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

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

Finance Research Letters, 2017, vol. 21, issue C, 10-20

Abstract: 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.

Keywords: Value-at-Risk; Monte Carlo simulation; Delta–Gamma approximation; Vasicek model; Cox–Ingersoll–Ross model (search for similar items in EconPapers)
JEL-codes: C4 C5 C6 G1 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:21:y:2017:i:c:p:10-20

DOI: 10.1016/j.frl.2016.11.013

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