Does modeling jumps help? A comparision of realized volatility models for risk prediction

Vol: 
26/2012
Author name: 
Liao Y
Year: 
2012
Month: 
June
Abstract: 

Recent literature has focuses on realized volatility models to predict financial risk. This paper studies the benefit of explicitly modeling jumps in this class of models for value at risk (VaR) prediction. Several popular realized volatility models are compared in terms of their VaR forecasting performances through a Monte carlo study and an analysis based on empirical data of eight Chinese stocks. The results suggest that careful modeling of jumps in realized volatility models can largely improve VaR prediction, especially for emerging markets where jumps play a stronger role than those in developed markets.

Publication file: 

Updated:  1 December 2021/Responsible Officer:  Crawford Engagement/Page Contact:  CAMA admin