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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Criticise GARCH relative to Realized Volatility
I would like to have your opinion about a simple question.
While GARCH would be useful to calculate the conditional volatility, and the RV being in some sense the "historical" volatility, what would ...
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Measure how different forecasted volatility is from realized volatility
Hi Quantitative Finance Stack Exchange,
I'm looking for an opinion on a simple question. Suppose I use a Garch(1,1) model to make a volatility forecast.
At time $t$, I have realized volatility $\...
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Determine GARCH(1,1) from a mean reverting time series recursion
Let $(v_t)$ be a discrete time series of variance obeying a mean-reverting variance process $v_t$, which is actually the discrete version of the Heston model in finance.
\begin{align}
x_t &= \sqrt{...
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How to add controls (regressors) to GARCH model in R?
How can I estimate a GARCH(1,1) model with control variables like this:
$$Y_t=a_0+a_1X_t+e_t$$
where$$ e_t\sim N(0,h_t)$$
$$h_t=b_0+b_1e_{t-1}^2+b_3h_{t-1}+b_3Z_t$$
I've checked some packages but can'...
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Typical SPX variance GARCH(1,1) coefficients
Can someone provide a typical numerical values of GARCH(1,1) coefficients $(\omega,\alpha,\beta)$ for estimating SPX index variance? I will appreciate it if some references could be provided.
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Garch(1,1) in R [closed]
I'm evaluating the impact of two variables on stock returns. For this I am using a Garch(1,1)-model in RStudio. This is the result I am getting. Why is the garch model not a valid choice? The external ...
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ARMA-GARCH Forecasting [closed]
I want to forecast a differenced time series of an Index using the combined ARMA-GARCH model (because I want to forecast the mean and not the variance). My model is a ARMA(2,2)-GARCH(1,1) model. So ...
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VaR : Student-t GARCH
I have a question on the VaR estimation via the student t GARCH model.
Under this framework, the one day ahead VaR estimate is calculated by the following formula:
$$VaR_{p}=\mu_{t+1}+\sigma_{t+1}\...
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Deduce GARCH(1,1) to the stochastic variance model
Here is the proof that the limit of GARCH(1,1)
$$\sigma_n^2 = \gamma V_L + \alpha u_{n-1}^2 + \beta\sigma_{n-1}^2$$
is equivalent to stochastic process of variance
$$d V = \alpha(V_L - V) dt + \xi VdW....
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Unconditional variance of an E-GARCH model
I am attempting to calculate the unconditional variance of an E-GARCH model:
$$\log(h_{t+1}) = \beta_{0} + \beta_{1}\log(h_{t}) + \beta_{2}\left[|\varepsilon_{t} - \lambda| + \gamma(\varepsilon_{t} - \...
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VaR estimation when returns are not independent, e.g. ARCH
Time series of returns, $r_t$, in finance are often modeled with some type of conditional heteroskedasticity model, e.g. ARCH(1):
$$r_t = \sigma_t z_t$$
$$\sigma_t^2 = a_0 +a_1 r_{t-1}^2$$
where, ...
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GARCH Option Pricing Model (Duan 1995)
I am trying to replicate Duan's results from his 1995 Paper, "The GARCH Option Pricing Model". I have written this code in Python myself, and using his parameters I consistently seem to obtain results ...
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I am trying to fit an GARCH(p,q) model to FX volatility. Should I be interested in the t-value of GARCH parameters?
So what I am trying to do is to model the volatility for different currencies by fitting a GARCH(p, q) model. I am selecting the values of (p, q) by iteratively going through p & q such that max(p,...
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Can I forecast stock returns using GARCH?
I know this is a rookie question, but I have seen some comments about using GARCH to forecast stock returns.
Is it something people do? Wasn't GARCH just for volatility?
Also, can you suggest any (...
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Information criteria via different GARCH models
I have a question about comparison of different GARCH models via information criteria.
I use rugarch package. So, let's have 3 model types: "sGARCH", "eGARCH", "gjrGARCH". I fit all 3 type models for ...
