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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What are the current gold standards for volatility prediction error?

I'm working on volatility forecasting models for equities and currencies. I am using daily data and am interested in producing forecasts for the next n days. To ...
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348 views

Joint estimation of GARCH models with ARMA terms in the conditional mean: a necessity?

Supposing I am using the following models to forecast conditional volatility of index returns, whereby In-sample data is 1996-2007 and out of sample data is 2007-2012, using GARCH type models. I have ...
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Questions on the concept of GARCH model [closed]

As we all know, the GARCH model is stated as $\epsilon_t = \sigma_tz_t$ $\sigma_t^2 = w + \sum^q_{i=1}\alpha_i\epsilon_{t-i}^2 + \sum^q_{i=1}\beta_i\sigma_{t-i}^2$ In application, the estimate $\hat{\...
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Most GUI user friendly Time series Econometrics software for modelling and Forecasting GARCH models [closed]

I think the question is simple enough. I have been using Eviews, but it is unable to do recursive one step ahead forecasting directly and requires me to use coding, which I'm not very good at. I need ...
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802 views

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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Heston & Nandi GARCH model, parameters estimation from option data

I wonder if anybody has code for the HN-GARCH model where the parameters is NOT estimated with maximum likelihood and instead estimated by looking at the option data where an loss function is chosen ...
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VARMA GARCH modelling in R

I want to simulate a VARMA-GARCH process in R. Unfortunately, I found no package to help me with that. I tried modelling the MGARCH part on itw own and combine it with the VARMA simulation using MTS ...
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R GARCH simulation providing whole components (y, cond.vol. etc.)

fGarch::garchSim provides only realizations of y variable, is there way to obtain also conditional variance and time series of ...
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Rotations and Shifts in the f-GARCH News Impact Curve

I re-post my question from the Cross Validated section as requested by another user. I am using the beautiful "rugarch" package and presently have an issue concerning the interpretation of two ...
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390 views

Forecasting volatility with rugarch and Covariance Matrix

I am trying to do a financial time series forecast in order to build a portfolio. I already have some code running rugarch library and I am not sure if I am forecasting correctly, after that I would ...
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Fitting GARCH(1,1) in Python for moderately large data sets

I am using the arch package in python to fit a GARCH(1,1) to fit daily S&P 500 returns from 1990 to 2017 (about 6800 data points). The code I am using is as follows: ...
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Suggestions for a Master thesis in option pricing models

I am willing to do my Master Thesis about option pricing. Do you have any suggestions? I would like it to be something simple, like comparing methods, e.g. compare ARCH and GARCH approaches for ...
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What is the difference between conditional volatility and realized volatility?

I am working on conditional volatility and realized volatility but the difference between these two measures is not clear to me. Can anybody explain how these two volatilities are related? Does the ...
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517 views

Modeling tail data using Generalized Pareto distribution

I just estimated a ARMA(1,1)+GARCH(1,1)+Threshold order(1) equation for time series of stock prices. Now I'm going to estimate the residuals' marginal ...
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Negative constant in GARCHX model

I am fitting the following ARX(1,1)-GARCHX(1,1,1): \begin{align*} y_t&=c+a_1y_{t-1}+\gamma_1x_t+\varepsilon_t\\ h_t&=\delta+\omega_1h_{t-1}+\theta_1\varepsilon_{t-1}^2+\pi_1x_{1,t} \end{align*...
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How is a GARCH model readily complementary to a forecasting model?

Hi Quantitative Finance Stack Exchange, It's my first go at GARCH models so give me a chance with my phrasing. I'm looking for an answer to a general question. First, I understand that you can have ...
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Are GARCH models dependent on the returns forecasting model?

Hi Quantitative Fiance Stack Exchange, It's my first go at GARCH models so please give me a chance with my phrasing. I understand that GARCH models are used to forecast volatility. The GARCH(1,1) ...
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Why are my GARCH forecasts biased?

I've run an ARMA(1, 1)-GARCH(1, 1) model with normal density on log returns for twelve stocks. I computed the one-step-ahead out of sample forecast for daily volatility on a rolling windows for 500 ...
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MSGARCH package on R

I am using the MSGARCH package on R to fit a Markov switching GARCH model. I fit the GARCH model using fit.MLE (so standard Maximum Likelihood), using three regimes. The parameters are estimated and ...
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Using GO GARCH to optimize a yearly-rebalanced portfolio based on daily data

Is it reliable to optimize portfolio weights on a yearly-rebalanced portfolio based on the Generalized Orthogonal GARCH (GO-Garch) covariance, coskewness, and cokurtosis matrices with the rmgarch R-...
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Price return or total return for GARCH models

Is there a problem in modeling total return rather than price return when using GARCH models? My line of thinking is that total return includes dividends, which is only a "pseudo-random variable" in ...
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146 views

References for biased forecasts from EGARCH

A few months ago I've read somewhere that although the exponential GARCH model may lead to higher BIC values in comparison to other extensions of the GARCH family (GARCH, GJR-GARCH, TGARCH, ...), ...
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Transformation of GARCH Equation to multiple-day Forecast Equation

I want to understand the procedure of how to predict with the GARCH Modell. Therefore it is said that a one day ahead forecast is easy due to the fact that the GARCH equation can produce this. ...
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LSE GARCH Modells

currently I am working with GARCH Modells. And it came to my attention that for the parameter estimation Maximum Likelihood approaches are commonly used. However I was wondering why Least Squared ...
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How to estimate an Engle's asymmetric DCC model in R?

