Quasi Maximum Likelihood

My purpose is to recognize the volatility clustering property without statistical tests such as The Lagrange Multiplier (LM) test.

HI,

In a DCC-GARCH(1,1) model (dependent variable is first difference of logarithm of the series) based on monthly data,

1. How do you interpret unconditional and conditional correlation in a DCC-GARCH model?

2. Is it possible to get a correlation matrix (like the unconditional correlation matrix, without correlation for each month and pair) for conditional correlations? or we just need to present the data using a conditional variance graph for each pair?

Much appreciated your comments/advice on this.

Kind regards

Thushara

I’m working on a time series project of sales data for my work. My advisor guided me to use the Arch Garch method. How do I first make sure my data is suitable for this method? What tests to run and what do I look for?

The data I use is monthly European CPI transformed into annual inflation (calculated as log(CPIt) - log(CPIt-12). The dependent variable in ARMA is the first difference of that (so there is no unit-root and the time series is stationary). I have found ARCH effects in the ARMA model, however the coefficients of the GARCH model are statistically insignificant (only the ARCH component and constant are). I have tried multiple error distributions (student t and GED). The analysis is performed in EViews.

Dear experts,

I am working on stock market time series. I have applied EGARCH model to analyse volatility dynamics. I have queries on its output as follows-

1.) What to do when mean equation coefficients in EGARCH output are coming with insignificant constant and signicant AR term coefficient? 

2.) what actually arch term, Garch term, gamma term means? whether we can say arch (alpha) term explains volatility clustering, GARCH (beta) term explains persistense, gamma(Leverage) term explains leverage effect.

3.) How to check persistence in EGARCH with only beta value or with sum of arch and garch term both? what means if arch and garch term sum exceeds one in EGARCH output? model estimation is wrong or can conclude with explosive volatility comments without making model estimation wrong. because i applied  AR 1,2,3,4,5 - MA 1,2,3,4,5 - ARMA 1,1 - 1,2 - 2,2 - 2,1 - 3,1 - 1,3 - 1,4 - 3,3  etc. but even after getting ARCH effect in return series EGARCH output of mean equation is coming out with insignificant constant and significant and insignicant somewhere these AR, MA, ARMA terms.

4.) In variance equation output alpha is coming with negative but signicant value at 5% what does it means?

Please guide me......

Hello , i'm a phd student , i work on volatility modeling ,i tried GARCH, EGARCH , IGARCH, TGARCH and CGARCH to model my time series , but after applying ARCH-LM test, i still find the presence of ARCH effect on the residuals, can someone propose to me other models that i could try ? or give me some advises please , and thanks

I am using rugarch package in RStudio for running EGARCH model on S&P500 (01/01/2008 - 10/03/2017). As per theory the financial time series normally have a positive ARCH effect and negative Leverage effect since the bad news have more effect volatility. I am getting a negative ARCH effect and positive Leverage effect. I have been trying to find the reason for this but haven't succeeded. Can someone please advise? (Results attached)

Seeking studies involving applications of the GARCH-MIDAS model.