A study on window-size selection for threshold and bootstrap value-at-risk models

IF 0.4 4区经济学 Q4 BUSINESS, FINANCE

Journal of Risk Model Validation Pub Date : 2019-10-17 DOI:10.21314/jrmv.2019.211

Anri Smith, Chun-Kai Huang

引用次数: 0

Abstract

This paper investigates the effects of window size selection on various models for Value-at-Risk (VaR) forecasting using high performance computing. Subsequently, automated procedures using change-point analysis for optimal window size selection are proposed. In particular, stationary bootstrapping and the peaks-over-threshold methods are utilized for the rolling daily VaR estimation and are contrasted with the classical conditional Gaussian model. It is evidenced that change-point procedures can, on average, result in more adequate risk predictions than a predetermined fixed window size. The data sets analyzed include indices across 5 continents, i.e., the Dow Jones Industrial Average Index (DJI), the Financial Times Stock Exchange 100 Index (UKX), the NIKKEI Top 225 Index (NKY), the Johannesburg Stock Exchange Top 40 Index (JSE Top40), the Ibovespa Brazil Sao Paulo Stock Exchange All Index (IBOV), and the Bombay Stock Exchange Top 500 Index (BSE 500).

查看原文本刊更多论文

阈值和自举风险值模型的窗口大小选择研究

本文研究了窗口大小选择对使用高性能计算进行风险值（VaR）预测的各种模型的影响。随后，提出了使用变化点分析进行最佳窗口大小选择的自动化程序。特别地，平稳自举和峰值过阈值方法被用于滚动每日VaR估计，并与经典的条件高斯模型进行了对比。事实证明，与预先确定的固定窗口大小相比，变化点程序平均可以产生更充分的风险预测。分析的数据集包括五大洲的指数，即道琼斯工业平均指数（DJI）、英国《金融时报》证券交易所100指数（UKX）、日经225强指数（NKY）、约翰内斯堡证券交易所40强指数（JSE Top40）、巴西圣保罗证券交易所IBOV指数和孟买证券交易所500强指数（BSE 500）。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of Risk Model Validation BUSINESS, FINANCE-

CiteScore

1.20

自引率

28.60%

发文量

期刊介绍： As monetary institutions rely greatly on economic and financial models for a wide array of applications, model validation has become progressively inventive within the field of risk. The Journal of Risk Model Validation focuses on the implementation and validation of risk models, and aims to provide a greater understanding of key issues including the empirical evaluation of existing models, pitfalls in model validation and the development of new methods. We also publish papers on back-testing. Our main field of application is in credit risk modelling but we are happy to consider any issues of risk model validation for any financial asset class. The Journal of Risk Model Validation considers submissions in the form of research papers on topics including, but not limited to: Empirical model evaluation studies Backtesting studies Stress-testing studies New methods of model validation/backtesting/stress-testing Best practices in model development, deployment, production and maintenance Pitfalls in model validation techniques (all types of risk, forecasting, pricing and rating)