A Study on the Application of Quantile Regression Equation in Forecasting Financial Value at Risk in Financial Markets

Chen, Lin; Hatamleh, Ibrahim

doi:10.2478/amns.2022.2.0174

Cited by 2 publications

(1 citation statement)

References 12 publications

(12 reference statements)

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“…He found that this model outperformed both the GARCH-based methods and the CAViaR models. Chen and Chen (2002) found that calculating VaR at the Nikkei 225 index using quantile regression outperformed the conventional variance-covariance approach. In addition, Shim et al (2012) employed semiparametric support vector quantile regression (SSVQR) models to estimate VaR on return data on the S&P 500, NIKEI 225, and KOSPI 200 indices.…”

Section: Literature Reviewmentioning

confidence: 99%

Estimating Value-at-Risk in the EURUSD Currency Cross from Implied Volatilities Using Machine Learning Methods and Quantile Regression

Blom

Lange

Risstad

2023

JRFM

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In this study, we propose a semiparametric, parsimonious value-at-risk forecasting model, based on quantile regression and machine learning methods, combined with readily available market prices of option contracts from the over-the-counter foreign exchange rate interbank market. We aim at improving existing methods for VaR prediction of currency investments using machine learning. We employ two different methods, i.e., ensemble methods and neural networks. Explanatory variables are implied volatilities with plausible economic interpretation. The forward-looking nature of the model, achieved by the application of implied volatilities as risk factors, ensures that new information is rapidly reflected in value-at-risk estimates. To the best of our knowledge, this study is the first to utilize information in the volatility surface, combined with machine learning and quantile regression, for VaR prediction of currency investments. The proposed ensemble models achieve good estimates across all quantiles. The light gradient boosting machine model and the categorical boosting model both yield estimates which are better than, or equal to, those of the benchmark model. In general, neural network models are quite unstable.

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Section: Literature Reviewmentioning

confidence: 99%

Estimating Value-at-Risk in the EURUSD Currency Cross from Implied Volatilities Using Machine Learning Methods and Quantile Regression

Blom

Lange

Risstad

2023

JRFM

View full text Add to dashboard Cite

show abstract

Estimating Value at Risk From Implied Volatilities Using Machine Learning Meth-Ods and Quantile Regression

Blom¹,

Lange²,

Risstad³

2023

Preprint

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In this study we propose a semi-parametric, parsimonious Value at Risk forecasting model, based on quantile regression and machine learning methods, combined with readily available market prices of option contracts from the over-the-counter foreign exchange rate interbank market. We aim at improving existing methods for VaR prediction of currency investments using machine learning. We employ two different methods - ensemble methods and neural networks. Explanatory variables are implied volatilities with plausible economic interpretation. The forward-looking nature of the model, achieved by the application of implied volatilities as risk factors, ensures that new information is rapidly reflected in Value at Risk estimates. To the best of our knowledge, this paper is the first to utilize information in the volatility surface, combined with machine learning and quantile regression, for VaR prediction of currency investments. The proposed ensemble models achieve good estimates across all quantiles. The light gra-dient-boosting machine model and the categorical boosting model both yield estimates which are better than, or equal to, those of the benchmark model. The neural network models are in general quite unstable.

show abstract

A Study on the Application of Quantile Regression Equation in Forecasting Financial Value at Risk in Financial Markets

Cited by 2 publications

References 12 publications

Estimating Value-at-Risk in the EURUSD Currency Cross from Implied Volatilities Using Machine Learning Methods and Quantile Regression

Estimating Value-at-Risk in the EURUSD Currency Cross from Implied Volatilities Using Machine Learning Methods and Quantile Regression

Estimating Value at Risk From Implied Volatilities Using Machine Learning Meth-Ods and Quantile Regression

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