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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Master Thesis about Heston vs. Duan option pricing model

I would like to write my master's thesis on volatility in option pricing. My idea was to compare the stochastic volatility model of Heston 1993 with the GARCH option pricing model of Duan 1995. For ...
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In copula modeling for time series data, why do we need to fit ARIMA/GARCH and then work on standardized residulas.?

I have read that for standard copula modeling, you can get empirical cdf of data and use it for copulas. But for time series data, we must first fit ARIMA/GARCH, get standardized residuals, and only ...
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Can the white noise in multivariate GARCH have different distributions?

I have two datasets of log returns, one is clearly normal while the other is t-distributed. I want to fit these with a mutlivariate GARCH model. A multivariate GARCH model is defined as $$\mathbf{r}_t=...
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Estimate of 90-day volatility using GARCH when I have minute-by-minute data

I have minute-by-minute data and would like to use GARCH to produce an estimate for 90-day ahead volatility. I'm using the arch_model library in Python which has a <...
Julie Taylor's user avatar
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Use filtered historical simulation to calculate VaR on a repo trade

I would like to calculate the VaR for a repo trade using filtered historical simulation incorporating GARCH. So, for example, in the first leg, 3000 of bond goes out on day 1. In the second leg, 3000 ...
user20831463's user avatar
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Uncertainty on volatility prediction using GARCH(1,1)

I have daily returns data and I predict the variance for the next day using GARCH(1,1) as follows ...
PhDStudent's user avatar
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Value At Risk Modelling for electricity market with negative prices

I'm a bit at loss after trying to find papers regarding tail risk for electricity markets. There doesn't appear to be a whole lot of literature (or perhaps I haven't managed to find it) regarding ...
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Is there any way to estimate a multivariate GARCH-MIDAS model in R?

I'm writing my master thesis in economics, and would like to research the impact of both financial and macroeconomic variables on the S&P500 index. My plan was to use a GARCH model. I've stumbled ...
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CoVaR/dCoVaR modelling using bivariate DCC-GJR-GARCH

For the several weeks, I have been looking for a way to calculate and display the results of my DCC-GJR-GARCH model to picture a dynamic relationship between daily return of, let's say for example, ...
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Are ARMA-GARCH-type models suitable for monthly data?

I understand that ARMA-GARCH models and their variations are usually applied to daily time series. While I know that such models can be also estimated on monthly data, I have seen few applications in ...
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Expanding window with ugarchroll in rugarch in R

I was wondering whether my code is crafted correctly to satisfy this requirement: use 1:1000 to predict 1001, then use 1:1001 to predict 1002, and so on rOHLC has a length of 10079 ...
Porsche Tan's user avatar
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Garch Model with Vix as external regressor un dummy rugarch r studio

I would like to try to replicate this variance dummied model in r studio, to try to compare garch vs i.v in forecasting vol: Data : S&P 500 log-return from 03.01.2020 to 31.12.2022 Ext regressor : ...
fabdellar's user avatar
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GARCH on returns or on log-returns?

I'm trying to capture heteroskedasticity in the returns of a price time series using a GARCH model. A basic intuition suggests that I should fit the GARCH model on log-returns: indeed, if the price is ...
Jerem Lachkar's user avatar
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125 views

Black-Scholes implied volatility using a GARCH model

Why I'm not getting the same Black-Scholes implied volatility values as the ones given in the paper "Asset pricing with second-order Esscher transforms" (2012) by Monfort and Pegoraro? The ...
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VEC model on log prices, for random simulation?

In the context of pair trading, I’m trying to regress a VEC model on cointegrated pairs (and also a GARCH model on the residual of that VEC model).I would like to generate random réalisations of each ...
Jerem Lachkar's user avatar
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Is my time horizon for GARCH(1,1)/ARCH(1)/EGARCH(1,1) reasonable?

I am trying to learn about volatility forecasting using three models: ARCH(1), GARCH(1, 1) and EGARCH(1, 1) using python. I wanted to know if my general procedure is correct, and specifically if my ...
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How to use GARCH/ARCH/EGARCH volatility forecasts to compare the Black Scholes constant volatility assumption with GARCH/ARCH/EGARCH volatility

I should preface this by saying I am an undergraduate physics student, this is more of a side interest to me, so I apologise if I am missing something obvious. I am not following a formal class or ...
probablysid's user avatar
2 votes
1 answer
169 views

Standardized residual by GARCH model shows bimodal distribution, is it normal?

