Autoregressive conditional heteroskedasticity | Wikipedia audio article
Channel: wikipedia tts
Date: 06/10/2019

This is an audio version of the Wikipedia Article:
https://en.wikipedia.org/wiki/Autoregressive_conditional_heteroskedasticity


00:00:10 1 ARCH(iq/i) model specification
00:00:40 2 GARCH
00:01:05 2.1 GARCH(ip/i, iq/i) model specification
00:01:26 2.2 NGARCH
00:01:31 2.2.1 NAGARCH
00:01:41 2.3 IGARCH
00:01:51 2.4 EGARCH
00:02:11 2.5 GARCH-M
00:02:21 2.6 QGARCH
00:02:31 2.7 GJR-GARCH
00:02:47 2.8 TGARCH model
00:03:07 2.9 fGARCH
00:03:17 2.10 COGARCH
00:03:52 2.11 ZD-GARCH
00:04:08 2.12 Spatial GARCH



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SUMMARY
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In econometrics, the autoregressive conditional heteroskedasticity (ARCH) model is a statistical model for time series data that describes the variance of the current error term or innovation as a function of the actual sizes of the previous time periods' error terms; often the variance is related to the squares of the previous innovations. The ARCH model is appropriate when the error variance in a time series follows an autoregressive (AR) model; if an autoregressive moving average (ARMA) model is assumed for the error variance, the model is a generalized autoregressive conditional heteroskedasticity (GARCH) model.ARCH models are commonly employed in modeling financial time series that exhibit time-varying volatility and volatility clustering, i.e. periods of swings interspersed with periods of relative calm. ARCH-type models are sometimes considered to be in the family of stochastic volatility models, although this is strictly incorrect since at time t the volatility is completely pre-determined (deterministic) given previous values.