ABSTRACT This paper examines the drivers and impacts of extreme capital flow events in emerging markets, with a focus on distinguishing among flow types (portfolio, bank and FDI) and event categories (surge vs. stop). We find that the global financial cycle drives extreme events in portfolio and bank flows, while FDI is more sensitive to domestic factors. Notably, sudden stops in cross‐border capital flows, particularly in bank and FDI flows, have a greater impact on the overall risk interconnectedness of domestic financial submarkets compared to surge events. We also identify the transmission channels: extreme bank flow events increase credit market net risk spillovers, while concurrent extreme capital flow events heighten foreign exchange market net risk spillovers. Further discussion shows that foreign exchange sales and macroprudential policies mitigate the adverse effects of negative global financial cycle shocks, with macroprudential measures demonstrating stronger effectiveness in the medium term.

Measuring daily systemic risk with intraday data: Evidence from foreign exchange market

This policy paper examines the relationship between insecurity, foreign direct investment (FDI) inflows, and foreign exchange market performance in selected African countries that have suffered major security crises. The Dunning’s Eclectic Paradigm provides the basis for analysis. Using a mixed-methods approach, the study explores insecurity, FDI trends, and forex market outcomes in ten countries: Nigeria, Ethiopia, Kenya, South Sudan, Mali, Burkina Faso, D.R. Congo, Somalia, Mozambique, and Cameroon. Findings show that insecurity significantly undermines both FDI and forex market performance. These nations, in varying degrees, have suffered capital flight, increased forex volatility, and economic fragmentation due to insecurity. A clear adverse relationship exists between FDI inflows and insecurity: relatively stable nations like Ethiopia (pre-war) and Kenya attract more investment than highly unstable ones such as South Sudan and Somalia. Armed conflicts discourage investment, ruin infrastructure, and disrupt economic activities, hindering human and capital growth. The study contributes uniquely by offering a comparative analysis of security crises, FDI inflows, and forex markets across diverse African economies, with actionable policy recommendations. It emphasizes that no development can flourish amid violent conflict. Even stable economies face perception risks from regional instability. Addressing insecurity is therefore both a national and continental priority for sustaining FDI and healthy forex markets. All-inclusive approaches, military, institutional, diplomatic, and developmental are needed to restore investor confidence and stability. Without deliberate policy action, African countries risk continued economic underperformance, currency crises, and reliance on emergency financial interventions.

Achieving a clean and sustainable environment requires conscious and deliberate efforts from policymakers, both from technical and market perspectives. In this case, markets must be functional and less susceptible to shocks, with adequate financial resources, since green growth (GG) is a highly capital-intensive process. Therefore, this study examines the impact of positive and negative fluctuations in both the exchange rate and the interest rate on GG in China from 1990 to 2022 using the quantile-on-quantile regression approach of Sim and Zhou (2015). The results showed a negative correlation between positive fluctuations in the exchange rate and GG at the lower and middle quantiles. This outcome suggests that GG and positive fluctuations in exchange rates migrate in opposite directions. On the flip side, GG and the negative fluctuations in the exchange rate have a weak and positive relationship in some lower quantile combinations. At the lowest quantile (0.2q) of GG and interest rate, a substantial positive correlation between the two variables is seen. At the middle quantile (0.5q) of GG and interest rate, a substantial positive correlation exists between both variables. Furthermore, a substantial positive correlation between GG and positive fluctuations in interest rates was seen when the graph was examined by quantiles. Therefore, policymakers in China ought to concentrate on creating a strong foreign exchange market that can regulate the renminbi’s fluctuation in relation to other currencies.

A deep learning solution of time-fractional Fokker–Planck equation for special stochastic processes in foreign exchange market

Fuel importation continues to shape Nigeria's macroeconomic outcomes, particularly in the context of exchange rate movements and overall economic stability. Despite being a major crude oil producer, Nigeria paradoxically depends on imported refined petroleum products to meet domestic demand. This dependence places sustained pressure on the foreign exchange (FX) market, especially because fuel imports are financed largely in US dollars. Over the years, rising import bills, fluctuations in global oil prices, and structural weaknesses in the downstream petroleum sector have collectively heightened exchange rate instability. Evidence from studies conducted between 2018 and 2025 shows that fuel importation contributes to excess demand for FX, accelerates depletion of external reserves, widens the trade deficit, and amplifies naira depreciation. Recent economic reforms---including the removal of fuel subsidies in 2023--2024 and the gradual expansion of domestic refining capacity---have altered the dynamics of fuel importation. Nevertheless, FX pressures remain, reflecting long-standing structural bottlenecks. This seminar paper critically examines the relationship between fuel importation and exchange rate stability in Nigeria. Drawing from theoretical perspectives and empirical findings, the paper demonstrates how energy-sector inefficiencies translate into macroeconomic volatility. It also highlights how emerging domestic refining initiatives could support FX stability if sustainably implemented. Recommendations are provided for policymakers, regulators, and private sector actors to address Nigeria's fuel--FX imbalance in a sustainable manner.

