Journal of Risk Model Validation最新文献_第5页

A Verification Model to Capture Option Risk and Hedging Based on a Modified Underlying Beta 基于修正基础贝塔的期权风险捕获与套期保值验证模型

IF 0.7 4区经济学

Journal of Risk Model Validation Pub Date : 2019-09-18 DOI: 10.21314/JRMV.2020.233

Chuan-he Shen, Yang Liu

{"title":"A Verification Model to Capture Option Risk and Hedging Based on a Modified Underlying Beta","authors":"Chuan-he Shen, Yang Liu","doi":"10.21314/JRMV.2020.233","DOIUrl":"https://doi.org/10.21314/JRMV.2020.233","url":null,"abstract":"The mining and hedging of option volatility information are the core issues of stock option markets. This paper analyzes the relationship between option risk and expected return from the perspective of the underlying beta, and estimates the degree of correlation. As the assumptions of the capital asset pricing model and Black–Scholes model are not consistent with the actual situation in the financial market, we use applied statistical models to introduce kurtosis and skewness, and to introduce curvature and high-order-moment error terms to optimize the underlying beta model. We then develop a verification model for mining option risk and hedging by employing the modified underlying beta. We verify the hedging performance of the above model by choosing different market samples, such as the China, Hong Kong and US financial markets. The results show that the hedging performance of the optimized underlying beta model in the US market is most satisfactory, followed by the Hong Kong market and then the Chinese mainland market. Meanwhile, the hedging effect of the underlying beta model improved by curvature and high-order-moment error terms is superior to that of the model of the underlying beta adjusted by the kurtosis and skewness.","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2019-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45091779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Nonparametric tests for jump detection via false discovery rate control: a Monte Carlo study 通过错误发现率控制的跳跃检测的非参数检验:蒙特卡罗研究

IF 0.7 4区经济学

Journal of Risk Model Validation Pub Date : 2019-09-10 DOI: 10.21314/jrmv.2019.209

Kaiqiao Li, Kang He, Lizhou Nie, Wei Zhu, Pei-Fen Kuan

引用次数: 0

Model risk management: from epistemology to corporate governance 模型风险管理:从认识论到公司治理

IF 0.7 4区经济学

Journal of Risk Model Validation Pub Date : 2019-09-10 DOI: 10.21314/jrmv.2019.208

Bertrand K. Hassani

引用次数: 2

An advanced hybrid classification technique for credit risk evaluation 一种用于信用风险评估的先进混合分类技术

IF 0.7 4区经济学

Journal of Risk Model Validation Pub Date : 2019-09-02 DOI: 10.21314/jrmv.2019.210

Chong Wu, Dekun Gao, Qianqun Ma

引用次数: 1

Risk data validation under BCBS 239 BCBS 239规定的风险数据验证

IF 0.7 4区经济学

Journal of Risk Model Validation Pub Date : 2019-08-12 DOI: 10.21314/JRMV.2019.207

Lukasz Prorokowski

引用次数: 0

Old-Fashioned Parametric Models are Still the Best: A Comparison of Value-at-Risk Approaches in Several Volatility States 老式的参数模型仍然是最好的:几种波动状态下风险价值方法的比较

IF 0.7 4区经济学

Journal of Risk Model Validation Pub Date : 2019-07-09 DOI: 10.21314/JRMV.2020.222

Mateusz Buczyński, M. Chlebus

{"title":"Old-Fashioned Parametric Models are Still the Best: A Comparison of Value-at-Risk Approaches in Several Volatility States","authors":"Mateusz Buczyński, M. Chlebus","doi":"10.21314/JRMV.2020.222","DOIUrl":"https://doi.org/10.21314/JRMV.2020.222","url":null,"abstract":"Numerous advances in the modelling techniques of Value-at-Risk (VaR) have provided the financial institutions with a wide scope of market risk approaches. Yet it remains unknown which of the models should be used depending on the state of volatility. In this article we present the backtesting results for 1% and 2.5% VaR of six indexes from emerging and developed countries using several most known VaR models, among many: GARCH, EVT, CAViaR and FHS with multiple sets of parameters. The backtesting procedure has been based on the excess ratio, Kupiec and Christoffersen tests for multiple thresholds and cost functions. The added value of this article is that we have compared the models in four different scenarios, with different states of volatility in training and testing samples. The results indicate that the best of the models that is the least affected by changes in the volatility is GARCH(1,1) with standardized student's t-distribution. Non-parmetric techniques (e.g. CAViaR with GARCH setup (see Engle and Manganelli, 2001) or FHS with skewed normal distribution) have very prominent results in testing periods with low volatility, but are relatively worse in the turbulent periods. We have also discussed an automatic method to setting a threshold of extreme distribution for EVT models, as well as several ensembling methods for VaR, among which minimum of best models has been proven to have very good results - in particular a minimum of GARCH(1,1) with standardized student's t-distribution and either EVT or CAViaR models.","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2019-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45415275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Model risk tiering: an exploration of industry practices and principles 模型风险分级:对行业实践和原则的探索

IF 0.7 4区经济学

Journal of Risk Model Validation Pub Date : 2019-06-06 DOI: 10.21314/jrmv.2019.205

N. Kiritz, Miles Ravitz, Mark E. Levonian

引用次数: 1

Credit portfolio stress testing using transition matrixes 使用过渡矩阵进行信贷组合压力测试

IF 0.7 4区经济学

Journal of Risk Model Validation Pub Date : 2019-06-06 DOI: 10.21314/jrmv.2019.204

R. Neagu, G. Lipsa, Jing Wu

引用次数: 0

Validation of the backtesting process under the targeted review of internal models: practical recommendations for probability of default models 在有针对性地审查内部模型的情况下验证回溯测试过程：关于默认模型概率的实用建议

IF 0.7 4区经济学

Journal of Risk Model Validation Pub Date : 2019-05-16 DOI: 10.21314/JRMV.2019.203

Lukasz Prorokowski

{"title":"Validation of the backtesting process under the targeted review of internal models: practical recommendations for probability of default models","authors":"Lukasz Prorokowski","doi":"10.21314/JRMV.2019.203","DOIUrl":"https://doi.org/10.21314/JRMV.2019.203","url":null,"abstract":"This paper provides practical recommendations for the validation of the backtesting process under the targeted review of internal models (TRIM). It advises on the introductory steps for validating the backtesting process and reviews the available statistical tests for calibration, discrimination and stability backtesting. The TRIM regulatory exercise is an international supervisory initiative that inspects the internal models and related internal risk and governance policies of eurozone banks that are permitted to use the advanced internal risk-based (AIRB) approach. Under the TRIM guidelines, the designated banks should have specific policies and internal guidelines for the validation of the backtesting process. Further, the affected banks are required to validate the entire backtesting process. Addressing these needs, this paper serves as a basis for producing such policies and utilizing appropriate statistical tools for validating the backtesting process. The paper focusses on probability of default models. To date, no academic study has discussed the validation of the backtesting process with reference to the TRIM rules.","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2019-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45855584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

An optimized support vector machine intelligent technique using optimized feature selection methods: evidence from Chinese credit approval data 一种基于优化特征选择方法的优化支持向量机智能技术——来自中国信贷审批数据的证据

IF 0.7 4区经济学

Journal of Risk Model Validation Pub Date : 2019-04-08 DOI: 10.21314/JRMV.2019.206

M. Z. Abedin, Chi Guo-tai, F. Moula

引用次数: 4