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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30 views

How to deal with blank entries in computing log growth

I am conducting a study to discover which variables best explain stock volatility during COVID. I am currently completing linear regressions before implementing GARCH, however I have come across a ...
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62 views

Looking for a good introduction to modelling ARCH-type models

I am starting to think about my dissertation topic for my undergraduate degree. I am interested in comparing volatility of stock indices during COVID-19 to the years leading up to the pandemic. I have ...
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1answer
124 views

Confidence Intervals for ARMA+GARCH forecasts

I have fitted an ARMA(1,1)+GARCH(1,1) model to my logreturns series. When it comes to my standarized error's distribution however, I have opted for a Skewed Generalized Error Distribution, because of ...
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338 views

Manually calculating and backtesting VaR and CVaR from DCC-GARCH R

I estimated a GARCH fit to the log returns of three series (CAC 40, a french real estate index and french T10 bond yield series) using rugarch. I then manually ...
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1answer
160 views

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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37 views

Comparison of results given by volatility estimators: Garman-Klass Vs Garch(1,1)

I am pretty new with volatility estimators and I am trying to see if Garman-Klass estimator and Garch(1,1)estimator are closed. So I implemented a python code for the two estimators (an also for the ...
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1answer
942 views

How to fit exogenous + GARCH Model In Python?

I am studying a textbook of statistics / econometrics, using Python for my computational needs. I have encountered GARCH models and my understanding is that this is a commonly used model. In an ...
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2answers
188 views

Extract the short-run and long-run volatility of any time series with component sGarch (rugarch)

I try to estimate a component sGarch model with the rugarch package in R. My goal is to extract the short-run and long-run volatility components of any time series. I am not interested in the ...
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1answer
121 views

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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35 views

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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1answer
27 views

Squared Residuals equal Variance of Dependent Variable (ARMA-GARCH)

My understanding of ARMA-GARCH models for a variable $X$ is as follows: I estimate a conditional mean of a variable $X$ by use of the ARMA part of the model. I estimate the conditional variance of ...
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60 views

Should stock return series be modeled with a parametric distribution, or an autoregressive function? [closed]

If I have prior knowledg that a stock return series follows a parametric distribution, such as a Student t-distribution with 4 degrees of freedom, without actively looking for prior knowledge of ...
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1answer
361 views

VAR-aDCC full ARCH and GARCH parameter matrices in R

I am working with the rmgarch package in R and I estimated a VAR-aDCC model. Is there any way to extract the extended version of estimates (allowing for volatility ...
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21 views

GARCH model with exogenous events

GARCH models capture positive serial correlation in volatility. Sometimes events occur "out of the blue", causing volatility that a GARCH model cannot be expected to predict. One example is ...
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22 views

EGARCH and GARCH effects with White Noise squared residuals

I'm asked to model a series which it's returns are white noise and after adjusting a regression like $r_t=c$ and looking it's squared residuals (white noise too) I'm asked to adjust a GARCH and EGARCH ...
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269 views

When the two time series with different length, how could we analysis them with a bivariate GARCH model?

At this moment, i need to do the analysis of rouble/us dollars exchange rate and the stock market index in Russia, I prefer to do that in a multivariate GARCH model. However, I have a question about ...
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How to fit a SARIMA + GARCH in R?

I'd like to fit a non stationary time series using a SARIMA + GARCH model. I have not found any package that allow me to fit this model. I'm using rugarch: model=ugarchspec( variance.model = list(...
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246 views

Why is volatility unobservable even ex post?

I am looking into how to measure volatility, and I am not sure if I have confused myself too much in my research. So now I really need your help. So please either confirm my understanding of ...
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1answer
51 views

$n$-day ahead forecast for asymmetric DCC-GARCH model

I am working on forecasting covariances with the use of MGARCH models. I was wondering if anyone knows how to implement a n-day ahead forecast of the aDCC (asymmetric DCC) model in R. The ...
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1answer
73 views

Ratios or combinations of risk measures

In finance, alternative risk measures such as value-at-risk (VaR) and GARCH are introduced as replacements to standard deviation volatility. Is there any application or value where several risk ...
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17 views

How to estimate Hodrick Standard Errors in R

Does anyone know how to implement Hodrick Standard errors in R? I could not find any package for it in R. Is anyone aware of the same or any open source code that implements it? I want to use Hodrick ...
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1answer
172 views

evaluating garch models

I used ugarchroll to backtest my garch model on S&P returns this is my code ...
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1answer
221 views

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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129 views

How to project 1 Year ATM Implied volatility for SPX 500 1Year from now? Final goal is to calculate 1 Year Call prices on SPX 500 1 year from now?

I have the historical data for 1Year ATM Implied Volatility on SPX 500. I want to simulate the 1 year call option prices 1 year from now. What methods and approaches do I need to use? (Heston,GARCH, ...
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51 views

GARCH(1,1) forecast plot in R with training data

I've fit a GARCH(1,1) model in R and would like to create a plot similar to the one in this question: Is this the correct way to forecast stock price volatility using GARCH Could someone direct me to ...
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139 views

Can we model Implied volatility using GARCH?

Can I use Implied volatility as a dependent variable in a GARCH model? I believe my IV data shows ARCH effects and hence can I use it to model volatility of the volatility? I know literature has used ...
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201 views

Marginal Distribution using GARCH model: How to do inverse probability transform?

