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

•A novel simulation approach for valuation of VaR is proposed.•Calibrated to fit both non-parametric and semi-parametric asset structures.•Applicable to options and bonds with a variety of financial and market conditions.•Prior knowledge of statistical features of risks factors is not required.

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.