Abstract
As seen in earlier chapters, financial market data often exhibits volatility clustering, where time series show periods of high volatility and periods of low volatility; see, for example, Fig. 14.1. In fact, with economic and financial data, time-varying volatility is more common than constant volatility, and accurate modeling of time-varying volatility is of great importance in financial engineering.
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Notes
- 1.
Weighted Ljung-Box and ARCH-LM statistics of Fisher and Gallagher (2012) are provided by the ugarchfit() function to better account for the distribution of the statistics when applied to residuals from a fitted model; their use and interpretation remains unchanged.
- 2.
These Chi-squared tests are based on the tests of Palm (1996); group indicates the number of bins used in the implementation.
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Ruppert, D., Matteson, D.S. (2015). GARCH Models. In: Statistics and Data Analysis for Financial Engineering. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2614-5_14
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