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The impact of oil and natural gas prices on overnight risk in exchange rates based on the MVMQ-CAViaR models

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  • Peng, Wei

Abstract

Existing research has not yet studied the impact of oil and gas prices on the overnight risk in exchange rates. This paper examines the impact of oil and natural gas prices on the overnight risk in the RMB, HKD, and Yen. An MVMQ-CAViaR model of multiple markets and an MVMQ-CAViaR model of combined influence are proposed to measure the impact of oil and natural gas prices on the overnight risk in the Yen, HKD, and RMB from 2013 to 2019. The empirical results show that the overnight risk in the RMB, HKD, and Yen are impacted by lagged risk and that the RMB suffer from the largest risks. The three exchange rates' overnight risks are impacted by the risk in oil prices and natural gas prices. However, the Yen suffers from the largest risk, and the HKD suffers from the smallest risk. This shows the fragility of Japan's energy sector and its extreme dependence on energy imports. This shows that the energy sector has little impact on the Hong Kong economy, which is dominated by finance and trading. The two industries of trading and finance do not require much energy. The impact of natural gas price risks on the overnight risk in the RMB, HKD, and Yen is smaller than that of oil price risks. All overnight risks are affected by the combined influence of oil prices and natural gas prices. The Yen exchange rate suffers from the greatest influence relative to the HKD exchange rate and RMB exchange rate. Thus, when oil prices and natural gas prices fall at the same time, more attention should be paid to the effect of their combined influence on national exchange rates. Financial regulators need to be ready for exchange rate fluctuations. The MVMQ-CAViaR model of combined influence is more accurate than the MVMQ-CAViaR model of multiple markets, especially at the 1% significance level and for HKD, and the MVMQ-CAViaR model of combined influence produces better estimates.

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  • Peng, Wei, 2023. "The impact of oil and natural gas prices on overnight risk in exchange rates based on the MVMQ-CAViaR models," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 616-625.
    • Handle: RePEc:eee:reveco:v:86:y:2023:i:c:p:616-625
    DOI: 10.1016/j.iref.2023.03.031
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    References listed on IDEAS

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    1. James W. Taylor, 2008. "Using Exponentially Weighted Quantile Regression to Estimate Value at Risk and Expected Shortfall," Journal of Financial Econometrics, Oxford University Press, vol. 6(3), pages 382-406, Summer.
    2. Batten, Jonathan A. & Ciner, Cetin & Lucey, Brian M., 2017. "The dynamic linkages between crude oil and natural gas markets," Energy Economics, Elsevier, vol. 62(C), pages 155-170.
    3. Jooyoung Jeon & James W. Taylor, 2013. "Using CAViaR Models with Implied Volatility for Value‐at‐Risk Estimation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(1), pages 62-74, January.
    4. Qing Xu & Terry Childs, 2013. "Evaluating forecast performances of the quantile autoregression models in the present global crisis in international equity markets," Applied Financial Economics, Taylor & Francis Journals, vol. 23(2), pages 105-117, January.
    5. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    6. Nicholas Apergis, 2015. "Money Demand Sensitivity to Interest Rates: The Case of Japans Zero-Interest Rate Policy," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 5(9), pages 1043-1049, September.
    7. Ewa Ratuszny, 2013. "Robust Estimation in VaR Modelling - Univariate Approaches using Bounded Innovation Propagation and Regression Quantiles Methodology," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 5(1), pages 35-63, March.
    8. Georgios Tsiotas, 2018. "A Bayesian encompassing test using combined value-at-risk estimates," Quantitative Finance, Taylor & Francis Journals, vol. 18(3), pages 395-417, March.
    9. Nicholas Apergis, 2015. "Money Demand Sensitivity to Interest Rates: The Case of Japans Zero-Interest Rate Policy," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 5(9), pages 1043-1049.
    10. Naser Ali Yadolahzade Tabari & Fateme Nazari & Maryam Shafiee Kakhki, 2015. "Investigating the Effect of Using Oil, Natural Gas and Coal on Economic Growth of Iran," Iranian Economic Review (IER), Faculty of Economics,University of Tehran.Tehran,Iran, vol. 19(1), pages 17-27, Winter.
    11. Chong, Zheng Rong & Yang, She Hern Bryan & Babu, Ponnivalavan & Linga, Praveen & Li, Xiao-Sen, 2016. "Review of natural gas hydrates as an energy resource: Prospects and challenges," Applied Energy, Elsevier, vol. 162(C), pages 1633-1652.
    12. Mauro Bernardi & Leopoldo Catania, 2016. "Comparison of Value-at-Risk models using the MCS approach," Computational Statistics, Springer, vol. 31(2), pages 579-608, June.
    13. Boyd, John H. & De Nicolò, Gianni & Rodionova, Tatiana, 2019. "Banking crises and crisis dating: Disentangling shocks and policy responses," Journal of Financial Stability, Elsevier, vol. 41(C), pages 45-54.
    14. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    15. Ewa Ratuszny, 2015. "Risk Modeling of Commodities using CAViaR Models, the Encompassing Method and the Combined Forecasts," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 15, pages 129-156.
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    More about this item

    Keywords

    Oil price; Overnight risk; Natural gas price;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • F31 - International Economics - - International Finance - - - Foreign Exchange

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