AR(1) parsimonious model. But, since ...
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Mean-level forecast from rugarch does not match manual calculation
I am looking into the rugarch package and am trying to understand how the one-step-ahead forecast is calculated.
Specifically, I am fitting an AR(2)-GARCH(1,1) ...
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Why is non-normality of time series not a problem for ARIMA and GARCH?
My time series is very leptokurtic and non-normal, which is of course highly common for time series data. However, I don't exactly understand why that is not a problem for ARIMA modeling and GARCH ...
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Is ARIMA-GARCH nested within ARIMA?
I wanted to compare ARIMA(1,1,1)-GARCH(1,1) and ARIMA(1,1,1) model forecasts with a Diebold-Mariano test, but I know that it cannot be used for nested models. Is ARIMA-GARCH technically nested within ...
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Interpretation of Li-Mak test [duplicate]
I have performed a Li-Mak test Weighted.LM.test on the squared residuals from a fitted GARCH(1,1) model. However, I find the understanding of the null hypothesis ...
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Can I compare model output from GARCH and EGARCH when the EGARCH is log conditional variance?
I have used the rugarch package in R to construct a sGARCH and an eGARCH model, but have ...
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python arch lib incorrect conditional volatility values, while tgarch and gjr garch vols are correct
Somehow when I estimated a GARCH model using arch.arch_model, its resulting conditional volatility took values that are not correct (around 12, cf picture). I did the exact same process for GJR GARCH ...
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Forecasting with ugarchforecast
I'm a bit confused on how to use the ugarchforecast function for forecasting. I estimated a GARCH(1,1) model based on the training dataset (...
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AIC/BIC of ARIMA and ARIMA-GARCH
I was modelling a time series with an ARIMA(1,1,1) model which had an AIC of -4782.96. However, after checking squared residuals and performing ARCH tests (Engle's and McLeod-Li) I detected the ...
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Formal test for TARCH (threshold ARCH) errors?
The statsmodels package includes a generic Lagrange Multiplier test for residual autocorrelation. The documentation mentions
Returns Engle’s ARCH test if resid is ...
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Does the Absence of a Unit Root Imply Wide Sense Stationarity?
I'm taking a course on time series currently and have been slightly confused about the interplay between unit roots and stationarity in a question I've been attempting to answer. The question set up ...
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Forecasting using Copula GARCH methods
I need to replicate what Huang and al (2009)* did without using built-in functions in R. What I'm struggling with is how to forecast returns for my two data samples. I've found the GARCH specs and ...
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How to estimate Pearson type-IV-GARCH using MLE in R?
I am just wondering whether there is an R program that can be applied to run GARCH specifications with Pearson types IV distributions. If you are familiar with any or can guide, it is greatly ...
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Estimating parameters GARCH model
Suppose I have the following model
\begin{equation}
R_{i t}-r_{t}=\alpha_{i}+\beta_{i}\left(R_{m t}-r_{t}\right)+s_{i} S M B_{t}+h_{i} H M L_{t}+\varepsilon_{i t}
\label{eqn:egarch}
\end{...
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GARCH(2,1) interpretation
I have a GARCH(2,1) model that mitigates heteroscedasticity, i.e. no ARCH effects.
I use GARCH(2,1) because GARCH(1,1) didn't mitigate heteroscedasticity.
Below I will give the values of the ARCH &...
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Lag selection and model instability for ARIMA-GARCH in rolling windows for forecasting
I'm to produce rolling forecasts with an ARIMA-GARCH model using a moving window size of 1000. Given that structural changes in the series might take place at some point in the forecast horizon, is ...
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GARCH model and variance equation
I have wheat prices in log 1st difference. I tested it for ARCH(p) effects, and ARCH effects does exists. So i built an GARCH(p,q) model.
My issue is that I don't know which GARCH model I should use.
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Estimating volatility of 15 stock market indices: univariate vs. multivariate models
I am working with R and with financial series of 15 stock market indices with a weekly frequency.
I want to obtain the estimated volatility since it is the input I need to perform a volatility ...
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Error when forecasting volatility with GARCH model in R [closed]
I am trying to forecast volatility on four different time series which is returns of SP500, Nasdaq 100, Dow Jones and Russel 2000. the four time series consists of 3259 observation and is divided into ...
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Is it possible to create ARCH-GARCH model using MA(q) or ARMA(0,0,q) as conditional mean equation?
I tried to create ARCH-GARCH as conditional variance equation but the result of conditional mean analysis (I use ARIMA) shows MA(q) or ARMA(0,0,q) fits conditional mean modelling.
Data has ARCH ...
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Estimating abnormal returns with a GARCH(1,1) model in R Studio
I'm using the standard market model in an event study analysis to estimate abnormal returns for a particular event. In the baseline model, I use simple OLS and regress the returns of stock i (Ri) on ...
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Why Choose IGARCH over Standard GARCH?
I understand that IGARCH is a nested version of the standard GARCH model where alpha+beta=1, which implies a unit root. Although, I am struggling to see why having a GARCH process with a unit root is ...
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