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

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.