(EViews10): How to Estimate Exponential GARCH Models #garchm #tgarch #egarch #igarch #cgarch #arch

Please pardon my gaffes. Referring to “ARCH” as “GARCH” in some cases (lol).
This video simplifies the understanding of the generalised autoregressive conditional heteroscedasticity (GARCH) using an approach that beginners can grasp. The GARCH Modeling series has 9 collections on the following topics: (1) ARCH versus GARCH (Background), (2) Basics of GARCH Modeling, (3) how to estimate a simple GARCH model, (4) ARCH versus GARCH (Estimations), (5) how to estimate GARCH-in-Mean models, (6) how to estimate Threshold GARCH (GJR GARCH) models, (7) how to estimate Exponential GARCH models, (8) GARCH models and diagnostics and (9) how to forecast GARCH volatility. So, what is GARCH? Generalised autoregressive indicates that heteroscedasticity observed over different time periods may be autocorrelated; conditional informs that variance is based on past errors; heteroscedasticity implies the series displays unequal variance. Popularised by Tim Bollerslev in 1986.

Why use GARCH: Models the attitude of investors not only towards expected returns but also towards risk (uncertainty); Relates to economic forecasting and measuring volatility; Techniques  GARCH, GARCH-M, TGARCH, EGARCH, PGARCH, CGARCH, IGARCH and several other extensions; Concerned with modeling the volatility of the variance; Conditional and time-varying variance; Deals with stationary (time-invariant mean) and nonstationary (time-varying mean) variables; Nonstationary  varying mean; Heteroscedastic  varying variance; Concerns financial and macroeconomic time series; Duration  daily, weekly, monthly, quarterly (high frequency data); Financial/economic series  stock prices, oil prices, bond prices, inflation rates, exchange rates, interest rates, GDP, unemployment rates etc. What is conditional variance? The assumption of homoscedasticity (constant variance) is very limiting, hence preferable to examine patterns that allow the variance to depend (conditional) on its history. Volatility Clustering: Periods when large changes are followed by further large changes and periods when small changes are followed by further small changes. Shows wild and calm periods.

Some Lessons Learnt: The time-varying variance is modeled by the procedure called autoregressive conditional heteroscedasticity (ARCH); GARCH simply conveys that the series in question has a time-varying variance (heteroscedasticity) that depends on (conditional on) lagged effects (autocorrelation); GARCH model is intuitively appealing because it explains volatility as a function of the errors. These errors are called “shocks” or “news” by financial analysts. They represent the unexpected!; The larger the shocks, the greater the volatility in the series; Since variance is often used to measure volatility, and volatility is a key element in asset pricing theories, GARCH models have become important in empirical finance; Most financial time series like stock prices, exchange rates, oil prices etc. exhibit random walks in their level form, that is, nonstationary (time-varying means)

Need the data used in the video? Click on these links:

References and Readings: Asteriou and Hall (2016) Applied Econometrics, 3ed; Hill, Griffiths and Lim (2008) Principles of Econometrics, 3ed; Roman Kozhan (2010) Financial Econometrics with EViews; Gujarati and Porter (2009) Basic Econometrics, International Edition; R. Engle, “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation,” Econometrica, vol. 50. No. 1, 1982, pp. 987–1007; A. Bera and M. Higgins, “ARCH Models: Properties, Estimation and Testing,” Journal of Economic Surveys, vol. 7, 1993, pp. 305–366; Bollerslev (1986); Amadeus Wennström (2014) Volatility Forecasting Performance: Evaluation of GARCH type volatility models on Nordic equity indices; Bollerslev, T (1986)“Generalised Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation,” Journal of Econometrica, vol. 31, pp. 307–327; Tsay, R.S. (2002) Analysis of Financial Time Series, John Wiley & Sons, Inc., New York.

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[CrunchEconometrix]

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[cassidyboye]

Thank you, my sister. I viewed your videos from the superior institute for labor and business sciences in Lisbon, Portugal, and western Europe, where I am pursuing my Ph.D. in Economics.

[annabelg]

Thank you so much for all of these videos! They have completely transformed my econometric understanding and have given me confidence for my exams :)

[JaphethJev]

Thank you so much, Dr. Your videos are helping me to write my dissertation well.

[imdatkoksal]

I am from TURKEY and thank you very much for your informative videos....

[adorachukwu]

thank you very much for this impacting lesson

[neerugupta]

Thank you mam for sharing knowledge. I learned the application of Garch family model from your videos only. I have a question from your EGARCH video i.e how you calculated the value 64. 28%. the exponential value of -0.044197 (e^-0.044197=.9567) is 95.67%. my other question is can I calculate the % impact of negative and positive news on volatility. please tell me how I can do it. Thanks & Regards

[arafarizkasyaputra]

Thank you for the explanation ma'am. But how about Multivariate Garch? Hopefully, you can also make about Multivariate Garch as well. Thank you

[robertndaiga]

Thanks Dr. for sharing your knowledge. Quick question though. I am working with the natural log data of the NSE 20 Share Index. I have been able to estimate the GARCH(1, 1) GARCH-M(1, 1) and TGARCH(1, 1) using the data. However, the EGARCH estimation is returning an error message "log of non-positive value". What could be the problem and how can I resolve it? TIA.

[leozhang]

Support from China! thank you very much!

[mustanggemini]

Good morning Dr. Do you have a research study or paper that describes the process of egarch in your study? For reference and citation. Appreciate your input. Thanks

[imdatkoksal]

I want to aska question about forecasting price... Let we make a xGARCH estimation. How can we use this estimation to forecast t+1 time price and t+1 time confidence interval for the forecasted price? I will be very grateful to you if you answer this question. Or you can make a video for this subject....

[abdulhadialhatem]

Many thanks for the explanation. The results of Lambda C (5) in my paper showed significant but positive. Does this mean there is a leverage effect?

[SITIAISYAH-mkot]

thank you so much, you help me a lot! love from Malaysia :)

[edilson]

Dear Professor. Could you do a multivariate GARCH with a forecast of one of the volatility of the variables?

[shelternene]

Dear Prof. Many thanks for the explanation. Sorry to take you back. When testing for Heteroskedasticity (ARCH Effect), does the p-values have to be significant at 5% level?. Mine is 0.0987

[drsaghirghauri]

respected professor, did we get the series of leverage effects - kindly respond

[mdqamarazam]

Greeting from India.
Madam, while calculating EGARCH, the coefficient of lag return is found to be negative but it is statistically significant. Should I say that my estimation is good?

[kaancetinkaya]

Hello ma'm thank you for your help and videos, my EGARCH alfphas and Betas probabilities are significiant at 5% but my constant's probabiltiy is 0.13. This model is valid or not in this condition ?

[randomYtuberr]

Is it necessary fo variable C to be significant while formulating EGARCH model ? my EGARCH model shows p value higher than 0.05? What shud i do ?