Questions tagged [garch]

A model for time series in which the conditional variance is time-varying and autocorrelated.

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GARCH mean parameter estimation in R (rugarch)

I cannot seem to figure out how rugarch calculates the parameter $\mu$ when fitting a specific model For example: ...
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45 views

How to make optimization work normal in GARCH estimation, and question on standard errors?

I am currently trying to implement GARCH-M (garch in mean) model in Python (cannot use existing packages, and just want to understand the ground). I wanted to write not a big (but enough) piece of ...
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how to pick the daily volatility component in Multiplicative Components GARCH modelling?

Recently I've been drawn to the rather interesting Multiplicative Components GARCH model for intraday volatility modelling, a draft paper written on it can be found here: Chanda, Engle, Sokalska, 2005 ...
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Have log returns series almost always conditional mean zero? I presume no

I'm analyzing S&P500 stocks daily log-returns on the 505 time series of the biggest companies in the USA between 2014-01-01 and 2019-12-01. My task was to identify the ARMA-GARCH model of them. ...
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R-squared from conditional mean equation in GARCH models (rugarch package)

I was recently asked to report the r-squared statistics together with the estimations of GARCH models with exogenous regressors on the conditional mean equation. However, there is no function to get ...
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Negative unconditional variance forecast for EGARCH model in R

I'm trying to model several financial time series and to get some forecast on them. Considering the log-returns, I fitted an EGARCH(1,1) on the data and got the parameters, as known constraint-free ...
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1answer
28 views

How do ARMA and ARMA-GARCH models relate? [duplicate]

I would like to understand how GARCH models work but I'm having some problems. I have a highly persistent AR time series and I would like to model the conditional mean as well as its conditional ...
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Multivariate Garch DCC-ROLL in R (RMGARCH)

Little Disclaimer I originally posted this on Stack Overflow, but I'm not sure which is the correct place, because this question demands a knowledge of Econometrics. So, I'll replicate here and if I'...
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Efficient online (rolling window) estimation of a GARCH model

I have a time series $x_t$ of length $n$. I would like to model it using rolling window approach with window length (width) $w$: window $1$: $x_1,\dots,x_w$, window $2$: $x_2,\dots,x_{w+1}$, $\dots$, ...
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1answer
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Non Gaussian QMLE for GARCH(1,1)

What is the difference between QMLE and MLE method to estimate GARCH parameter? Because both maximizes the same log likelihood (?) I tried to estimate the GARCH(1,1) parameter by using quasi maximum ...
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Interpretation of the ACF of standardised residuals vs actual residuals

Is there any scientific reason why a lot of studies and packages choose the ACF plot of the standardised residuals rather than the residuals themselves?
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Erroneous fomula in `rugarch` package documentation [closed]

I think there is a mistake in the documentation of the rugarch package. In page 9, where it gives the equation of the fGARCH (family GARCH), it includes a $\sigma_{...
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Scenario Analysis with a GARCH model - conditional forecasts with hard restrictions on dependent variables?

Waggoner and Zha (1999), see reference below, developed an approach to produce conditional forecasts for VAR models with hard restrictions on the variables using Gibbs Sampling. As an example, they ...
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AR-ARCH conditional variance

Consider a AR(1)+ARCH(1) model: \begin{align*} &x_t=a_0+a_1x_{t-1}+u_t,\\ &u_t=\sigma_t\epsilon_t,\>\>\>\epsilon_t\sim N(0,\sigma^2_{\epsilon}),\\ &\sigma_t=\sqrt{b_0+b_1\sigma^2_{...
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GARCH modelling and making predictions

I have never applied a GARCH model to actual data and I was skeptical about the following, which was told to be by my professor. He said that to use GARCH for modelling, we first fit a model on the ...
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A Primer on Orthogonal GARCH Model Covariance Matrix

I am trying to replicate Table 3a: Correlation Matrix from this paper (Page 11): http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.201.7226&rep=rep1&type=pdf. (I believe there is a ...
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Z-score from Skewed Student T

