(EViews10): Forecasting GARCH Volatility #forecast #garchforecasts #volatilityforecast

This video explains how to forecast volatility of the conditional variance in the generalised autoregressive conditional heteroscedasticity (GARCH) model 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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[yahayamuhammad]

Thank you so much prof. honestly your video is an eye opener to most of econometric topics. Really appreciate it.

[blessingamosa.]

Thank you for the good work you are doing. You can add a donation link to your site. Some of us would like to show an appreciation for the wonderful work.

[pepe_the_frog-]

Thank you very much, very helpful content

[rosierui]

Thank you for the awesome tutorial. I'd love to learn how to do out-of-sample forecast with GARCH in EVIEWS?

[__DeaMelindaSimamora]

hai professor, thank u so much for this helpful video
i wanna ask you, if we want to forecast data with static forecast, do we have to predict them one by one?

and for the manual method, for the ar(1) model, how to determine the price difference that will be used for forecasting, because for forecasting the next few days there is definitely no original data? thank you for your help🙏🏻

[mohammedalnour]

Thank you so much for your professional explanation. If i am to ask, how can I test the convergence of the series in EViews and by which model?

[viniciussimoes]

Hi Professor. Thank you for the helpful video. If I want to predict 4 or 5 steps ahead, how could I do in Eviews?

[michaelbaah]

hello mummy God bless you for your good works. please I want kindly request if you can make a video on using the partial sum of process to generate positive and negative volatility of exchange rate...thanks Michael from ghana

[ighenry_ys]

Thank you very much, very helpful content. I have 20 data, and I used GARCH model to predict 5 data for the future (by using static forecast) and want to see the percentage error of my prediction. Because in Eviews, I must do the same thing until 5 times to predict the data. I was confused at my percentage error (MAPE) was different when I predict for 5 times. Is it take from the average percentage error (MAPE) of 5 times or I just take my last error percentage? Thank you

[swarnachari]

Thank you professor for the nice video....my question is how to say this model is good for

[mohapatraful]

Dear professor please make a video on multi garch model in eviews

[imdatkoksal]

Dear Professor, how can we forecast the price at t time of assets in a confidence interval. Can we?

[ahlemouhibi]

i want to get volatility series by using GARCH model, i should follow all this steps or whta? i'm really so confused

[azahsyafinazazhar]

Hi professor! Can I know how to forecast future volatility that is not in the sample using this method?

[abdiabrahim]

Dear Professor, How to interpret EGARCH MODEL coefficients output?