# 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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### 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 ...
99 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 ...
1 vote
84 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 ...
103 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 ... 94 views

### 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, ...
58 views

### 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 ...
47 views

### 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 ...
1 vote
63 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 : ...
1 vote
497 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 ...
114 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 ...
33 views

### 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 ...
103 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 ...
53 views

### 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 ...
163 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: ...
102 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 ...
1 vote
137 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 ...
262 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 ...
1 vote
105 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.
1 vote
520 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 ...
241 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-...
342 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, ...
177 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 ...
216 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?
151 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?
192 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 ...
1 vote
558 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: ... 1 vote
289 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 ...
432 views

406 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 ...
1 vote
I have trouble to understand what implies "conditional heteroskedasticity" term in (G)ARCH models. The residual $\epsilon$ is stationary, hence homoskedastic (unconditional variance is ...