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 AND CONDITIONAL VARIANCE

Suppose you wanted to model a $1 million dollar loss (for black swan events) on 1% of days, how do you incorporate this into GARCH? Is it through changing the alpha/beta weightings?
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278 views

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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Confidence Intervals for ARMA+GARCH forecasts

I have fitted an ARMA(1,1)+GARCH(1,1) model to my logreturns series. When it comes to my standarized error's distribution however, I have opted for a Skewed Generalized Error Distribution, because of ...
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Covariance matrix from GJR-GARCH?

I am implementing a AR(1)-GJR-GARCH(1,1) model to some asset returns, and I would need to have a covariance matrix but I struggle to see how I can compute one from the model I used? I know I can have ...
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When the two time series with different length, how could we analysis them with a bivariate GARCH model?

At this moment, i need to do the analysis of rouble/us dollars exchange rate and the stock market index in Russia, I prefer to do that in a multivariate GARCH model. However, I have a question about ...
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To calculate the Hedge Efficiency and Optimal Hedge Ratio with BEKK in R

I estimated an MGARCH-BEKK model (using the R package BEKK). (BEKK= Baba, Engle, Kraft and Kroner; see Engle and Kroner (1995)) on time series of spot and futures prices. The estimated parameters are: ...
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154 views

Multivariate Markov Regime switching GARCH

I have a regression with 4 independent variables and a dependent variable. I want to implement a Regime switching GARCH model but have been unable to find a package in R,Python or Matlab. MSGARCH ...
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79 views

ARMA+GARCH day-trading strategy

I have a question regarding this particular post on quantstart: https://www.quantstart.com/articles/ARIMA-GARCH-Trading-Strategy-on-the-SP500-Stock-Market-Index-Using-R In it, he designs a day-...
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147 views

Can you use GARCH-MIDAS for intraday data?

I'm working on a project to forecast volatility and I'm using intraday data (1 min). I want to include exogenous variables to the model that have daily frequency. I was wondering if GARCH-MIDAS can be ...
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51 views

Portofolio optimization using ARMA-GARCH-EVT-Copula

I am currently trying to do some portfolio optimization by reproducing the methodology found in Sahamkhadam, Stephan & Östermark (2018) ("Portfolio optimization based on GARCH-EVT-Copula ...
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59 views

Interpretation conditional volatility plot

I have plotten the log differences of exchange rates and in the same plot, I show the conditional volatility $\sigma_t^2$. The conditional volatility follows approximately the same path, but is much ...
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What's the interpretation behind this GARCH modeling?

I have an ARIMA model for monthly returns of the brazilian stock market index. Then I test the residuals of the model for ARCH effects. The ACF/PACF of squared residuals show that there are no ...
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What is the unconditional variance for a GARCH model?

I want to use a Matlab script to calculate Heston Nandi GARCH prices. I found an appropriate script online and it asks for the "unconditional variance" as an input. How do I calculate the appropriate ...
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GARCH volatility modeling, squared returns, and convergence

After reading some more of Volatility Trading, I decided to try to make a simple volatility model using daily log returns of an ETF I follow. It turns out "simple" is sort of relative. Unfortunately, ...
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How to forecast monthly volatility with daily gjrGarch estimates

I'm currently writing a paper and need to regress the 22 days realized volatility of the following month on its GARCH estimate and the 126days realized volatility up to t=1 The paper im referring to ...
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109 views

In-sample volatility measurement

I would like to know what is the most reasonable way to measure volatility in a sample of past observations. Aside from standard deviation, are more complex models like GARCH used for (historical) ...
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why we seldom see application of copula-garch model in macroeconomic

I find a lot of reference about copula-garch in finance market,but it seems that articles about copula-garch model in macroeconomic are rare.Is there any instrinc problem when it comes to ...
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Forecasting conditional returns in DCC-GARCH-copula approach in R

anyone who could help me interpreting and modifying this code? I have a dataset and want to reserve the last 100 returns for out-of-sample analysis. After specifying and fitting the garch-spd-copula, ...
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ARMA-GARCH estimation with EGB2 distribution

