Applied Econometrics

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GARCH in JMulti

● Window ARCH Analysis.

● But first, save the residuals from the ARIMA

model: Model Checking => Plot/Add Residuals

=>Add to dataset.

● Switch to ARCH panel

● Select GARCH or ARCH or TARCH models,

parameters and execute the test.

● Results should be white noise, have no

autocorrelations... (see from diagnostics)

● Note: this two equation method less efficient

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● But: Squares of log returns – variance (under the assumption of zero mean, which usually holds) – has significant autocorrelations: ● Clustered volatility, which is persistent and thus predictable

GARCH in JMulti ● Window ARCH Analysis. ● But first, save the residuals from the ARIMA model: Model Checking => Plot/Add Residuals =>Add to dataset. ● Switch to ARCH panel ● Select GARCH or ARCH or TARCH models, parameters and execute the test. ● Results should be white noise, have no autocorrelations... (see from diagnostics) ● Note: this two equation method less efficient

Page 1 and 2: Applied Econometrics Seminar: ARIMA
Page 3 and 4: ARMA and ARIMA Models ● Just the
Page 5: ARIMA models in JMulti ● Data: PX
Page 8 and 9: Comments to the results ● Still s
Page 10 and 11: GARCH and ARCH - Summary ● ARCH -
Page 14 and 15: ● Plots for residuals of CEZ ARIM

Applied Econometrics

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