The benefit of modeling jumps in realized volatility for risk prediction

evidence from Chinese mainland stocks

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13 Citations (Scopus)

Abstract

Recent literature has focused 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.
Original languageEnglish
Pages (from-to)25-48
Number of pages24
JournalPacific-Basin finance journal
Volume23
DOIs
Publication statusPublished - Jun 2013
Externally publishedYes

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Keywords

  • Value at risk (VaR)
  • Realized volatility
  • Jumps

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