If an auto regressive moving average model (ARMA model) is assumed for the error variance, the model is a generalized auto regressive conditional heteroskedasticity (GARCH) .

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GARCH model and prediction

I have a question about the prediction of volatility and returns of a time series. Basically it is a question about prediction in the ...
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How would you correct a GARCH model to deal with non mean reverting volatility?

I am currently attempting to model and forecast volatility of bitcoin but have not been able to find a GARCH model that fits the data appropriately. I've used tick data sampled at 1 hour intervals ...
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What impact does arbitrage have on realised volatility estimates?

Doing some research modeling/estimating volatility in the bitcoin market. There is quite a bit of scope for arbitrage within crypto-currency markets. Wonder if this has any impact on my volatility ...
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Cross validation of a garch model

Suppose I divide a time series into 10 sequential time windows, where each window contains 1000 data points. I want to do test 5 different garch models using cross validation. So for each model, I ...
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Optimal lag length selection criterion in GARCH(p,q) model using MATLAB

As assessed by the title, I'm trying to estimate a GARCH(p,q) model to forecast stock market volatility and, in order to be able to do that, I've to identify the optimal number of lags, p and q, to ...
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How come the existence of ARCH effect is not a violation of Random Walk Hypothesis 3?

An ARCH (autoregressive conditional heteroscedastic) (1) model is: $r_t=\mu +a_t$, where $a_t=$return residual, and $\mu$ is the drift of the stock return $a_t=\sigma_t\epsilon_t$, where ...
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What return equation is Engle referring to in his Nobel lecture?

Engle comments in "Risk and Volatility: Econometric models and Financial Practice" that If the price of risk were constant over time, then rising conditional variances would translate linearly ...
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Time-Varying Volatility and Conditional Likelihood

Engle's comment in his seminal paper "Risk and Volatility: Econometric models and Financial Practice" mentions that I had recently worked extensively with the Kalman Filter and knew that a ...
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Can we model components in a set of multivariate multi-period time-series data?

There are N data sets in periods occurring weekly/monthly, across a 10-year historical timeline. In each period, five dates are observed (labelled a to e), where a denotes the day the period ...
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Evaluation volatility with Garch model

I want to forecast the volatility (with Garch) of a canadian stock in 5 months with daily returns. How many data do I have to collect ? Thanks.
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How to trade volatility?

I am analyzing the volatility of financial stock returns and let's say I have a pretty good model to forecast tomorrows volatility of the stock returns. So let's say for simplicity reasons I have a ...
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Fitting Student t-distributions to log-returns

It seems that some tail-risk centric groups are bent on using Paretian and t-distributions to account for tail risk when fitting log-returns. It has been observed, however, that with and without ...
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Garch modelling on Stata

I would like to ask "how to do GARCH modelling on stata". Basically I want to estimate stock market volatility using daily data. I have one variable as return series, $r_t=\ln(\frac{P_t}{P_{t-1}})$. ...
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GARCH(1,1) prediction in R - Basic Questions

Background to question: Hi, I was trying to fit a GARCH(1,1) model to the variance of log returns of a series, and ARMA(0,0) for the mean. I was using the fGarch package to do this. The aim of the ...
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Algorithm to fit AR(1)/GARCH(1,1) model of log-returns

I am fitting numerically an AR(1)/GARCH(1,1) process to index and stock log-returns, $r_t=\log(P_t/P_{t-1})$, where $P_t$ is the price at time $t$, and thus far am not clear on where the observed log ...
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How to compare volatility models?

What are the most popular ways to compare volatility models? Suppose I wanted to compare the forecasting accuracy of a GARCH(1,1) model with the historic 30 day volatility. What method should I use? ...
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Fitting a GARCH BEKK model

I am trying to find whether there is significant volatility transmission between two price series (t=1000). A literature review learned me that the GARCH BEKK model is suitable for this. The SAS ...
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790 views

FIGARCH estimation in R

I am trying to estimate a FIGARCH(1,1) model in R for Value-at-Risk purposes. As I understand it, the rugarch package does not support FIGARCH or FIEGARCH. To that end, I used the garchOxFit function ...
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How GARCH/ARCH models are useful to check the volatility?

