Estimating the United States Dollar Index Returns’ Value at Risk: Empirical Evidence from RiskMetrics and Simultaneous Bootstrap Quantile Regression Methods

Estimating the United States Dollar Index Returns’ Value at Risk: Empirical Evidence from RiskMetrics and Simultaneous Bootstrap Quantile Regression Methods

Musolongo Mawete Charme

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Jiangxi University of Finance and Economics

Estimating the United States Dollar Index Returns’ Value at Risk: Empirical Evidence from RiskMetrics and Simultaneous Bootstrap Quantile Regression Methods

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Abstract

Two methods, namely simultaneous bootstrap quantile regression and RiskMetrics, are backtesting and compared to establish which one is a better Value at Risk (VaR) estimate for the United States dollar index returns. Using daily closing prices and the nearby contract settlement prices from 20 November 1985 to 15 February 2017, the results of this empirical research point out that at 5% of the significance level, RiskMetrics with IGARCH (1, 1) underestimates VaR for the next trading day. From the backtest findings, the number of violations in the RiskMetrics method is more than in simultaneous bootstrap quantile regression even after controlling for marginal effects of the index futures returns and volatilities in both spot and futures markets.

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No external funding was declared for this work.

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The authors declare no conflict of interest.

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No ethics committee approval was required for this article type.

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How to Cite This Article

Musolongo Mawete Charme. 2020. \u201cEstimating the United States Dollar Index Returns’ Value at Risk: Empirical Evidence from RiskMetrics and Simultaneous Bootstrap Quantile Regression Methods\u201d. Global Journal of Management and Business Research - C: Finance GJMBR-C Volume 20 (GJMBR Volume 20 Issue C1).

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GJMBR Volume 20 Issue C1

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Crossref Journal DOI 10.17406/GJMBR

Print ISSN 0975-5853

e-ISSN 2249-4588

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GJMBR-C Classification JEL Code: G00

Submission ReceivedDecember 12, 2019
Peer Review Double Blind
Handling Editor
Accepted January 4, 2020
Published January 15, 2020

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v1.2

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February 29, 2020

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