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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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GARCH model within a system of simultaneous equations

This is a system of simultaneous equations. The first equations is a GARCH(1,1) model with a exogenous variable. The dependent variable (x) from the fourth equation is exogenous independent variable ...
ZebrasInPjs's user avatar
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Fitting a Copula with GARCH volatility to stock returns

I have the log-returns $r_{n,t}$ for 3 stocks, $n=1,2,3$, and $t=1,..,T=365$ days, and I want to model the expected shortfall given arbitrary positions on those stocks. I calibrate the GARCH model ...
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Reducing possible models count for calibration in ARFIMA-GARCH models

I have the question connected with ARFIMA-GARCH models. I have a time series for which I want to calibrate best model (p,q)-(P, Q) (via BIC) with $ p,q <= 4, P,Q <=2$. GARCH part can be "...
Dmitriy's user avatar
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eGARCH(1,1) model evaluation (R). How to assess model integrity?

I am using GARCH modelling for my bachelor thesis in Economics. I am entirely new to the concept, and have only been looking into these kind of models for about a week now. I am trying to do a ...
Sam's user avatar
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Profitability on Value at Risk forecasting

I'm conducting a research related to Value at Risk forecasting using volatility models like GARCH and others. My predictions are turning out quite well with some models. Is there a way to capitalize ...
finance_bro's user avatar
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State Space model with heteroskedastic disturbance - approximation of error term

We have a state-space model with a heteroskedastic disturbance term modelled according to some time-varying process (e.g. ARCH, GARCH etc.). The disturbance term is modelled according to e.g. $h_{t+1}=...
Energy Media's user avatar
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Calibration of Heston-Nandi GARCH Model Using Historical Data: Risk-Neutral vs. Physical Measure

I am currently working on calibrating the Heston-Nandi GARCH model using historical asset return data and am faced with a decision on whether to use the risk-neutral or physical measure for this ...
Quant's user avatar
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GARCH for Mean Variance Optimization

I am currently trying to carry out a mean variance optimisation, with the implementation of GARCH. I'm not sure if this is going to make complete sense as my understanding of GARCH is limited. In the ...
FraserM2000's user avatar
1 vote
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ARCH-Vasicek model solution

I understand how we can obtain the solution of Vasicek model $dr_t=\alpha(\mu-r_t)dt+\sigma dW_t$: $$ r_t=r_0e^{-\alpha t}+\mu(1-e^{-\alpha t})+\sigma\int_0^te^{-\alpha(t-s)dW_{s}} $$ This easily ...
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Getting standardized residuals from MSGARCH in R

After estimation by the MSGARCH package in R, I couldn't find a way to extract residuals from the estimation results. Anyone know how to extract the residuals from MSGARCH package in R? Thanks!
Frank Cheng's user avatar
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GARCH before and after a shock. How to test if volatilities are different?

I have an intraday dataset with minute returns for a bond. At a specific point in time, say 10:30, there is an external shock (in my case an auction where that bond is traded). I want to know whether ...
Avocado's user avatar
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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 ...
Aaron 's user avatar
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1 answer
59 views

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 ...
nadeem's user avatar
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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=...
Isaac E'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
2 votes
1 answer
156 views

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
1 vote
1 answer
101 views

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 ...
Alex's user avatar
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280 views

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, ...
Restu's user avatar
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2 votes
1 answer
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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 ...
Barbab's user avatar
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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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1 answer
105 views

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
1 vote
3 answers
1k views

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
4 votes
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135 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 ...
StochasticNewby's user avatar
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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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195 views

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 ...
probablysid's user avatar
2 votes
1 answer
174 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
0 answers
114 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 ...
StochasticNewby's user avatar
1 vote
1 answer
187 views

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
395 views

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 ...
deblue's user avatar
  • 281
1 vote
2 answers
122 views

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
1 vote
1 answer
819 views

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 ...
Moataz's user avatar
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3 votes
1 answer
345 views

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-...
Moataz's user avatar
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0 answers
392 views

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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2 votes
1 answer
232 views

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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1 answer
235 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?
Hans's user avatar
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179 views

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?
user62408's user avatar
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0 answers
219 views

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
677 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: ...
user avatar
1 vote
1 answer
339 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 ...
Michał Dąbrowski's user avatar
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1 answer
531 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
  • 171
2 votes
1 answer
468 views

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 ...
Sane's user avatar
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0 votes
1 answer
123 views

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-...
Sane's user avatar
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0 answers
268 views

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 ...
Younis's user avatar
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138 views

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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2 votes
1 answer
2k views

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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3 votes
0 answers
88 views

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 + \...
Stéphane's user avatar
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2 votes
0 answers
97 views

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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