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What are the significant implications of the long-run average variance rate and why Engle won the Nobel Prize for ARCH model development?
In a ARCH(m) model we have
$$
\sigma_n^2=\sum_{i=1}^{m} \alpha_i u_{n-i}^2
$$
where $u_i$ is defined as the continuously compounded return during day $i$ (between the end of day $i-1$ and the end of ...
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Ljung_Box Statistic of R and R^2 values in Return analysis
I have found a result that I find truly puzzling. Here is an extract from a GARCH-Analysis I have performed:
Test______________Statistic_______p-Value
Ljung-Box Test_____R Q(10)_____0.4047773
...
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R fGARCH fitted Values
I am using the fGARCh package in R to analyze volatility of stock returns. More precisely I am using a garch(1, 1) fit. The code ...
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simulating from GARCH model with copula innovations
I have a GARCH model fitted on stock returns as:
...
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How can I compare 30 day implied volatility forecasts with GARCH forecasts?
I'm trying to understand whether there is a good way to compare forecasts for volatility from different sources i.e., implied volatility and GARCH. I'll outline a few statements that I believe and if ...
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EGARCH fitting in R
I am using the fGarch package in R to analyze stock volatility. To do this I am using the garchFit formula on my time series. ...
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Under which circumstances can conditional distribution of asset returns be less Gaussian than the unconditional distribution?
I looked into the unconditional and the conditional distribution of a return series, where the unconditional distribution is simply the marginal distribution of the returns, and the conditional ...
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Investigating a question: "Does commodity price volatility scale with price level?"
I'm trying to answer a simply posed question using a GARCH model: can we expect larger price shocks in a commodity when it's price is higher? (i.e., may we expect larger price shocks at \$100 per ...
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VAR-aDCC full ARCH and GARCH parameter matrices in R
I am working with the rmgarch package in R and I estimated a VAR-aDCC model. Is there any way to extract the extended version of estimates (allowing for volatility ...
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GARCH mean and volatility spillover R commands needed
I analyzed an MA(1)-GARCH(1,1) model in R, and now I want to test the conditional mean and volatility spillover effect between the two time series (exchange rates) (based on Hamao et al., 1990). ...
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What are the current gold standards for volatility prediction error?
I'm working on volatility forecasting models for equities and currencies. I am using daily data and am interested in producing forecasts for the next n days. To ...
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Joint estimation of GARCH models with ARMA terms in the conditional mean: a necessity?
Supposing I am using the following models to forecast conditional volatility of index returns, whereby In-sample data is 1996-2007 and out of sample data is 2007-2012, using GARCH type models.
I have ...
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Questions on the concept of GARCH model [closed]
As we all know, the GARCH model is stated as
$\epsilon_t = \sigma_tz_t$
$\sigma_t^2 = w + \sum^q_{i=1}\alpha_i\epsilon_{t-i}^2 + \sum^q_{i=1}\beta_i\sigma_{t-i}^2$
In application, the estimate $\hat{\...
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Most GUI user friendly Time series Econometrics software for modelling and Forecasting GARCH models [closed]
I think the question is simple enough. I have been using Eviews, but it is unable to do recursive one step ahead forecasting directly and requires me to use coding, which I'm not very good at.
I need ...
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Correct procedure for modelling GARCH for forecasting volatility of stock Index returns
I will be using Eviews and am looking to forecast volatility of stock index returns using ARCH/GARCH models. I've generated the logarithmic returns and done the unit root tests. I then proceeded to ...
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Heston & Nandi GARCH model, parameters estimation from option data
I wonder if anybody has code for the HN-GARCH model where the parameters is NOT estimated with maximum likelihood and instead estimated by looking at the option data where an loss function is chosen ...
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VARMA GARCH modelling in R
I want to simulate a VARMA-GARCH process in R. Unfortunately, I found no package to help me with that.
I tried modelling the MGARCH part on itw own and combine it with the VARMA simulation using MTS ...
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R GARCH simulation providing whole components (y, cond.vol. etc.)
fGarch::garchSim provides only realizations of y variable, is there way to obtain also conditional variance and time series of ...
