Analysis of Value-at-Risk (VaR) of Naira against BRICS Currencies

Authors

DOI:

https://doi.org/10.29015/cerem.1028

Keywords:

Variance-Covariance methodology, Monte-Carlo Simulation (MCS), Historical-Simulation (H-S), VaR, Variance-Covariance methodology, Monte-Carlo Simulation (MCS), Historical- Simulation (H-S), BRICS currencies, VaR, Naira Exchange Rate

Abstract

Aim: This study investigates foreign exchange market dynamics by forecasting and analyzing the Value-at-Risk (VaR) for the Nigerian Naira against BRICS currencies utilizing daily data from January 1, 2010 to December 31, 2024.

Design/Research methods: The five BRICS currencies (BRL, RUB, INR, CNY, and ZAR), were analyzed to explore the impact of foreign exchange market dynamics on the Nigerian Naira against BRICS currencies. The value-at-risk methodology was implemented plus the Monte Carlo simulation. The calculated VaR95% quantifies potential losses, emphasizing the importance of managing downside currency exchange risks in a volatile financial market at both the 95% and 99% confidence thresholds. The robustness of the Monte-Carlo simulation (MCS) and historical simulation (H-S) results validates the conditional variances and the corresponding value-at-risk estimates for the Naira exchange rate in relation to each currency of the BRICS derived from the variance-covariance model. GJR-GARCH model reveals critical insights into the valuation and volatility risk associated with the Naira exchange rate against BRICS currencies.

Findings: The valuation of the Naira/Real rate has significant vulnerabilities to changes in oil prices, external debt, and changes in money supply; the results show that the Naira/Rubble exchange rate had significant and negative responsiveness to changes in output growth, crude oil prices and external debt levels; valuation of the Naira/Rupee exchange rate is significantly responsive to the vulnerability of trade balance, external reserves, foreign debt, monetary policy rate, and crude oil prices; valuation of the Naira/Yuan exchange rate has significant vulnerabilities to changes in oil prices, output growth, external debt, and CBN policy rate; valuation of the Naira/Rand has significant vulnerabilities to changes in external reserves, financial healthiness and external debt levels.

Originality / value of the article: The study findings are robust explanation of asymmetric risk identified by VaR with policy advice for the CBN to strategically rebalance its exposure to BRICS currencies by using risk-weighted analysis instead of just trading volumes. Also, the study contributed to prediction of possible losses associated with unfavorable Naira currency fluctuations when trading particularly with the BRICS, and so emphasized the necessity for the adoption of VaR-based stress testing to national foreign exchange reserves and financial institutions. Conclusion: Given the asymmetric risk, the CBN should intentionally rebalance its exposure to BRICS currencies by using risk-weighted analysis instead of just trading volumes. For example, the CBN ought to look at establishing more local currency settlement agreements with the BRICS countries. This could reduce exposure to the volatility of the US dollar and dependence on it.

Keywords: Variance-Covariance methodology, Monte-Carlo Simulation (MCS), Historical-Simulation (H-S), BRICS currencies, VaR, Naira Exchange Rate

JEL: B23, D25, C17.

References

Adeniran A. A., Popoola M.I. (2020), The Impact of China’s Economic Policies on Nigeria’s Foreign Exchange Market, “African Economic Policy Review”, vol. 12 no. 1, pp. 78–92.

Ahmed F., Patel R., Khan M. (2021), Application of Extreme Value Theory (EVT) in Estimating Value-at-Risk (VaR) for the NGN/USD Exchange Rate, “Journal of Financial Risk Management”, vol. 12 no. 4, pp. 345–367.

Ali M., Aziz S., Tariq M. (2023), Ensemble Learning Algorithms for Value-at-Risk (VaR) Estimation of the PKR/USD Exchange Rate, “Journal of Emerging Market Finance”, vol. 15 no. 3, pp. 256–279.

Ateba J., Ewondo D., Abega D.A., Enama A., Abomo Zang J.C. (2024), Analysis of the Heterogeneity Impact of Oil Price Shocks on Exchange Rates in Oil Exporting Countries: Evidence of Quantiles-on-Quantiles Approach, “International Journal of Advanced Economics”, vol. 6 no. 11, pp. 652–677, https://doi.org/10.51594/ijae.v6i11.1712

Bagchi B., Dandapat D., Chatterjee S. (2016), Dynamic Linkages and Volatility Spillover: Effects of Oil Prices on Exchange Rates, and Stock Markets of Emerging Economies, Emerald Publishing, Leeds.

Bamidele K. (2024). Impact of Exchange Rate Volatility on Foreign Portfolio Investment in Nigeria (1986–2023), SSRN 4954853.

Bank for International Settlements (2022), Triennial Central Bank Survey of Foreign Exchange and Over-the-Counter Derivatives Markets in 2022. BIS.

