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

Herman Mørkved Blom, Petter Eilif de Lange () and Morten Risstad
Additional contact information
Herman Mørkved Blom: Department of Economics, Norwegian University of Science and Technology, Klæbuveien 72, 7030 Trondheim, Norway
Petter Eilif de Lange: Department of International Business, Norwegian University of Science and Technology, Klæbuveien 72, 7030 Trondheim, Norway
Morten Risstad: Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology, Klæbuveien 72, 7030 Trondheim, Norway

JRFM, 2023, vol. 16, issue 7, 1-23

Abstract: 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.

Keywords: value-at-risk; over-the-counter foreign exchange (OTC FX) options; quantile regression; machine learning (ML) (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1911-8074/16/7/312/pdf (application/pdf)
https://www.mdpi.com/1911-8074/16/7/312/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jjrfmx:v:16:y:2023:i:7:p:312-:d:1181058

Access Statistics for this article

JRFM is currently edited by Ms. Chelthy Cheng

More articles in JRFM from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().