I fit a GARCH(1,1) model on the spread of 2 correlated assets : the GARCH model shows this summary: ...
Jerem Lachkar's user avatar
2 votes
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113 views

Problem matching prices of Black-Scholes vs. GARCH(1,1) in Duan (1995)

In the paper of Duan (1995) the author compare European call option prices using Black-Scholes model vs. GARCH(1,1)-M model (GARCH-in-mean). To be brief, the author fits the following GARCH(1,1)-M ...
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1 answer
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GARCH process simulation in R

I'm trying to learn how to simulate the GARCH(1,1) for option pricing using Monte Carlo. I need to learn how to code the equations for the stock log returns and the variance process. I'm trying to ...
StochasticNewby's user avatar
2 votes
2 answers
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Assessing the GARCH model out-of-time

I have fitted two competing GARCH models, one GARCH(1,2) model and another EGARCH(1,1,1) both with t-distributed errors, on the ...
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GARCH models for assets with scheduled announcements

How do you fit a GARCH model to the returns of a stock given the dates of past earnings announcements? Volatility will tend to higher than a GARCH model would predict on the announcement day.
Fortranner's user avatar
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Multistep ahead forecasts in GARCH equations

If my one step ahead forecasts from GARCH(1,1)-X are: \begin{equation} \hat{h}_{t+1} = \hat{\alpha}_0 + \hat{\alpha}_1 \hat{u}^2_t + \hat{\beta}_1 \hat{h}_t + \hat{\psi} X_t \end{equation} Where ...
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Volatility Modelling negative GJR-GARCH-X coefficient

I have estimated GARCH and GJR-GARCH with several exogenous variables. Some of the exogenous variables have negative coefficients that are statistically significant. For instance, I can write my GJR-...
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Forecasting VIX with GARCH(1,1)

Aim: Forecast VIX using GARCH(1,1) Reason: I want to be able to forecast VIX on several horizons, in order to be able to forecast the SP500 index through linear regression. Tools used: Python, ...
GusC's user avatar
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Optimal Hedging Ratio using Copula Models

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 ...
Blg Khalil's user avatar
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229 views

Can one estimate rather than forecast volatility using the GARCH model?

Can one use the GARCH model to estimate the realized variance/volatility, such as done in this paper, rather than forecast the volatility, from (high frequency) price/tick data?
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Long-run volatility forecast of a GARCH(1,1)

Can I assume that "the long run volatility forecast of a GARCH(1,1) is higher in periods of high volatility than in periods of low volatility?
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GARCH option pricing

I have been trying to implement GARCH(1,1) model for pricing call options. Suppose I have calibrated Garch(1,1) model for modelling the conditional volatility using the historical data of an equity ...
Dhruv Rathore's user avatar
1 vote
2 answers
624 views

2-day ahead prediction of value at risk with GARCH(1,1) in R

Let's say I have a 10 year dataset of Tesla (example) and I am taking the percentage change of lag 2: ...
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1 vote
1 answer
311 views

Variance of the price from returns variance

Let's say that we have the variance of the daily return at $t_0$: $$\sigma_{r_{t_0}}^2=\text{Var}[r_{t_0}]=\text{Var}[\frac{S_{t_0}-S_{t_0-1}}{S_{t_0-1}}]$$ for price process $S_t$. Is there a way to ...
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1 answer
477 views

Conditional Value at Risk using GARCH models

In this paper: https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwjSlIHYnMj1AhWqNOwKHZfHDhkQFnoECAkQAQ&url=https%3A%2F%2Fwww.mdpi.com%2F2076-3387%2F9%...
Barbab's user avatar
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How to deal with negative intercept terms on GJR-GARCH(1,1) model?

Recently, I have been studying the relationship between COVID-19 and stock returns using a GJR form of threshold ARCH model. However, I got some unusual estimation results I can't figure out whether ...
Niraj Koirala's user avatar
1 vote
1 answer
82 views

What implies "conditional heteroskedasticity" in (G)ARCH? [closed]

I have trouble to understand what implies "conditional heteroskedasticity" term in (G)ARCH models. The residual $\epsilon$ is stationary, hence homoskedastic (unconditional variance is ...
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Why in ARCH/GARCH model we don't add residual?

