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Estimating value-at-risk using quantile regression and implied volatilities Journal of Risk Model Validation (IF 0.25) Pub Date : 2022-01-01 Petter de Lange,Morten Risstad,Sjur Westgaard
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Size does matter: a study on the required window size for optimal-quality market risk models Journal of Risk Model Validation (IF 0.25) Pub Date : 2022-01-01 Mateusz Buczyński,Marcin Chlebus
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Beyond the contract: client behavior from origination to default as the new set of the loss given default risk drivers Journal of Risk Model Validation (IF 0.25) Pub Date : 2021-02-01 Wojciech Starosta
Modeling loss given default has increased in popularity as it has become a crucial parameter for establishing capital buffers under Basel II and III and for calculating the impairment of financial assets under the International Financial Reporting Standard 9. The most recent literature on this topic focuses mainly on estimation methods and less on the variables used to explain the variability in loss
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Research on listed companies’ credit ratings, considering classification performance and interpretability Journal of Risk Model Validation (IF 0.25) Pub Date : 2021-02-01 Zhe Li,Guotai Chi,Ying Zhou,Wenxuan Liu
Any credit evaluation system must be able not only to identify defaults, but also to be interpretable and provide reasons for defaults. Therefore, this study uses the correlation coefficient and F -test to select the initial features of a credit evaluation system, and then a validity index for a second selection to ensure that the feature system has the optimum ability to discriminate in determining
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Calibration of rating grades to point-in-time and through-the-cycle levels of probability of default Journal of Risk Model Validation (IF 0.25) Pub Date : 2021-01-01 Mark Rubtsov
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A prudent loss given default estimation for mortgages. II Journal of Risk Model Validation (IF 0.25) Pub Date : 2021-01-01 Bogie Ozdemir,Emma Huang
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The value-at-risk of time-series momentum and contrarian trading strategies Journal of Risk Model Validation (IF 0.25) Pub Date : 2021-01-01 Keunbae Ahn,Jihye Park,KiHoon Hong
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A pricing model with dynamic credit rating transition matrixes Journal of Risk Model Validation (IF 0.25) Pub Date : 2021-01-01 Yun-Cheng Tsai,Sheng-Hsuan Lin,Yuh-Dauh Lyuu
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Empirical validation of the credit rating migration model for estimating the migration boundary Journal of Risk Model Validation (IF 0.25) Pub Date : 2021-01-01 Yang Lin,Jin Liang
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Bifractal receiver operating characteristic curves: a formula for generating receiver operating characteristic curves in credit-scoring contexts Journal of Risk Model Validation (IF 0.25) Pub Date : 2021-01-01 Błażej Kochański
This paper formulates a mathematical model for generating receiver operating characteristic (ROC) curves without underlying data. Credit scoring practitioners know that the Gini coefficient usually drops if it is only calculated on cases above the cutoff. This fact is not a mathematical necessity, however, as it is theoretically possible to get an ROC curve that keeps the same Gini coefficient no matter
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Evaluation of backtesting techniques on risk models with different horizons Journal of Risk Model Validation (IF 0.25) Pub Date : 2021-01-01 Grigorios Kontaxis,Ioannis Tsolas
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Comprehensive Capital Analysis and Review consistent yield curve stress testing: from Nelson–Siegel to machine learning Journal of Risk Model Validation (IF 0.25) Pub Date : 2021-01-01 Vilen Abramov,Christopher Atchison,Zhengye Bian
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Validation nightmare: the slotting approach under International Financial Reporting Standard 9 Journal of Risk Model Validation (IF 0.25) Pub Date : 2021-01-01 Lukasz Prorokowski,Oleg Deev,Jena-Daniel Guigou
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Backtesting of a probability of default model in the point-in-time–through-the-cycle context Journal of Risk Model Validation (IF 0.25) Pub Date : 2021-01-01 Mark Rubtsov
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What can we learn from what a machine has learned? Interpreting credit risk machine learning models Journal of Risk Model Validation (IF 0.25) Pub Date : 2021-01-01 Nehalkumar Bharodia,Wei Chen
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The use of range-based volatility estimators in testing for Granger causality in risk on international capital markets Journal of Risk Model Validation (IF 0.25) Pub Date : 2020-09-01 Marcin Fałdziński,Magdalena Osińska