I want to estimate a ARMA-GARCH model by using the EGB2 distribution instead of the normal distribution. The model I want to estimate is: $$y_t = \mu + \phi_1 y_{t-6} + \phi_2 y_{t-8} + \theta_1 \...
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Backtesting EGARCH-NIG CVaR in R

I fitted an EGARCH model with a NIG distribution to a series of returns. Using the following link I tried got how I should calculate the CVaR of the model http://r.789695.n4.nabble.com/CVaR-with-NIG-...
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GARCH, EGARCH, GJR with different distributions

I have estimated different models based on different distributions. Since they are not nested models of each other, I can't use LR tests. But how can I compare the models? Can I do something with the ...
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Constraints by estimating GARCH, EGARCH, GJR-GARCH models

I know that by estimating an GARCH model, given by: $$\sigma_t^2 = \omega + \alpha \epsilon_{t-1}^2 + \beta \sigma_{t-1}^2,$$ $\omega, \alpha, \beta >0$ and $\alpha + \beta <1$. But what are ...
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How to account for intraday seasonality in GARCH model?

I am using a GARCH(1,1) model to estimate volatility. I am using hourly data to do this (I have hourly data for 100 trading days). Besides removing the first hour (which represents the overnight ...
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GARCH(1,1) one-step ahead volatility forecast biased, higher than Parkinson's HL volatility

I am trying to create one-step ahead forecasts for the S&P500 using a GARCH(1,1) model. I am using the rugarch package in R. As you can see, the forecasted points are consistently higher than the ...
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Fitting Copula and Simulation

I would greatly appreciate any insights into the problem described below, regarding using the data obtained from applying the functions of the rugarch package ...
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What is the best GARCH model for forecasting daily stock return and why?

If I want to forecast daily stock return let say Apple what would be the best GARCH model and why? (ARCH, GARCH-M, IGARCH, EGARCH, TARCH etc)
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rugarch and rolling estimation

I use Rugarch for a long time in order to calibrate GARCH models on FX rates time series and perform simulations. I am trying to understand the ugarchroll method. However even if I can find plenty of ...
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RUGARCH (output) and Residual Resampling using GARCH(1,1)

I try to replicate the methodology proposed by Freedman and Peters (1984a, 1984b) which was applied in the famous paper by Brock, Lakonishok and LeBaron (1992) to generate many artificial log return ...
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Fitting a forecasting S&P500 roll volatilities

I have a time series of S&P500 prices, for which I have calculated log-returns and roll-volatility. My goal is to forecast daily realized volatility and test a straddle strategy based on it (I ...
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399 views

MLE error in R: initial value in 'vmmin' is not finite

I am trying to fit an ARIMA(1,1)-GARCH(1,1) model. I changed the starting values a lot but still its returning the same error. Below is my code which contains two functions ...
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GJR-GARCH model using garchFit function

I'm trying to use the garchFit function described here in order to define a GJR-GARCH model to estimate volatility and then forecast VaR. I tried using ...
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Standard GARCH(1,1) model with external regressors

I have a queastion how does a standard GARCH(1,1) model with external regressors in mean and variance euqations look like ? I know that standard GARCH(1,1) model without external regressors has the ...
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What kind of ARMA-GARCH model is that?

