Perspectives on risk measurement: a critical assessment of PC-GARCH against the main volatility forecasting models
The paper makes a critical assessment of the Principal Components-GARCH (PC-GARCH) model and argues why, when dealing with hundreds or thousands of variables, this model comes up as the most appropriate to be used. The suitability originates from the perspective of quality/cost ratio of volatility forecasts, allowing for a trade-off between quality and costs when computational efforts are significant. PC-GARCH not only provides a method that allows for simpler volatility modeling, reducing significantly the computational time and getting rid of any problem that may arise from complex data manipulations, but also improves the modeling process quality by ensuring a stricter control of noise due to more stable correlation estimates.
Volume (Year): (2012)
Issue (Month): 1 (March)
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- Tim Bollerslev, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
EERI Research Paper Series
EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
- Bera, Anil K & Higgins, Matthew L, 1993. " ARCH Models: Properties, Estimation and Testing," Journal of Economic Surveys, Wiley Blackwell, vol. 7(4), pages 305-66, December.
- Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
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