I have a $N x d$ matrix of standardized residuals, and I want to estimate the parameters $\alpha$, $\beta$ and $\gamma$ of the asymmetric version (Cappiello, Engle, Sheppard, 2006) of the usual ...
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Robust standard errors in GARCH modelling (rugarch)

I am currently conducting some GARCH modelling and I am wondering about the robust standard errors, which I can obtain from ugarchfit() in ...
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Regression coefficient and basic trading strategy

This question might be very basic but still I couldn't really find a satisfying answer anywhere. I want to analyse the effect of a repeated event (data release) on the price of a specific asset (I ...
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1answer
131 views

Streaming update of the GARCH(1,1) model

Given the estimate of GARCH(1, 1) model parameters I observe the new price. How to update the estimate with this new information. Let's assume I know the coefficients that maximize the likelihood ...
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GARCH models vs VIX

I am examining how investor sentiment affects the probability of stock market crises. I am using methodology similar to this paper https://ideas.repec.org/p/dij/wpfarg/1110304.html. VIX (equivalents) ...
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GARCH variance vs standard deviation for volatility

in my series of questions related to GARCH and volatility I finally think I've got a decent grasp on it. You guys have been great help clearing up my questions for me. My next question is just a ...
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GARCH volatility modeling, squared returns, and convergence

After reading some more of Volatility Trading, I decided to try to make a simple volatility model using daily log returns of an ETF I follow. It turns out "simple" is sort of relative. Unfortunately, ...
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284 views

Problems in computing VaR with GARCH-GPD-copula approach

I use a time-varying Gaussian copula (with GARCH-filtered standardized residuals modeled semiparametrically with Gaussian kernel interior and GPD tails, i.e. generalized pareto distributed) to ...
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What is the unconditional variance for a GARCH model?

I want to use a Matlab script to calculate Heston Nandi GARCH prices. I found an appropriate script online and it asks for the "unconditional variance" as an input. How do I calculate the appropriate ...
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282 views

Question regarding volatility forecasting using High Frequency Data

Hi guys this is my first question on the Quantitative Finance section of the Stack Exchange network. I am currently reviewing the paper by Professor Alan E. Speight and David G. McMillan 'Daily FX ...
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False warning messages in R, is it possible?

I'm modeling GARCH-filtered standardized residuals via semiparametric distribution with Gaussian kernel and GPD (generalized pareto distribution) tails with thresholds at 5% and 95%. For some series I'...
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630 views

Forecasting conditional returns in DCC-GARCH-copula approach in R

anyone who could help me interpreting and modifying this code? I have a dataset and want to reserve the last 100 returns for out-of-sample analysis. After specifying and fitting the garch-spd-copula, ...
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rugarch: GARCH external regressors

I'm currently playing around with the great rugarch package in R. However, I tried to test the external regressor functionality. I implemented a GARCH(1,1) process and compared it with a GARCH(0,1) ...
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'GARCH - extreme value theory - copula' approach to estimate risk measures in R

I'm reading about this approach of using GARCH-EVT-copula methodology to separate univariate and joint estimation and then estimate for example VaR and ES. I wanted to try something similar, but my ...
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340 views

GARCH Model Constant in Regression

When regressing a variable on a constant of 1, the coefficient of this constant is the mean. However, when I specified that the residuals follow a GARCH(1,1) model, the coefficient of the constant ...
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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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377 views

Define the order of GARCH(m.s)

I know that if the order of Arch(m) is over 3, we should use GARCH and GARCH(1,1) was proved to be the best. But was GARCH(1,1) proved to be available for any country's stock market? My result show ...
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logarithm and absolut value in returns of stocks [closed]

Well, i'm interested in model a GARCH for a serie. The original serie is $y_t$ (price index of a Stock Market), which has a unit root. So i create the returns: $x_t = ln(y_t) - ln(y_{t-1})$. Now, i'm ...
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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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238 views

GARCH model is better for index than stock

We have used a standard GARCH(1,1) model with t distributed innovations for daily data of S&P index and JPM stock. Question: is there any financial or statistical reason why the GARCH model ...
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614 views

Modelling log-returns and calculating the portfolio return

I know this might be a trivial question, however, I would be grateful for some clarification. I am working on weekly log-return data, doing volatility-foracasting using GARCH models and then using ...
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How to choose a GARCH model which delivers iid standardized residuals?

For my thesis I first need to examine nine financial time series and fit a conditional volatility model such that the obtained standardized residuals ($z_t = \epsilon_t / \sigma_t$) are approximately ...
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Fitting Copula and Simulation

I would greatly appreciate any insights into the problem described below, regarding using the data obtained from applying the functions of the rugarch package ...
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60 views

Distribution of AR and MA polynoms roots in ARMA/ARMA-GARCH models

I have another noob question. So, for example, I have ARMA(2,2) model: $$ x_{t} = \phi_{1}x_{t-1} + \phi_{2}x_{t-2} + e_{t} + \theta_{1} e_{t-1} + \theta_{2} e_{t-2}$$. So, I have 2 polynoms: $$1 - \...
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Package for multivariate Garch Vech model for R?

I`m new to programming and searching a package for R which inherents the estimation for a Vech Garch(1,1). This is a multivariate Garch model which forms the residuals and the covariance matrix from a ...
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268 views

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 ...