In July 2024, the Board approved a four-year arrangement under the Extended Credit Facility to support the Ethiopian authorities’ Homegrown Economic Reform agenda (HGER), which aims to correct macroeconomic imbalances, restore external debt sustainability, and lay the foundations for high, private sector-led growth. Progress under the HGER agenda continues, with better-than-anticipated macroeconomic outcomes. The authorities continue to take steps to enhance the foreign exchange (FX) market, modernize the monetary policy framework, mobilize fiscal revenue, and advance the financial regulatory agenda. Maintaining reform momentum is essential to consolidate gains and support growth and poverty reduction in the medium term.

This empirical study explores the changing aspects of the exchange rate, AFN versus USD, in Afghanistan, over an extended period ranging from March 1, 2003, until December 31, 2020, starting with the beginning of demonetization up to the eve of a political change in Afghanistan. The study focuses on illuminating the characteristics pertinent to modelling and predicting the instability of the exchange rate. The current study used univariate time series approaches to outline the pattern of the exchange rate and the ARCH and GARCH models. Both in-sample and out-of-sample datasets were developed to test the fitness and ability of models to predict the data. The results confirm that, over the selected time period, the exchange rate exhibits persistence in volatility with a stylized fact, namely, volatility clustering. The results highlight that the standard GARCH (1,1) model is effective in capturing and predicting the volatility dynamics of AFN/USD exchange rate returns, exhibiting strong volatility clustering and persistence. The models employed in this paper are highly useful for people who are engaged in the foreign exchange market and for policymakers while formulating economic policies in Afghanistan.

In terms of business, exchange rates are crucial factors influencing a nation's economic prosperity, impacting investors, government bodies, policymakers, and various other components. This research aimed to examine the dynamics of the Nepalese rupee relative to the US dollar in the Nepalese foreign exchange market, recognising that exchange rates play a crucial role in competitiveness. The primary goal of this study was to assess the applicability of GARCH-type models, including GARCH, TGARCH, and EGARCH, for modelling the NPR-USD exchange rate using daily time series data provided by Nepal Rastra Bank. The analysis compared the results with ARIMA models. The data analysed spans from January 1, 2014, to March 30, 2024, with in-sample and out-of-sample datasets covering January 1, 2014, to September 30, 2023, and October 1, 2023, to March 30, 2024, respectively. The study also involved exploratory data analysis of the variables, which underwent diagnostic tests, including unit root and normality tests. A key finding is that all GARCH-type models indicate that historical exchange rate volatility has a significant impact on current volatility. Three models were developed and tested for diagnostic accuracy, with the Threshold GARCH model demonstrating suitability and stability. The findings concluded that negative shocks have a greater effect on volatility than positive shocks. Furthermore, this methodology is recommended for future studies and can be applied for predicting exchange rate volatility in Nepal.

Background: In recent years, cryptocurrencies have emerged as alternative financial assets, gaining increasing attention in Nigeria due to rising inflation, currency depreciation, and restrictions on foreign exchange (FX) access. Aims: The study examined the effect of foreign exchange policies' spillover on the volatility of cryptocurrency returns in Nigeria. Methods: The study utilised time series data, which consisted of United States dollars and Nigerian Naira, while the cryptocurrencies were extracted from the Nigerian foreign exchange market over a two-year period. Vector Autoregressive (VAR) was employed as a method of data analysis. Results: The findings revealed that there is a statistically significant long-run relationship between FX rates and cryptocurrency returns. The exchange rate fluctuations, reflective of FX policy shifts, generate asymmetric and persistent volatility in major cryptocurrencies, and the impact of FX policies on crypto returns varies across assets, with BTC and ETH being more responsive than USDT and SOL. Conclusions: Cryptocurrencies can no longer be viewed in isolation, as they are increasingly influenced by global and national economic decisions. Implications: There is a need to incorporate macroeconomic indicators, particularly exchange rate trends, into trading strategies to anticipate market movements and reduce risk exposure.

