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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70 views

How skewed are FX returns? Does this look like a plausible histogram of EURUSD?

I'm reading about volatility. I've charted the histogram of EURUSD and I am wondering if this looks plausible? What I've charted are the 1-hour percent change returns (not log returns). I've removed 0 ...
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Correct terminology - estimate or model?

I am doing some academic work and I'd like to summarise the picture around volatility models. As such, I'd like to refer to several ways of estimating volatility and I'd like to use proper terminology....
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Understanding GARCH

I asked this on stats.stackexchange but I realized this might be a better place to ask this question. I am new to finance and volatility forecasting and am trying to understand how garch model works. ...
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95 views

negative gamma value for gjr-garch output

I was wondering if anyone could tell me if my model is completely incorrect as I haven't been able to find anything online for this. I am running a Gjr Garch model to measure volatility in gold ...
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45 views

Do I use % return, log return or diff of prices to plot ACF?

I am reading a book on time series. To make a non-stationary series stationary, sometimes we need to difference the series. When it comes to finance, prices are non-stationary. Many authors fit ARMA ...
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53 views

Is there an alternative to the rugarch package for GARCH modelling?

I have been trying to use the rugarch package but I find it sometimes limiting. After certain amount of data points the package doesn't converge and it becomes kind of annoying. Is there any R/Python/...
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does anybody know a package for the estimation in python of multivariate garch model? [duplicate]

is there any package in python for the estimation of multivariate garch models? (bekk, dcc) i tried with the package mgarch but it provides only a few commands and wanted to know if there are some ...
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Ideal Training Set for a GARCH (p,q) model ( what is the optimal number of periods? )

I have searched high and low in the web and I cannot find a good answer on what is the ideal training set for Garch (p,q). I am attempting to use GARCH to do stop loss for my algo base on the ...
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Is there a HAR that deals with the leverage effect?

The EGARCH is a special GARCH model that treats the leverage effect of the volatility. The HARV does not make a distinction between negative and positive returns. Is there a special HARV that deals ...
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52 views

Forecasting returns and volatility using ARIMA-GARCH model in R

I am using rugarch package in R to forecast returns and volatility of a stock. I train an ARIMA (p ,d q) + GARCH(s, r) model on ...
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Estimation of time series using GARCH on Eviews

Firstly I should mention that I am new to both Eviews and GARCH models. Anyway, I am conducting some research into the effect that different macroeconomic factors have had on stock index volatility ...
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Option pricing and GARCH resources

Can anyone suggest resources for option pricing using GARCH models? Although I have a fairly good knowledge of GARCH models, for some reason I cannot seem to be able to follow Duan's paper and how to ...
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Heston model vs. GARCH

Heston model is a stochastic volatility extension of the Black-Scholes model. On the other hand, there is also closed-form expression for option pricing that uses GARCH stochastic volatility model. ...
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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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72 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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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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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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25 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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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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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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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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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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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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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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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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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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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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157 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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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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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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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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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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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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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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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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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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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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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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how to interpret the results of a GARCH model fit R/python

I have got the following output from a gjrGARCH model, and I need help to interpret it in order to decide whether it is already a good model and proceed with the forecast. ...
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“ugarch” roll from “rugarch” not working in source()

I have an automatic rolling GARCH forecast using the rugarch package in R. It is stored in a file GARCH.R. When I try to run the code using source('GARCH.R'), I get ...
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145 views

Modelling Geometric Browian Motion price model with stochastic volatility

I'd like to generate scenarios (simulate several paths of the process) for several stocks using multinomial Geometric Brownian Motion under Stochastic volatility assumption. I'm going to use it in my ...
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Modelling volatility for higher frequency data

I'm doing some academic work on volatility forecasting. I've got 1-minute bar data. It is not clear to me what model is best suited for forecasting volatility when higher frequency data is available. ...
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Non-Linear Time-Dependent Volatility

My data consist of monthly electricity futures contracts. Unlike other commodities, electricity is delivered throughout a month (rather than on a specific date), which means that, as the active month ...
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Garch models - are they useful for hedging? If so how?

I understand that Garch models are useful to predict volatility. But are they useful for hedging in practice? If I want to hedge volatility, why shouldn't I just use a Variance Swap? In other words, ...
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A simple question about the dummies of structural variance (ICSS) breaks on GARCH

The question related to How to implement dummy variables into GARCH(1,1) model from structural breaks (ICSS) I have a simple question about that. Suppose my series have two breaks on t=2 and t=5 for ...
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76 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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377 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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What is the difference between parametric and non-parametric models?

I'm reading about volatility modelling and I came across the concept of parametric and non-parametric models. For example, GARCH is a parametric model and Realized Volatility is a non-parametric model....

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