The most simple ARCH is given by: $$\sigma^2_t=E{\epsilon_t^2|I_{t-1}}=\alpha_0+\alpha_1\epsilon^2_{t-1}$$ Why in this model we do not have residual as well? Example: $$\sigma^2_t=E{\epsilon_t^2|I_{t-...
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how should i interpret the gjr-garch output where the gamma coefficient comes positives but insignificant?

i run gjrgarch model on russia stock market where the gamma coefficient in gjrgarch(1,1) model output is insignificant but positive. "gamma1 -0.026240 0.033785 -0.77669 0.437340" how ...
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Any reproducible r code for week day effect in garch?

I am looking for an r code to run a GARCH model with a day of week effect. Is there any package or code I can use for this?
forb's user avatar
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I am getting an $\alpha=0$ in the GARCH(1,1) model. Is this normal and how must I interpret it?

I am running a GARCH(1,1) on return data. For some data sets, I am getting an $\alpha=0$ and a $\beta$ of 0.999. Is this normal? If so how should I interpret it? Here is my code, here j are daily ...
forb's user avatar
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1 answer
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How to interpret Sign bias test in GARCH (1,1) and in GJR-GARCH?

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Younis's user avatar
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What is the relationship between the estimated GARCH(1,1) conditional volatility and the true conditional volatility

Suppose that the data has been generated by a GARCH(1,1) model, i.e. \begin{align} y_t &= h_t \epsilon_t, \; \epsilon_t \sim N(0,1) \\ h_t &= \alpha_0 + \alpha_1 \epsilon_{t-1}^2 + \...
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Examining the dependence of the fractional difference parameter in ARFIMA(0,d,0) vs bar size for Realized Volatility

Realized volatility is a long-memory process and so I fitted an ARFIMA(0,d,0) to log(RV15) where RV15 is realized volatility calculated from 15-min bars. I proceeded to examine how changing the bar ...
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How to use conditional volatility under GARCH model to forecast price?

I have come across videos on youtube about GARCH model in stimulating and forecasting stock price, however, it is programmed in R language. Is there any tutorials teach the similar as the videos shown ...
TAN YONG SHENG's user avatar
1 vote
2 answers
532 views

GARCH(1,1) parameter estimation optimization method

In estimating a GARCH(1,1) model, $$\sigma_{t+1}^2 = \omega+\alpha \epsilon_t^2+\beta\sigma_t^2$$ Usually the parameter tuple $(\omega,\alpha,\beta)$ is estimated by the quasi-maximal likelihood. ...
Hans's user avatar
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GARCH parameter estimation by linear regression?

In estimating a GARCH(1,1) model, $$\sigma_{t+1}^2 = \omega+\alpha \epsilon_t^2+\beta\sigma_t^2$$ Usually the parameter tuple $(\omega,\alpha,\beta)$ is estimated by the quasi-maximal likelihood$. Can ...
Hans's user avatar
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Realized Volatility + GARCH - can I use hourly realized volatility?

I hav minute bar FX data and I am trying to fit a realized variance GARCH model using rugarch. This normally works by providing daily returns and daily realized volatility to the model. Realized ...
s5s's user avatar
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1 answer
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GARCH calibration with overlapping time intervals

In constructing a GARCH(1,1) model over a time length $\delta$, I am considering the following procedure. The purpose of this procedure is to give more training (calibrating) samples than non-...
Hans's user avatar
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Fitting GARCH(1,1) to log returns instead of residuals - centering crucial?

For a project I need to fit a GARCH(1,1) model to the log returns of an index. When using the residuals of an ARMA or ARIMA model it is clear that the (conditional) mean is 0. When using the log ...
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How to include heteroscedasticity in copula modelling

I have a dataset of 9 variables and I want to fit a t-copula to them in order to construct a multivariate and after that resample from it. I am using Matlab. ...
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HNGARCHFIT in R (No standard deviations or P values printed)

When I estimate an HN-GARCH model using the hngarchfit() from the fOptions package in R, only the coefficient estimates are printed. There are no standard deviations or P-values printed. Does anyone ...
August's user avatar
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Perfect in-sample size for out-sampling volatility prediction (EGARCH(1,1)

I have a few questions regarding in-sample size for volatility forecasting in EGARCH(1,1). I'm currently sitting with a dataset consisting of 1387 trading days of the S&P-500 index. I would like ...
Sebastian Strauss Hansen's user avatar

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