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Rotations and Shifts in the f-GARCH News Impact Curve
I re-post my question from the Cross Validated section as requested by another user.
I am using the beautiful "rugarch" package and presently have an issue concerning the interpretation of two ...
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Forecasting volatility with rugarch and Covariance Matrix
I am trying to do a financial time series forecast in order to build a portfolio. I already have some code running rugarch library and I am not sure if I am forecasting correctly, after that I would ...
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Fitting GARCH(1,1) in Python for moderately large data sets
I am using the arch package in python to fit a GARCH(1,1) to fit daily S&P 500 returns from 1990 to 2017 (about 6800 data points). The code I am using is as follows:
...
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Suggestions for a Master thesis in option pricing models
I am willing to do my Master Thesis about option pricing.
Do you have any suggestions? I would like it to be something simple, like comparing methods, e.g. compare ARCH and GARCH approaches for ...
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What is the difference between conditional volatility and realized volatility?
I am working on conditional volatility and realized volatility but the difference between these two measures is not clear to me.
Can anybody explain how these two volatilities are related? Does the ...
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Modeling tail data using Generalized Pareto distribution
I just estimated a ARMA(1,1)+GARCH(1,1)+Threshold order(1) equation for time series of stock prices.
Now I'm going to estimate the residuals' marginal ...
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Negative constant in GARCHX model
I am fitting the following ARX(1,1)-GARCHX(1,1,1):
\begin{align*}
y_t&=c+a_1y_{t-1}+\gamma_1x_t+\varepsilon_t\\
h_t&=\delta+\omega_1h_{t-1}+\theta_1\varepsilon_{t-1}^2+\pi_1x_{1,t}
\end{align*...
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How is a GARCH model readily complementary to a forecasting model?
Hi Quantitative Finance Stack Exchange,
It's my first go at GARCH models so give me a chance with my phrasing. I'm looking for an answer to a general question.
First, I understand that you can have ...
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Are GARCH models dependent on the returns forecasting model?
Hi Quantitative Fiance Stack Exchange,
It's my first go at GARCH models so please give me a chance with my phrasing.
I understand that GARCH models are used to forecast volatility. The GARCH(1,1) ...
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Why are my GARCH forecasts biased?
I've run an ARMA(1, 1)-GARCH(1, 1) model with normal density on log returns for twelve stocks. I computed the one-step-ahead out of sample forecast for daily volatility on a rolling windows for 500 ...
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MSGARCH package on R
I am using the MSGARCH package on R to fit a Markov switching GARCH model. I fit the GARCH model using fit.MLE (so standard Maximum Likelihood), using three regimes. The parameters are estimated and ...
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Using GO GARCH to optimize a yearly-rebalanced portfolio based on daily data
Is it reliable to optimize portfolio weights on a yearly-rebalanced portfolio based on the Generalized Orthogonal GARCH (GO-Garch) covariance, coskewness, and cokurtosis matrices with the rmgarch R-...
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EWMA in python using the arch 3.2 package and pandas
I have a hard time figuring out whether my EWMA calculation of variance is correct when using the python package ARCH 3.2.
Currently, I am doing the following:
...
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Price return or total return for GARCH models
Is there a problem in modeling total return rather than price return when using GARCH models?
My line of thinking is that total return includes dividends, which is only a "pseudo-random variable" in ...
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References for biased forecasts from EGARCH
A few months ago I've read somewhere that although the exponential GARCH model may lead to higher BIC values in comparison to other extensions of the GARCH family (GARCH, GJR-GARCH, TGARCH, ...), ...
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Transformation of GARCH Equation to multiple-day Forecast Equation
I want to understand the procedure of how to predict with the GARCH Modell. Therefore it is said that a one day ahead forecast is easy due to the fact that the GARCH equation can produce this. ...
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LSE GARCH Modells
currently I am working with GARCH Modells. And it came to my attention that for the parameter estimation Maximum Likelihood approaches are commonly used. However I was wondering why Least Squared ...
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