This study utilizes the extreme value theory (EVT) approach to compare the performance of a wide variety of range-based volatility estimators in the analysis of causality in risk between emerging and developed markets. The AR(1)-GARCH(1, 1) model with t -distribution is used as a benchmark. Regulator and firm loss functions are used to select the best volatility model. Two tests of causality in risk
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Benchmarking loss given default discount rates Journal of Risk Model Validation (IF 0.25) Pub Date : 2020-09-01 Harald Scheule,Stephan Jortzik
This paper provides a theoretical and empirical analysis of alternative discount rate concepts for computing loss given default (LGD) rates using historical bank workout data. It benchmarks five discount rate concepts for workout recovery cashflows in order to derive observed LGDs in terms of economic robustness and empirical implications: contract rate at origination, loan-weighted average cost of
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Old-fashioned parametric models are still the best: a comparison of value-at-risk approaches in several volatility states Journal of Risk Model Validation (IF 0.25) Pub Date : 2020-06-01 Mateusz Buczyński,Marcin Chlebus
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
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An empirical evaluation of large dynamic covariance models in portfolio value-at-risk estimation Journal of Risk Model Validation (IF 0.25) Pub Date : 2020-06-01 Keith Law,Wai Keung Li,Philip Yu
The estimation of portfolio value-at-risk (VaR) requires a good estimate of the covariance matrix. As it is well known that a sample covariance matrix based on some historical rolling window is noisy and is a poor estimate for the high-dimensional population covariance matrix, to estimate the conditional portfolio VaR we develop a framework using the dynamic conditional covariance model, within which
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Risk-neutral densities: advanced methods of estimating nonnormal options underlying asset prices and returns Journal of Risk Model Validation (IF 0.25) Pub Date : 2020-06-01 André Santos,João Guerra
Option prices can be used to extract the implied risk-neutral density functions of the future underlying asset prices and returns. These market expectations provide valuable information that can be helpful to policy makers and investors. We tested the accuracy and stability of four nonstructural models in estimating the “true” risk-neutral density functions from option prices: the density functional
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An alternative statistical framework for credit default prediction Journal of Risk Model Validation (IF 0.25) Pub Date : 2020-06-01 Mohammad Shamsu Uddin,Guotai Chi,Tabassum Habib,Ying Zhou
The purpose of this study is to introduce a gradient-boosting model that is robust to high-dimensional data and can produce a strong classifier by combining the predictors of many weak classifiers for credit default risk prediction. Therefore, this method is recommended for practical applications. This study compares the gradient-boosting model with four other well-known classifiers, namely, a classification
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How accurate is the accuracy ratio in credit risk model validation? Journal of Risk Model Validation (IF 0.25) Pub Date : 2020-01-01 Marco Van der Burgt
The receiver operating curve and the cumulative accuracy profile visualize the ability of a credit scoring model to distinguish defaulting from nondefaulting counterparties. These curves lead to performance metrics such as the accuracy ratio and the area under the curve. Since these performance metrics are sample properties, we cannot draw firm conclusions on the model performance without knowing the
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Statistical properties of the population stability index Journal of Risk Model Validation (IF 0.25) Pub Date : 2020-01-01 Bilal Yurdakul,Joshua Naranjo
The population stability index (PSI) is a widely used statistic that measures how much a variable has shifted over time. A high PSI may alert the business to a change in the characteristics of a population. This shift may require investigation and possibly a model update. PSI is commonly used among banks to measure the shift between model development data and current data. Banks may face additional
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Validation of index and benchmark assignment: adequacy of capturing tail risk Journal of Risk Model Validation (IF 0.25) Pub Date : 2019-12-01 Lukasz Prorokowski
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Value-at-risk in the European energy market: a comparison of parametric, historical simulation and quantile regression value-at-risk Journal of Risk Model Validation (IF 0.25) Pub Date : 2019-12-01 Sjur Westgaard
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International Financial Reporting Standard 9 expected credit loss estimation: advanced models for estimating portfolio loss and weighting scenario losses Journal of Risk Model Validation (IF 0.25) Pub Date : 2019-01-01 Bill Huajian Yang,Biao Wu,Kaijie Cui,Zunwei Du,Glenn Fei
