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When to apply GARCH model?
GARCH is a statistical model that can be used to analyze a number of different types of financial data, for instance, macroeconomic data. Financial institutions typically use this model to estimate the volatility of returns for stocks, bonds, and market indices.
Can anything beat a GARCH11?
First, in the analysis of the exchange rate data we find no evidence that the GARCH(1,1) is inferior to other models, whereas the GARCH(1,1) is clearly outperformed in the analysis of IBM returns. The different mean specifications, zero-mean, constant mean and GARCH-in-mean, result in almost identical performances.
Is GARCH11 the best?
The evaluation is based on daily realized volatility of IBM equity return data and find that the GARCH(1,1) is significantly outperformed by other models, mainly those that accommodate a leverage effect. Published in: 2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003.
What is volatility in time series?
In finance, volatility (usually denoted by σ) is the degree of variation of a trading price series over time, usually measured by the standard deviation of logarithmic returns. Historic volatility measures a time series of past market prices.
How are time series used to test GARCH models?
In addition, existing tests for GARCH models usually check a fixed finite number of lag orders. However, recent empirical studies (e.g., Baillie et al., 1996) find that high-frequency financial time series may display a long memory in volatility clustering, where volatility depends on a very long past history.
When does GARCH ( 1, 0 ) make sense?
Hence, GARCH (1,0) only makes sense when ω = 0 and δ = 1, which means the whole GARCH model is redundant as the conditional variance is constant. Of course, when estimating models in practice, we do not have infinite past; but for long enough time series this approximation should be reasonably representative.
Is the term GARCH autoregressive or homoskedastic?
In other words, GARCH (1,0) implies homoskedasticity and thus the “autoregressive” term, and indeed the whole model, becomes redundant. My argumentation in the paragraph above was imprecise and likely misleading.
How is the EGARCH model different from the GARCH model?
Therefore while the GARCH model imposes the nonnegative constraints on the parameters, EGARCH models the log of the conditional variance so that there are no restrictions on these parameters [3].