Below a R code wrote by the moderator @richardh (whom I want to thank again) about ARCH/GARCH models. ...
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garchOxFit in R

Could someone please help me with trying to get the Ox interface to work in R. I followed the steps outlined in this paper (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1752095), but I get the ...
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rugarch: Joint estimation leads to different results

I want to fit an ARMA-GARCH model to my data using rugarch package in R. First of all, I look at the acf and pacf: ...
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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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Problems with dealing with GARCH models and intra-day data

Short question would be "Which type of model from GARCH family is most suitable for modeling 5-minute data returns ?" but I've added some story to it. Long time ago I was preparing my thesis, one ...
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Volatility Return Distribution/Garch Modeling

For simplicity sake, if stock returns are normally distrusted, would that imply that second moment, variance/volatility, is chi-squared distrusted? If so wouldn't that imply the statistics(employed to ...
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So many volatility models. Any comparisons of them?

Are there any papers that make an explicit contrast/comparison of the following (or other) vol models in terms of the suitability for addressing some empirical problem? Wavelet multiresolution ...
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Error term/Innovation process in ARCH/GARCH processes?

I am wondering about the distribution of the error term/innovation process in a ARCH/GARCH process and its implementation, I am not sure about some points. The basic assumption is ...
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Forecasting using rugarch package

I want to do one step ahead in-sample forecasts. My data can be found here. This is just a data frame with the date as the rownames. I specify my model and do the fit and show the plots with ...
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Improving GARCH modeling approach

Modeling Exchange Rate Using GARCH Let's consider the following exchange rate : USD/JPY For each sequence, we consider changes in the daily difference between the highest price and the open price of ...
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Why do we use GARCH(1,1) to predict volatility?

What makes GARCH(1,1) so prevalent in modeling especially in academia? What does this model has that is significantly better than the others?
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Conditional or unconditional volatility?

I am reading a paper (reference below) that states "The conditional volatility for each underlying security (or for a market index) can be estimated using the standard deviation of the stock’s ...
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552 views

Markov-Switching E-GARCH with R

I am looking for a R library for modeling a Markov-Switching E-GARCH process. In other questions at StackExchange related to GARCH models, the package rugarch is often mentionned. Do you recommend it ...
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387 views

Volatility models using Rugarch

I have estimated sGARCH, EGARCH and TGARCH, which some for particular models are significant. For others, the alpha remain insignificant using various innovations such as the skewed variants of the ...
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Stock Price Behavior and GARCH

In my (limited) understanding, the behavior of a stock price can be modeled using Geometric Brownian Motion (GBM). According to the Hull book I'm currently reading, the discrete-time version of this ...
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2answers
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Should I use GARCH volatility or standard deviation in cross-sectional regression?

I want to do a cross-sectional study where the historical, medium-long run volatility of some return series (call it $R_t$) is included as a regressor. Which of the following two estimates of ...
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Backtesting VaR model violation independence

I am interested in hearing about the practitioner state of the art for testing the time independence of a VaR model (i.e. that VaR violations are independent in time). There are a number of tests in ...
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How do I evaluate the suitability of a GARCH model?

Suppose I downloaded the closing price of a company, say Google or whatever, I want to use GARCH model to model and forecast the volatility of the return. To simplify, I only have two questions. ...
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Rolling window Kendall's tau against APARCH(1,1) correlation

Assume you want to forecast the correlation matrix of a stocks' basket (say 15 ~ 20 stocks from different sectors); assume you need to forecast at $T$ days because you will use the forecast ouput with ...
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Does the correlation amongst stocks rise when stock values decline?

Is there any research on whether the correlations among stocks rise when stock indices decline? Which model could account and test for that effect ? Maybe GARCH-BEKK, or some models using copulas?
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How do I model GARCH(1,1) volatility for historical indexes in Matlab?

I'm currently working with historical index data from Yahoo Finance and would like to plot the GARCH(1,1) volatility of these indexes. I'm working with the Datafeed and Finance Tollboxes in Matlab ...
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Is a linear combination of GARCH processes also a GARCH process?

If two time series follow a GARCH process, and a third is a linear combination of them, is the third also GARCH process?
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GJR-GARCH Model In R

Any idea how to estimate GJR-GARCH models in R? Is there any particular library like fGarch that supports such models?