I'm implementing the following method. The text is provided for background, but my question is about line (8). Am I understanding this as "a z-score generated from a standardized skewed Student t?" ...
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ugarchfit diagnostic tests: goodness of fit

I modeled a couple of financial return time series with different kinds of GARCH-models and different distribution termns and now whant to compare them. I very often face the problem that all ...
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GARCH modelling by maximum likelihood in R

I would like to estimate the following ARCH model in R by maximum likelihood. This question is related to: Maximum likelihood estimation of GARCH modelling in R The answer given to another question ...
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Evaluating GARCH Model

I used ugarchroll to backtest my garch model on S&P returns. This is my code: ...
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Using garchFit to fit an ARCH(1) process

I tried using the garchFit function FinTs library. This is what I am trying to run: garchFit(formula=~arma(0,1,2)+garch(0,1), data$`1 YR`) However I get the ...
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How do I extract VaR estimation from DCC-GARCH model in R?

I have estimated a DCC-GARCH model in R and I need to extract the Value at Risk for an equally weighted portfolio from the estimated model. ...
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Backtesting GARCH model in R

I used garch to predict the volatility of shopify(SHOP.TO) . I used ugarchroll to backtest my GARCH model but my mean absolute percentage error was incredibly high: ...
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The difference between garch{tseries} and garchFit{fGarch}. the acf of residuals^2 are different. What's wrong?

I simulate a garch(1,1) series, and use garch and garchFit to build model. the acf plots of residual of garch and garchFit model is quite similar. But the acf plots of residual^2 are different, the ...
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GARCH Model Estimation

I am analysing a GARCH(1,1) model under the assumption of t-Student distribution. In particular, I set the problem in the following way. I have a series ${y_t}, t \in{1,2,...,T}$ and I assume that: ...
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Backtesting GARCH model [closed]

Hi I am wondering wether the tgarch model is better for predicting volatility than regular garch I built a garch model in R and im trying to predict future 1 day volatility of this equity I am ...
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GARCH model prediction

I was analyzing a GARCH(1,1) process. In particular, let's say that I have a process ${y_t}$, with $t \in {1,2,...,T}$. I have created a GARCH process that can be written as: $\sigma_t^2 = \omega + \...
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GARCH model with constant average

I am modelling an univariate GARCH and I would like to know if I am operating in the correct way. First of all, I suppose that the performance of my asset can be described as: $Y_t=\mu+\epsilon_t $ ...
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For GARCH model Bayesian estimation is better than ML estimation [closed]

In the work of [Ardia] - Financial Risk Management with Bayesian Estimation of GARCH Models_ Theory and Applications in which the Bayesian estimation method with uninformative prior ...
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ARIMA + Rolling Window

I'm currently working on building an ARIMA+GARCH model using R. My dataset consists of the logarithmic returns of the Dow Jones index for a period of 11 years 2005-2016, however, it's worth noting ...
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Conceptual explanation of Maximum Likelihood Estimation

Given a generic time-series $$y_{t+1}= \alpha y_{t} + \Sigma_{t+1}^{1/2}\varepsilon_{t+1} \quad \text{with} \quad \varepsilon_{t+1}=N(0, I)$$ where $\Sigma_{t+1}^{1/2}$ indicates the conditional ...
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Why do non-informative a priori distributions be chosen to compare the Bayesian and frequentist estimation method?

For example for GARCH models $$\sigma_t^2=\alpha_0 +\alpha_1 y_{t-1}^2 + \beta_1 \sigma^2_{t-1}$$ it is usual to use as distributions for the parameters of truncated normal distributions with very ...
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Is a GARCH a Bilinear model also?

Following this quote from a 4* econometrics journal, "Note that a linear conditional mean model with ARCH disturbances can be described by a nonlinear specification without ARCH, i.e. the ...
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Why do the non-informative a priori distributions give better results than the frequentist estimate?

