Estimation of VaR in conditional heteroscedastic models for principal‐protected notes

Author:

Chen Fen‐Ying

Abstract

PurposeThe aim of this paper is to examine the accuracy of GARCH and provide a comparison of GARCH‐type and the other time series models in financial commodity markets.Design/methodology/approachFirst, a model fitting is performed to choose suitable models with conditional volatility for principal‐protected and path‐dependent notes by means of Akaike information criterion (AIC) and Schwartz Bayesian information criterion (SBC). Second, this paper adopts the backtesting criteria and the Diebold and Mariano test to compare the performances of the selected time series models.FindingsThe empirical results show that the performance of GARCH is significantly worse than EGARCH(1,1) based on the Diebold and Mariano test criteria. By backtesting test criteria, the null hypothesis that a given confidence level is the true probability in ARCH(4) cannot be rejected. The interesting results are different from past studies.Originality/valueThere is little literature of principal‐protected notes that focuses on the downside risk for investors. But, managing downside risk is important for individual and institution investors. This paper offer new insight into the literature of principal‐protected notes.

Publisher

Emerald

Subject

Finance

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3