GARCH-type forecasting models for volatility of stock market and MCS test

ABSTRACT

The prediction of time-changing volatility is an important task in the modeling of financial data. In the paper, a comprehensive analysis of the mean return and conditional variance of SSE380 index is performed to use GARCH, EGARCH and TGARCH models with Normal innovation and Student's t innovation. Conducting a bootstrap simulation study which shows the Model Confidence Set (MCS) captures the superior models across a range of significance levels. The experimental results show that, under various loss functions, the GARCH using Student's t innovation model is the best model for volatility predictions of SSE380 among the six models.

KEYWORDS:

• GARCH-type
• MCS test
• SSE380
• Volatility

Notes

1 As discussed by Engle and Patton, 2001, the specification of the mean equation is not important for forecasting studies, without significantly degrading the performance of the proposed model. In the paper, the results of the model estimations are not presented when the study concentrates on forecasting performance, but the model is available from the author's request.