Bouslama N. (2023), Interdependence between the BRICS Stock Markets and the Oil Price since the Onset of Financial and Economic Crises, “Journal of Risk and Financial Management”, vol. 16 no. 7, 316. https://doi.org/10.3390/jrfm16070316.

Chen S., Zhang W., Li X. (2019), Estimating Value-at-Risk (VaR) for the EUR/USD Exchange Rate Using GARCH Models under Varying Volatility Regimes, “International Review of Financial Analysis”, vol. 63, pp. 72–87.

Chinwe A.N., Rotimi O., Awa F.N., Arisi-Nwugballa E.A. (2024), An Empirical Analysis of Causative Effect of Lingering Naira Devaluation on Nigerian Economy, “Certified National Accountant Journal”, vol. 32 no. 1, pp. 31–49.

Dlamini S., Ndlovu T., Khumalo J. (2024), Hybrid GARCH and Extreme Value Theory (EVT) Models for Value-at-Risk (VaR) Estimation: A Case Study of the ZAR/USD Exchange Rate, “Journal of Risk Management in Emerging Economies”, vol. 8 no. 2, pp. 132–150.

Efuntade O.O., Efuntade A.O. (2023), Oil Price-Exchange Rate Interdependence: Relevance of Theory of Exchange Rate Overshooting in Nigeria, “International Journal of Economics and Financial Management”, vol. 8 no. 2, pp. 75–91, https://doi.org/10.56201/ijefm.v8.no2.2023.pg75.91

Ezinwa U., Anyanwu E. (2019), The Russian Ruble’s Impact on Nigeria’s Economy: An Analysis, “International Journal of Emerging Markets”, vol. 5 no. 4, pp. 274–287.

Fernandes A. (2024), Impact of Foreign Direct Investment (FDI) on Economic Growth: A Study of Brazil, “International Journal of Economics”, vol. 9, pp. 31–41, DOI: 10.47604/ijecon.2442.

FEWS NET (2016), Further Nigerian and Regional Market Impacts Expected with the Floating of the Naira, http://www.fews.net/west-africa/alert/june-2016.

Fisera B., Tiruneh M.W., Hojdan D. (2021), Currency Depreciations in Emerging Economies: A Blessing or a Curse for External Debt Management?, “International Economics”, vol. 168, pp. 132–165.

Ghauri S.P., Ahmed R.R., Streimikiene D., Qadir H., Hayat A. (2024), Macroeconomic Factors Driving Exchange Rate Volatility and Economic Sustainability: Case Study of Pakistan, “Amfiteatru Economic”, vol. 26 no. 66, pp. 612–628.

Gupta R., Patel P. (2020), Monte Carlo Simulation for Value-at-Risk (VaR) Estimation of the INR/USD Exchange Rate, “Journal of Computational Finance”, vol. 19 no. 6, pp. 104–125.

Hasan S.M. (2024), Does Islamic Mutual Fund Bear Higher Risk than Conventional Mutual Funds? An Empirical Analysis from Bangladesh: Islamic Mutual Fund vs. Conventional Mutual Fund, “Jahangirnagar University Journal of Business Research”, vol. 24 no. 01, pp. 43–62.

Hassan S., Alam M. (2021), Predicting the Exchange Rate of the Pakistani Rupee (PKR) against the US Dollar Using the ARIMA Model, “International Journal of Financial Studies”, vol. 29 no. 4, pp. 211–228.

Hashmi S.M., Chang B.H., Huang L., Uche E. (2022), Revisiting the Relationship between Oil Prices, Exchange Rate, and Stock Prices: An Application of Quantile ARDL Model, “Resources Policy”, vol. 75 art. 102543, https://doi.org/10.1016/j.resourpol.2021.102543

Ibrahim T., Yusuf A., Odunayo M. (2020), Foreign Exchange Market Developments and Macroeconomic Stability in Nigeria: Policy Implications, “African Journal of Policy Analysis”, vol. 18 no. 2, pp. 112–129.

Iwedi M. (2023), Monetary Policy and Macroeconomic Volatility in Nigeria, “Journal of Money, Banking and Finance”, vol. 8 no. 2, pp. 119–139.

Johnson L., Roberts D. (2024), Ensemble Learning for Value-at-Risk (Var) Estimation of the GBP/USD Exchange Rate: A Comparison of Boosting and Bagging Methods, “Journal of Financial Technology”, vol. 11 no. 1, pp. 98–117.

Khan M., Teng J.-Z., Khan M.I. (2019), Cointegration between Macroeconomic Factors and the Exchange Rate USD/CNY, “Financial Innovation”, vol. 5 art. 5, DOI: 10.1186/s40854-018-0117-x.

Kim S., Choi M. (2021), Deep Learning Models for Value-at-Risk (VaR) Estimation of the USD/KRW Exchange Rate: An Application of LSTM Networks, “Journal of Financial Data Science”, vol. 3 no. 5, pp. 215–230.

