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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Implementing GARCH(2,2) QMLE: where does the data (squared returns) come into play? [closed]
I am trying to implement a QMLE estimation of GARCH(2,2) model as a side project.
We can represent GARCH(2,2) as follows:
\begin{aligned}
r_{t} &= \mu_{t} + \epsilon_{t}, \\
\mu_{t} &= 0, \\
\...
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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.
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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, ...
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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 ...
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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 ...
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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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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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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%...
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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 ...
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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?
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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 ...
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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 ...
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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$$
Usually the parameter tuple $(\omega,\alpha,\beta)$ is estimated by the quasi-maximal likelihood. ...
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GARCH parameter estimation by linear regression?
In estimating a GARCH(1,1) model,
$$\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 ...
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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 ...
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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-...
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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 ...
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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 ...
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Realized Variance (realized volatility)
I'm confused about realized variance. I roughly know the theory around Ito Calculus and quadratic variation and integrated volatility so I understand what realized variance measures (even though as ...
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How to estimate lambda from NAGARCH submodel in R
I am trying to estimate the model="fGARCH", submodel="NAGARCH" from the rugarch package in R.
However, when I am estimating the parameters, only omega, alpha, beta and gamma are ...
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GARCH Option Pricing in R
I am trying to code a GARCH option pricing model in R. I am still new to R so this does seem a bit complicated.
I want to estimate an asymmetric GARCH model as well as an EGARCH model. This I have ...
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Empircal data analysis delta hedge error of Black-Scholes by Mark Davis
Regarding Mark Davis derivation of the delta-hedging error occuring in the black-scholes as a result of difference in realized volatility and implied volatily. The formula reads as follows:
$$ Z_t = \...
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Deciding (p,q) in garch and model test on empirical data
I'm currently working on a dataset containing data from the 29 January till the 29 July 2009. In the dataset I have prices of the S&P 500 index for all days. Furthermore, I have the implied ...
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HNGARCH Option Pricing in R (How to loop)
I am having difficulties when using the HNGOption program in R.
The program will only run for 1 specific option price, meaning that I would have to manually insert strike price etc. and this would ...
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Calculating E^2[σ^2] where σ is a GARCH(1,1) Proces
Given that α =0,113079 β = 0,873884 ω = 0,0000081
Need the calculate a call price using garch volatility I alsa calculated the kurtosis = 235 enter image description here:
https://www.researchgate.net/...
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EGARCH(1,1) mean
I'm trying to model an EGARCH(1,1). However, I dont understand why the mean from the general to (1,1) becomes $\sqrt{(\frac{2}{\pi})}$.
The following I am refering to is:
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Heston Nandi Garch Implementation Problem for Python
I have a coded my own Garch class in order to implement the Heston-Nandi Garch model.
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Skewness and Kurtosis in GARCH vs Heston
GARCH(1,1)
In discrete time, we can model returns as follows
\begin{align}
r_t &= \mu + \sigma_t\epsilon_t\\
\sigma_t^2 &= \omega + \alpha \epsilon_{t-1}^2 + \beta\sigma_{t-1}^2
\end{align}
...
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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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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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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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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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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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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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