Value-at-Risk estimation with stochastic interest rate models for option-bond portfolios

Xiaoyu Wang, Dejun Xie*, Jingjing Jiang, Xiaoxia Wu, Jia He

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


This article proposes a Monte Carlo simulation based approach for measuring Value-at-Risk of a portfolio consisting of options and bonds. The approach allows for jump-diffusions in underlying assets and affords to fit a variety of model layout, including both non-parametric and semi-parametric structures. Backtesting was conducted to assess the effectiveness of the method. The algorithm was tested against various trading positions, time horizons, and correlations between asset prices and market return rates. A prominent advantage of our approach is that its implementation does not require prior knowledge of the joint distribution or other statistical features of the related risk factors.

Original languageEnglish
Pages (from-to)10-20
Number of pages11
JournalFinance Research Letters
Publication statusPublished - 2017


  • Cox–Ingersoll–Ross model
  • Delta–Gamma approximation
  • Monte Carlo simulation
  • Value-at-Risk
  • Vasicek model


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