The estimation of portfolio expected credit loss is required for International Financial Reporting Standard 9 (IFRS9) regulatory purposes. This starts with the estimation of scenario loss at loan level, which is then aggregated and summed up by scenario probability weights to obtain the portfolio expected loss. This estimated loss can vary significantly depending on the levels of loss severity generated
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An advanced hybrid classification technique for credit risk evaluation Journal of Risk Model Validation (IF 0.25) Pub Date : 2019-01-01 Chong Wu,Dekun Gao,Qianqun Ma,Qi Wang,Yu Lu
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Nonparametric tests for jump detection via false discovery rate control: a Monte Carlo study Journal of Risk Model Validation (IF 0.25) Pub Date : 2019-01-01 Kaiqiao Li,Kan He,Lizhou Nie,Wei Zhu,Pei Fen Kuan
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Model risk management: from epistemology to corporate governance Journal of Risk Model Validation (IF 0.25) Pub Date : 2019-01-01 Bertrand Hassani
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Model risk tiering: an exploration of industry practices and principles Journal of Risk Model Validation (IF 0.25) Pub Date : 2019-01-01 Nick Kiritz,Miles Ravitz,Mark Levonian
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Quantification of model risk in stress testing and scenario analysis Journal of Risk Model Validation (IF 0.25) Pub Date : 2019-01-01 Jimmy Skoglund
Understanding and quantifying the model risk inherent in loss projection models used in the macroeconomic stress testing and impairment estimation is of significant concern for both banks and regulators. The application of relative entropy techniques allow model misspecification robustness to be numerically quantified using exponential tilting towards an alternative probability law. Using a particular
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The utility of Basel III rules on excessive violations of internal risk models Journal of Risk Model Validation (IF 0.25) Pub Date : 2019-01-01 Wayne Tarrant
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Quantification of the estimation risk inherent in loss distribution approach models Journal of Risk Model Validation (IF 0.25) Pub Date : 2019-01-01 K. Panman,L. van Biljon,L. J. Haasbroek,W. D. Schutte,T. Verster
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A study on window-size selection for threshold and bootstrap value-at-risk models Journal of Risk Model Validation (IF 0.25) Pub Date : 2019-01-01 Anri Smith,Chun-Kai Huang
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Risk data validation under BCBS 239 Journal of Risk Model Validation (IF 0.25) Pub Date : 2019-01-01 Lukasz Prorokowski
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Credit portfolio stress testing using transition matrixes Journal of Risk Model Validation (IF 0.25) Pub Date : 2019-01-01 Radu Neagu,Gabriel Lipsa,Jing Wu,Jake Lee,Stephane Karm,John Jordan
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Validation of the backtesting process under the targeted review of internal models: practical recommendations for probability of default models Journal of Risk Model Validation (IF 0.25) Pub Date : 2019-01-01 Lukasz Prorokowski
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
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Incorporating volatility in tolerance intervals for pair-trading strategy and backtesting Journal of Risk Model Validation (IF 0.25) Pub Date : 2019-01-01 Cathy W. S. Chen,Tsai-Yu Lin,T. Y. Huang
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On the mathematical modeling of point-in-time and through-the-cycle probability of default estimation/validation Journal of Risk Model Validation (IF 0.25) Pub Date : 2019-01-01 Xin Zhang,Tony Tung
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Optimal allocation of model risk appetite and validation threshold in the Solvency II framework Journal of Risk Model Validation (IF 0.25) Pub Date : 2018-01-01 Liyi Lin,Marc Heemskerk,Peter Dekker
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A risk-sensitive approach for stressed transition probability matrixes Journal of Risk Model Validation (IF 0.25) Pub Date : 2018-01-01 Ahmet Perilioglu,Karina Perilioglu,Sukriye Tuysuz
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Procyclicality of capital and portfolio segmentation in the advanced internal ratings-based framework: an application to mortgage portfolios Journal of Risk Model Validation (IF 0.25) Pub Date : 2018-01-01 Jose Canals-Cerda
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The validation of filtered historical value-at-risk models Journal of Risk Model Validation (IF 0.25) Pub Date : 2018-01-01 Pedro Gurrola-Perez
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Validation of profit and loss attribution models for equity derivatives Journal of Risk Model Validation (IF 0.25) Pub Date : 2018-01-01 Dilip B. Madan,King Wang
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Shrunk volatility value-at-risk: an application on US balanced portfolios Journal of Risk Model Validation (IF 0.25) Pub Date : 2018-01-01 Stefano Colucci
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Model risk in the Fundamental Review of the Trading Book: the case of the Default Risk Charge Journal of Risk Model Validation (IF 0.25) Pub Date : 2018-01-01 Sascha Wilkens,Mirela Predescu