My question is what kind of ARMA-GARCH model is the following equation and how to specify it in rugarch R module: $$r_{t+1}- r_t = \alpha_0 + \alpha_1r_t+\...
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241 views

Rolling forecast using GARCH model

EDIT This is not a duplicate of my original question linked, since I have since overcome that problem and have posted an answer. Since solving the previous problem, I have run into the problem ...
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270 views

Is this the correct way to forecast stock price volatility using GARCH

I am attempting to make a forecast of a stock's volatility some time into the future (say 90 days). It seems that GARCH is a traditionally used model for this. I have implemented this below using ...
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GARCH Parameters Standard Errors

How do you compute the standard errors of a GARCH model estimated with MLE ? This paper references a method by Bollerslev-Wooldridge: [...] and computed standard errors using the robust method of ...
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Log likelihood function, GARCH(1,1) with asymmetric term

I am modelling a GARCH(1,1) and a GARCH(1,1) with an asymmetric term. $$h(t)=\omega+\alpha\varepsilon(t-1)^2+\beta\sigma(t-1)^2$$ and $$h(t)=\omega+\alpha u(t-1)^2+\beta\sigma(t-1)^2 + \gamma (u(...
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Estimating an GARCH(1,1) model? Long hand method

I am really trying to invest some time to estimate a GARCH(1,1) method, I know there is many statistical packages that will do this for me (Eviews, MATLAB, R), but I am trying to do this by hand, so ...
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RiskMetrics VAR calculations and conditional distribution of sum of log returns

According to Tsay's book in Chapter 7, for the Risk Metrics model: A nice property of such a special random-walk IGARCH model is that the conditional distribution of a multiperiod return is ...
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VaR of ARCH model

Consider the following: $r_t = \theta r_{t-1}+u_t$ $u_t=\sigma_t\epsilon_t$ $\sigma^2_t=\omega+\alpha u^2_{t-1}$ $-1<\theta<1,\omega>0,\alpha \in(0,1)$ What is the 99% 2-day VaR of a ...
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Poor results forecasting stock price volatility using Python's GARCH model

As far as I understand, forecasting stock price volatility should be more achievable than forecasting absolute prices or returns. It seems as though GARCH models are the traditional and most widely ...
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ARMA+GARCH prediction with package rugarch (R)

I am analyzing FTSE 100 series, from 2007-01-01 to 2010-12-31 (university exam homework). I have to use the data 'til 2010-11-30 as sample, and the remaining (23) observations as in-sample forecast (...
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284 views

Fractionally Integrated GARCH

I am currently working on a project to compare different GARCH(1,1) models on a financial data set. I use the rugarch package in R, and everthing seemed fine at first. However, now that I have started ...
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1answer
13k views

How to calculate the conditional variance of a time series?

I am reading a paper where the term conditional variance is mentioned, but I am not really sure what is meant by this and how this can be calculated: Fig. 2 shows the conditional variances of the ...
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51 views

When modelling ARCH/GARCH effects, do we use excess returns?

When modelling ARCH/GARCH effects, do we use excess returns? Is it common in the literature to use excess returns when modelling volatility as opposed to raw return data?
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GARCH(1,1) and Value at Risk: Rolling window or non-overlapping samples

Currently studying on financial risk management. I want to test different methods of VaR estimation. I want to model volatility using a GARCH(1,1) model. My question is what should the size of the ...
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Time series analysis for stock prices

I am using GARCH model to simulate price of an index for 7 years. For input I am using difference of Log of prices (log of return). GARCH(1,1) has the lowest AIC, and I found parameters for the ...
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Predicting stock returns with GARCH in Python

I have seen this post: Correctly applying GARCH in Python which shows how to correctly apply GARCH models in Python using the arch library. Now I am wondering how I ...
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Volatility clustering and Behavioral Finance, possible explanation

Currently studying about time series modelling of financial data and faced the known GARCH$(p,q)$ model for modelling volatility. We observe that big changes are followed by large changes and vice ...
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204 views

Kurtosis in GARCH

In a GARCH(1,1) model $$ x_t = \sigma_tz_t$$ $$\sigma_{t+1}^2=a_0 + a_1x_t^2 + b_1\sigma_t^2$$ the kurtosis (when it exists) can be shown to be equal to $$ \kappa_x = \kappa_z \frac{1-(a_1+b_1)^2}...