López G., Martinez S. (2022), Quantile Regression for Value-at-Risk (VaR) Estimation of the MXN/USD Exchange Rate, “Journal of Quantitative Finance”, vol. 19 no. 7, pp. 188–203.

Markowitz H. (1952), Portfolio Selection, “The Journal of Finance”, vol. 7 no. 1, pp. 77–91.

Mpofu T.R. (2021), The Determinants of Real Exchange Rate Volatility in South Africa, “The World Economy”, vol. 44 no. 5, pp. 1380–1401.

Müller J., Schneider P., Weber M. (2022), Component GARCH Models for Value-at-Risk (VaR) Estimation of the CHF/USD Exchange Rate, “Journal of Volatility and Risk”, vol. 17 no. 2, pp. 101–118.

Obi A.D., Olayemi M.O., Akinyemi A.A. (2022), Modern Portfolio Theory and Currency Portfolio Optimization in Volatile Markets, “Journal of Investment Strategies”, vol. 33 no. 2, pp. 101–117.

Ofori E., Mensah K. (2023), Historical Simulation for Value-at-Risk (VaR) Estimation of the NGN/USD Exchange Rate, “Journal of Risk and Financial Markets”, vol. 14 no. 8, 265–280.

Ohaegbulem E.U., Iheaka V.C. (2024), The Impact of Macroeconomic Factors on Nigerian-Naira Exchange Rate Fluctuations (1981–2021), “Asian Journal of Probability and Statistics”, vol. 26 no. 2, pp. 18–36.

Okafor D., Adeyemi F. (2022), A Comparative Analysis of Monte Carlo Simulation and ARIMA in Forecasting the NGN/USD Exchange Rate, “Journal of Emerging Market Economies”, vol. 12 no. 1, pp. 89–104.

Olu T., Anu A. (2019), Investigating the Determinants of Exchange Rate Fluctuations in Nigeria: A Structural Approach, “Global Journal of Economics and Management”, vol. 11 no. 1, pp. 87–105.

Oseni U. (2020), The Influence of the Indian Rupee on Nigeria’s Economic Performance: A Trade Perspective, “Journal of International Trade and Economic Development”, vol. 13 no. 1, pp. 77–90.

Owuru J.E., Olabisi E.O. (2023), Dynamic Response of Emerging Market Stock Returns to Exchange Rate and Oil Price: A Case of Nigeria, “Romanian Journal of Economics”, vol. 57 no. 2, pp. 114–130.

Oyetade S., Uche C., Nwachukwu N. (2019), Forecasting Exchange Rates: Evidence from Advanced and Emerging Economies, “International Journal of Forecasting and Economics”, vol. 8 no. 2, pp. 101–119.

Ren X., Li Z., Sun Y. (2021), Monte Carlo Simulations for Risk Management in Foreign Exchange Markets, “Journal of Computational Finance”, vol. 24 no. 3, pp. 145–169.

Shayya R., Sorrosal-Forradellas M.T., Terceño A. (2023), Value-at-Risk Models: A Systematic Review of the Literature, “Journal of Risk”, vol. 25 no. 4.

Silva T., Pérez L., Ferreira J. (2021), Hybrid GARCH-ANN Models for Estimating Value-at-Risk (VaR) of the BRL/USD Exchange Rate, “Journal of Hybrid Modeling in Finance”, vol. 23 no. 1, pp. 43–60.

Singh A., Kumar S. (2024), Wavelet Transforms for Value-at-Risk (VaR) Estimation of the INR/USD Exchange Rate, „Journal of Financial Time Series”, vol. 22 no. 4, pp. 167–182.

Wahab B.A. (2024), Trade-Related Infrastructure and Bilateral Trade Flows: Evidence from Nigeria and Its Trading Partners, “Journal of Economic Structures”, vol. 13 no. 1, 13.

Wang Y., Zhang C., Wu Q. (2020), Copula-Based Value-at-Risk (VaR) Estimation for the CNY/USD Exchange Rate, “Journal of Financial Econometrics”, vol. 8 no. 6, pp. 401–417.

Williams D., Green M., Thompson K. (2020), Predictive Accuracy of Monte Carlo Simulation in Exchange Rate Forecasting: A Study of the BRICS Economies, “Journal of Emerging Market Finance”, vol. 17 no. 2, pp. 184–198.

Zhang X., Lee K., Han J. (2022), Integrating SVM with Monte Carlo Simulation for Value-at-Risk (Var) Estimation of the JPY/USD Exchange Rate, “International Journal of Financial Engineering”, vol. 20 no. 9, pp. 451–469.

Zhao J., Liu L., Wu F. (2024), Integrating Monte Carlo Simulation with Agent-Based Models for Forecasting the USD/CNY Exchange Rate, “Behavioral Economics and Financial Modeling”, vol. 8 no. 1, pp. 75–90.

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Published

2025-06-27