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A comprehensive evaluation of value-at-risk models and a comparison of their performance in emerging markets Journal of Risk Model Validation (IF 0.25) Pub Date : 2018-01-01 Saeed Shaker-Akhtekhane,Mohsen Seighali,Solmaz Poorabbas
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Evaluating the credit exposure of interest rate derivatives under the real-world measure Journal of Risk Model Validation (IF 0.25) Pub Date : 2018-01-01 Takashi Yasuoka
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Smoothing algorithms by constrained maximum likelihood: methodologies and implementations for Comprehensive Capital Analysis and Review stress testing and International Financial Reporting Standard 9 expected credit loss estimation Journal of Risk Model Validation (IF 0.25) Pub Date : 2018-01-01 Bill Huajian Yang
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A central limit theorem formulation for empirical bootstrap value-at-risk Journal of Risk Model Validation (IF 0.25) Pub Date : 2018-01-01 Peter Mitic,Nicholas Bloxham
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Back to backtesting: integrated backtesting for value-at-risk and expected shortfall in practice Journal of Risk Model Validation (IF 0.25) Pub Date : 2018-01-01 Carsten S. Wehn
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Analytical expressions of risk quantities for composite models Journal of Risk Model Validation (IF 0.25) Pub Date : 2018-01-01 Jose Maria Sarabia,Enrique Calderin-Ojeda
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The predictability implied by consumption-based asset-pricing models: a review of the theory and empirical evidence Journal of Risk Model Validation (IF 0.25) Pub Date : 2018-01-01 Jiun-Lin Chen,Hyosoek Hwang
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Underperforming performance measures? A review of measures for loss given default models Journal of Risk Model Validation (IF 0.25) Pub Date : 2018-01-01 Katarzyna Bijak, Lyn C. Thomas
As far as Probability of Default (PD) prediction is concerned, the model performance is typically measured with the Gini coefficient and/or the Kolmogorov-Smirnov (KS) statistic. For Loss Given Default (LGD) models, there are no standard performance measures, though, and more than 15 different measures are used, including Mean Square Error (MSE), Mean Absolute Error (MAE), coefficient of determination
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Bayesian analysis in an aggregate loss model: validation of the structure functions Journal of Risk Model Validation (IF 0.25) Pub Date : 2017-09-01 Agustin Hernandez Bastida,Jose Maria Perez Sanchez,Pilar Fernandez-Sanchez
Common ordinal models, including the ordered logit model and the continuation ratio model, are formulated by a common score (ie, a linear combination of given explanatory variables) plus rank-specific intercepts. Sensitivity to the common score is generally not differentiated between rank outcomes. We propose an ordinal model based on forward ordinal probabilities for rank outcomes. In addition to
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The use of the triangular approximation for some complicated risk measurement calculations Journal of Risk Model Validation (IF 0.25) Pub Date : 2017-09-01 Nick Georgiopoulos
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On the correlation and parametric approaches to calculation of credit value adjustment Journal of Risk Model Validation (IF 0.25) Pub Date : 2017-09-01 Tao Pang,Wei Chen,Le Li
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Simple models in finance: a mathematical analysis of the probabilistic recognition heuristic Journal of Risk Model Validation (IF 0.25) Pub Date : 2017-06-01 Martín Egozcue,Luis Fuentes García,Konstantinos Katsikopoulos,Michael Smithson
It is well known that laypersons and practitioners often resist using complex mathematical models such as those proposed by economics or finance, and instead use fast and frugal strategies to make decisions. We study one such strategy: the recognition heuristic. This states that people infer that an object they recognize has a higher value of a criterion of interest than an object they do not recognize
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Goodness-of-fit for discrete-choice models of borrower default Journal of Risk Model Validation (IF 0.25) Pub Date : 2017-04-01 Arden Hall
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Forward ordinal probability models for point-in-time probability of default term structure: methodologies and implementations for IFRS 9 expected credit loss estimation and CCAR stress testing Journal of Risk Model Validation (IF 0.25) Pub Date : 2017-01-01 Bill Huajian Yang
Common ordinal models, including the ordered logit model and the continuation ratio model, are structured by a common score (i.e., a linear combination of a list of given explanatory variables) plus rank specific intercepts. Sensitivity with respect to the common score is generally not differentiated between rank outcomes. In this paper, we propose an ordinal model based on forward ordinal probabilities