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Results tagged with garch
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Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used for time series in which the conditional variance is time-varying and autocorrelated. The conditional variance is a linear combination of lagged conditional variances and lagged squared errors. The conditional variance equation in GARCH models is deterministic, in contrast to Stochastic Volatility (SV) models.
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Are GARCH models dependent on the returns forecasting model?
Hi Quantitative Fiance Stack Exchange,
It's my first go at GARCH models so please give me a chance with my phrasing.
I understand that GARCH models are used to forecast volatility. … The GARCH(1,1) takes the form:
$$\sigma^2_t=\alpha+\beta_1\epsilon_{t-1}+\beta_2\sigma^2_{t-1}$$
I understand the lagged term $\sigma^2_{t-1}$ makes up the AR part of GARCH. …
- 101
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How is a GARCH model readily complementary to a forecasting model?
First, I understand that you can have a forecasting model to forecast returns and a GARCH model to forecast volatility. … Therefore, GARCH is only readily implementable if you somehow found a way to incorporate volatility in your strategy. …
- 101