I have $n$ return series. I fitted AR(1)-GARCH(1,1) to each return series. Then used probability integral transform, PIT(residuals), to transform the residuals to have a uniform distribution. Then I ...
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1answer
147 views

Interpretation conditional volatility plot

I have plotten the log differences of exchange rates and in the same plot, I show the conditional volatility $\sigma_t^2$. The conditional volatility follows approximately the same path, but is much ...
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29 views

Applying GARCH to Panel Data

I have a panel consisting of some quantity - say earnings/cash flows/or something similar. I am interested in forecasting the volatility that is inherent to that respective measure. In a single time ...
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38 views

Can you apply GARCH to ARIMAX models?

Is it possible to apply the idea of GARCH to time series models that include exogenous variables? For example, say I estimate a cash flow forecast model. Does it make sense to model the residuals by ...
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Models that can improve FHS (with possible residuals manipulation)

The Filtered Historical Simulation (FHS) is a tough benchmark. By: choosing among the most complicated ARMA-GARCH variants with automatic model and lag selection, manipulating standardized residuals ...
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4answers
7k views

Multivariate GARCH in Python

Is there a package to run simplified multivariate GARCH models in Python? I found the Arch package but that seems to work on only univariate models. I'd like to test out some of the more simple ...
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1answer
188 views

In-sample volatility measurement

I would like to know what is the most reasonable way to measure volatility in a sample of past observations. Aside from standard deviation, are more complex models like GARCH used for (historical) ...
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34 views

Preferred stock volatility model [closed]

If I want to forecast stock volatility, what would be the best GARCH model and why? (ARCH, GARCH-M, IGARCH, EGARCH, TARCH etc)
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109 views

Why a model like GARCH is only good for daily volatility and not for intraday volatilities?

I´m currently looking to implement an intraday volatility model and I´m new at the quant world and I learned how superior is GARCH family is for daily volatilities, but in the research stage I found ...
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33 views

ARMA Order in GARCH

I want to do a GARCH forecast with a GARCH(1,1) Model but I am confused on which mean model I can or should choose. If I call the Auto.Arima function on the squared returns I get an ARMA(0,4) process ...
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31 views

EGARCH interpretation

I run EGARCH Model for my data, in Mean and Variance Equation.all P value are significant, but my ARCH Coefficient is negative. so my question .. is it ok if I use this model ? or maybe there’s a ...
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52 views

garch(1,1) Annualised Volitility with python

I am trying to calculate the annualized Volatility of given returns for a stock with Garch(1,1) on python using a code I found online. The value I should be getting is around 27, but the value I am ...
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2answers
2k views

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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28 views

Athens Stock Exchange GARCH-M (Paper replication)

I am trying to replicate one part of a paper which tries to model the Athens Stock Exchange daily returns. I do not have the original dataset, so some differences are expected, but when I fit the ...
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33 views

How to calculate all betas for Fama French 3 factor using DCC?

I have already calculated variance-covariance matrices using DCC on HML, SMB and Rm −Rf factors and got stuck here. I usually use regression ​to estimate all β. Can anybody tell me how to proceed? I ...
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1answer
67 views

forecasting hourly variance with higher resolution data available

Assume one has price data $P_{1}, P_{2}, \dots, P_{n}$ with one hour resolution and aims to forecast the variance for one hour ahead return. The first approach to try is ARCH or GARCH models. There ...
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1answer
133 views

How do you handle implied volatility performing a VaR Monte-Carlo simulation using a stochastic volatility process calibrated on the underlying

Say you have a portfolio consisting of options each having a market implied volatility. If you now use some stochastic volatility model like GARCH to calibrate the real world volatility of the ...
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1answer
143 views

VARX DCC GARCH in R for volatility spillover

I have 5 series for which I want to analyze volatility spillover (to and from the series) via VARX DCC GARCH for both dynamic and comtemporaneous effect. Moreover, I would like to analyze seasonal ...
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38 views

Use of ugarchroll vs ugarchforecast: setting parameters

I would like to generate 21 day ahead forecast volatility with ugarchroll. I know it is similar to ugarchforecast with the exception that ugarchroll is a rolling average which considers initially the ...
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37 views

Is variance of residuals of Markov switching GARCH model regime specific?

I'm using MSGARCH package in R. By return_data/Volatility(fit.model), I get the residuals. When I calculate the standard deviation of the residuals, it turns out that it's close to 1 for all residuals....
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36 views

BEKK Garch for time-varying beta in python

I am currently trying to analyse stocks of the S&P500 for their time-varying beta using BEKK Garch in python(jupyter). Unfortunately, I can't find any good packages and the documentation for bekk ...
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52 views

Can you approximate stochastic volatility processes using GARCH processes?

Let me specific. Suppose that you have the following process: \begin{align} z_t &= \sigma_t \epsilon_t \\ \sigma_t &= \sigma \exp \left( \frac{v_t}{2} \right) \end{align} where $v_t$...
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41 views

Python arch_model package last_obs vs. for loop rolling window slight differences

i am using the GARCH package in Python to forecast volatility of the SPX Index. According to the documentation, there are two arguments "first_obs" and "last_obs" first_obs({int, ...

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