Attempts have been made in this research to forecast returns of exchange rates of foreign countries in relation to Naira using the SVM, Neural Network (NN), and Random Forest (RF) forecasting models. The Value-at-Risk and Expected Shortfall results demonstrate that exchange rate risks intensified significantly during the post-crisis period. USD/NGN exhibited the highest post-crisis tail risk under the Neural Network model, with VaR₉₉ and ES₉₉ values reaching 1.2211 and 1.3003 respectively, indicating extreme downside exposure and elevated currency market fragility. Similarly, EUR/NGN and CAD/NGN recorded heightened post-crisis risk levels, reflecting increased investor uncertainty and inflationary exchange rate pressures. By contrast, the RF model generated more moderate and economically plausible risk estimates, suggesting stronger robustness and stability in volatile emerging market environments. Graphical analyses corroborate these findings, showing that Neural Network forecasts produced explosive and exponential depreciation trajectories in the post-pandemic era, while RF forecasts exhibited smoother and more gradual adjustment paths consistent with managed exchange rate dynamics. The study found consistently higher post-crisis VaR and ES values across models signal rising tail risks, which imply potential for large currency swings. Such volatility could exacerbate macroeconomic fragility, increase the cost of external debt servicing, and drive inflationary pressures through more expensive imports. Across all models, the RF system consistently delivered superior predictive accuracy, forecast stability, and tail-risk moderation particularly during the crisis and post-crisis periods. In contrast, the NN model produced exponential post-crisis forecast trajectories especially for USD/NGN and EUR/NGN; highly sensitive to structural breaks and may exaggerate persistent volatility in crisis-prone economies. Though informative, such outputs tended to overshoot plausible devaluation trends for the Naira, likely due to exaggerated extrapolation of recent market behaviors. SVM forecasts showed modest error levels and smoother, more plausible trends, offering less extreme but significant signals of future currency devaluation. This reinforces the importance of hybrid modeling approaches and the integration of non-linear machine learning tools in forecasting into central banking operations, exchange rate surveillance frameworks, and investor risk assessment strategies under turbulent economic conditions to enhance resilience against future external shocks and currency market disruptions.

This study evaluates exchange rate dynamics between the Naira and global currencies, utilizing weekly data from 2008 to 2024. The exchange rates, NGN/USD, NGN/CAD, NGN/AUD, NGN/EUR, and NGN/JPY were analyzed to explore the impact of macroeconomic determinants such as interest rate differentials, market volatility, and inflation rate differentials on exchange rates. The study employed ARIMA regression, and the wavelength techniques. The results climax the nuanced interplay between global financial flows and local economic conditions in determining exchange rates of the Naira against global currencies. The result on inflation differential aligns with the purchasing power parity (PPP) theory, which suggests that currencies adjust to offset inflation disparities. The market volatility result partly implies that periods of heightened market turbulence tend to favor the EUR, driving up the demand for the Euro and subsequently appreciating its value against the Naira. This finding aligns with traditional financial theories that associate risk-aversion behavior with movements towards stronger, less volatile currencies during times of market stress. The results of the study validate the interest rate parity theory, which posits that higher interest rates in one country attract foreign capital, influencing exchange rate dynamics. The detail level estimates of the decomposition component of wavelet results underscore the relevance of micro-level factors and short-lived fluctuations that may be influenced by weekly market activities, speculative trading, or sudden external shocks. These findings imply that the dynamics of exchange rates are complex, with both prolonged patterns and transient oscillations influencing the currency's overall movement. So, a comprehensive understanding of exchange rate behavior requires consideration of both the broader macroeconomic environment and the micro-level, weekly variations that induce volatility in the currency markets. The research findings emphasize the importance of stable monetary policies to manage currency fluctuation risk. These findings have significant implications for policymakers, financial institutions, and investors in the context of managing exchange rate risk in volatile markets.

In this study, we examined the volatility trend of stock return in eight ASEAN stock markets. These includes the Singapore Exchange (SGX), Bursa Malaysia Stock Exchange (YSX), the Stock Exchange of Thailand (SET), Indonesia stock exchange, the Vietnam Stock Exchange (VNX), the Cambodia Securities Exchange (CSX), the Lao Securities Exchange (LSX), and the Philippine Stock Exchange. Secondly, we evaluated the factors that influence the level of return in those stock markets with exchange rate volatility as a control variable. By employing FIGARCH-DCC and ARDL models, the study aimed to provide a more robust understanding of stock market dynamics. The findings reveal significant negative returns effect of market volatilities and liquidity crisis in all the stock exchanges of all sample countries in the study. In Singapore, money supply variation, market volatility, liquidity risks, and exchange rate volatility significantly influenced stock returns positively. The short-run model explains 52.26% of the variation in stock returns. Only in Malaysia, we had significant positive returns from exchange rate volatility. Nevertheless, the Russian model explains just 22.22% of the variation in stock returns. In Thailand and Indonesia alike, returns significantly and positively responded to variation in money supply, while the volatility in the market and currency rate exchange adversely impacted returns. The short-run models explain 53.66% and 65.21% of the variation in stock returns for Vietnam and Indonesia, respectively. The variation in money supply does not significantly affect stock returns and has no significant contribution to returns in Cambodia. The Cambodia model explains around 48.34% of the variation in returns. For Lao Stock Exchange, return effects of liquidity risk, and exchange rate instability were significant and negative. Market volatility had insignificantly impacted stock returns in Nigeria. The Lao model explains 50.38% of the variation in stock returns. In the Philippine Stock Exchange, the returns effect of exchange rate volatility and liquidity crisis are adverse and significant. Money supply variation and market volatility had insignificant influence on returns. The model explains 68.11% of the variation in returns. In the Philippines, market volatility, liquidity risks, and exchange rate volatility adversely impacted returns. Money supply variation had no such significant influence on returns. The panel model of the Philippines explains 62.9% of the variation in stock returns. The research accentuates the need for governments to stabilize exchange rates, boost liquidity, through targeted policies aimed at managing stock market dynamics especially as it relates to stock volatility in order to foster meaningful growth and development of the financial market.

Research on stock exchange markets is essential to stock market investors as it offers sensitivities to risk management. This research investigates the patterns of the volatility of stock market prices in ten African stock markets. We estimated the dynamic GARCH model of Engle using the method of maximum likelihood estimation. Daily time series from January 1, 2021 to December 30, 2022, were obtained from African Stock (Securities) Exchange database. The findings established the existence of a normally distributed Senegalese stock market as against time-varying volatility of stock prices in Nigeria, Ghana, Mali, Burkina Faso, Togo, Niger Republic, Benin Republic, Ivory Coast, and Gambian. Hence, the likelihood that an asset or stock is being overpriced (overvalued) or underpriced (undervalued) in the Senegal stock market is low. It is therefore easier for stock traders and investors in Senegal to pick entry and exit points. Unfortunately, this cannot be said of the investors in stock markets of other countries. In effect, the closing price of a stock is most often heavily deviated with significant outliers. This further infers that variations of stock prices in these markets are very wide, heavy, and unpredicted. Hence, it is a case of the volatility of volatilities.