For example, in the specific case of Markov-Switching GARCH models why is a non-informative prior distribution chosen for GARCH models with Bayesian estimation and why is this approach better than the ...
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What kind of a priori distribution for the Markov Switching models?

Why in the Markov-Switching models is chosen as prior distribution for the probability of the transaction as follows: $$f(P) \propto \prod_{i=1}^K \left(\prod_{j=1}^K p_{i,j}\right) I \left\{0 < ...
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Should I use a multivariate analysis or N univariate analyses in this case? [closed]

I have 100 Investment funds (Flexible allocation Morningstar category, same investment area, currency and distribution status: the sample is homogeneous) over a 10 yr period. I want to estimate a ...
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Arch models: dependence and squared residuals

I would like a mathematical and intuitive answer to those questions: Why a dynamics like arch in the volatility of a time series implies that the time series is dependent (although not autocorrelated)...
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Kalman Filter with heteroscedastic Q (covariance of the transition noise)

I am looking at a generic derivation of the Kalman Filter (like this but you can take any). And I was wondering, checking all the derivation, why are we forced to assume that the covariance matrix Q ...
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ARMA-GARCH python implementation

When it comes to predicting timeseries with ARMA-GARCH, the conditonal mean is modeled using an ARMA process and the conditional variance with a GARCH process. I've seen tutorials predicting returns ...
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Writing the likelihood and conditional variance in a ARMAX model or regression with GARCH (R rugarch with external covariates)

I was looking at the r package called rugarch (docs) also mentioned in this question and in the Matlab Guide but I cannot see any example of how the likelihood or log-likelihood is computed when ...
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Comparing volatility from different GARCH models with different distribution assumptions

As my master thesis aims to compare the volatility of 3 crypto-currencies, I want to find the best fitting garch model with the best fitting distribution assumption for each of my 3 crypto-currencies. ...
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Regression with GARCH error term

Let's consider the following multivariate regression ($y_{t}$ and $x_{t}$ below are matrixes of appropriate size) where the error term is assumed to follow a GARCH process: $$y_{t}=\beta x_{t} + e_{t}$...
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Can I continue ARIMA model despite my time time series has heterodasticity?

I estimated ARIMA model with daily gold time series. The residuals' corelogram is flat but its squared is not flat. Already I tried eVİEWS heterodasticity >> arch effect and ı found prob value 0.00 so ...
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How can someone report an r2 for a GARCH(1,1) model?

On page 306, Tambakis and van Royen report an adjusted r2 for a GARCH(1,1) model. How is this possible? What are they reporting? You can find the paper here
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how to model a state space with GARCH noise

I'm trying to model a state space model with GARCH noise and get stuck by the complexity of the equation. so the first equation is a observation equation and second one is a state equation where both ...
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Robustness Statistics on Time Series

For my thesis I'm working with 10 different time series datasets on the volatility of FX options. I applied event-study methodology and worked with the GARCH method, looking for announcement effects ...
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How to interpret the results from a CCC-GARCH model

I fit a CCC-GARCH model using R's ccgarch2 package. How do I interpret the results? Also, how do I know if the model is suited to my data?
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GARCH (1,1) - stationarity in case of insignificant alpha?

the questions are about GARCH-t (1,1) [t-distribution]. The first question in GARCH-t (1,1) model, the alpha (ARCH) is insignificant. How to rewrite the model? The second one, in case of ...
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ARIMA GARCH NAGARCH model invertibility conditions

According to ARIMA model and GARCH model whose terms are in linear form, we can insist on invertibility conditions involving coefficients. But for instance in nonlinear asymmetric GARCH model(NAGARCH) ...
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QML vs MLE for GJR-GARCH models

I am writing my master's thesis and using a AR(1) GJR-GARCH(1,1)-EVT-Copula model on my data. One of the main papers I use is McNeil & Frey (2000), in which they only do AR-GARCH-EVT. In this ...