Background: In recent years, cryptocurrencies have emerged as alternative financial assets, gaining increasing attention in Nigeria due to rising inflation, currency depreciation, and restrictions on foreign exchange (FX) access. Aims: The study examined the effect of foreign exchange policies' spillover on the volatility of cryptocurrency returns in Nigeria. Methods: The study utilised time series data, which consisted of United States dollars and Nigerian Naira, while the cryptocurrencies were extracted from the Nigerian foreign exchange market over a two-year period. Vector Autoregressive (VAR) was employed as a method of data analysis. Results: The findings revealed that there is a statistically significant long-run relationship between FX rates and cryptocurrency returns. The exchange rate fluctuations, reflective of FX policy shifts, generate asymmetric and persistent volatility in major cryptocurrencies, and the impact of FX policies on crypto returns varies across assets, with BTC and ETH being more responsive than USDT and SOL. Conclusions: Cryptocurrencies can no longer be viewed in isolation, as they are increasingly influenced by global and national economic decisions. Implications: There is a need to incorporate macroeconomic indicators, particularly exchange rate trends, into trading strategies to anticipate market movements and reduce risk exposure.

This paper investigates the impact of RMB exchange rate fluctuations on China’s industrial stock prices using daily data from January 1, 2016, to December 31, 2022. A vector autoregression (VAR) model is constructed based on the RMB/USD exchange rate and the Shanghai Industrial Index. The empirical results indicate a long-term cointegration relationship between the exchange rate and industrial stock prices. A depreciation of the RMB tends to drive up industrial stock prices in the short term. The findings support the existence of a positive transmission mechanism from the foreign exchange market to the stock market, particularly within the industrial sector. Policy implications are discussed in the context of exchange rate reform and capital market development.

With the further advance in economic globalization and reform and opening up, macro-regulation has significant influences on China’s financial market. China’s stock market and foreign exchange market are important branches of the financial market. This paper explores the heterogeneous effects of expansionary and contractionary monetary policies on the A-share market through disparate transmission channels, including liquidity, interest rate, risk premium, and profit expectations. It analyzes how these policies influence market returns, volatility, and valuation levels across different industries, with highly leveraged and high-beta sectors being more responsive. Additionally, the paper examines the pathways and extents of monetary policy impacts on the RMB exchange rate level, volatility, and risk premium, highlighting the role of interest rate differentials, market expectations, and capital flows. Furthermore, it proposes corresponding policy recommendations and improvement methods to enhance the effectiveness of macro-regulation and maintain financial stability. The findings provide valuable insights for policymakers and investors in navigating China’s complex financial environment.

ABSTRACT This paper explores whether the information from the stock market can provide positive contents for the implied volatility prediction in the crude oil market, gold market, and foreign exchange market. Specifically, we investigate the predictive effects of realized continuous volatility, realized jump volatility, positive and negative realized semi‐variations, and signed jumps from the S&P 500 index on three implied volatility indices, OVX (Crude Oil Volatility Index), GVZ (Gold Volatility Index), and EVZ (Euro Volatility Index). We construct a hybrid method by combining parametric models with machine learning to explore the market spillover effects of stock market information on three markets. The empirical results show that realized measures in the stock market can provide incremental information for the prediction of the implied volatility indices, the positive and negative semi‐variations of stock index showing better performance than that of jump volatility. The method of combining FNN with the parametric model shows better performance compared to SVR. The superiority of this hybrid approach is further verified based on the Model Confidence Set test. Furthermore, an economic significance evaluation confirms that the enhanced predictive accuracy translates into significant economic value.

This research paper delves into the transformation and growth of the Foreign Exchange (Forex) Market in India post-pandemic, with a specific focus on the evolving trends, participation patterns, and regulatory dynamics. The study is based on insights gained during an internship at a leading treasury advisory and forex risk management firm in India. The COVID-19 pandemic disrupted global trade and financial markets, including the foreign exchange (forex) market. However, post-2021, the Indian forex market has witnessed renewed momentum driven by increased retail investor participation, digital transformation, and higher foreign capital inflows. This study investigates the structural changes and growth trajectory of the forex market in this post-COVID landscape.

This study examines the impact of the Trump 2.0 economic policy, particularly the implementation of high protectionist import tariffs, on the stability of Indonesia’s banking system. Using qualitative descriptive analysis supported by secondary data, this research explores the transmission channels of US policy affecting credit risk, liquidity, exchange rate volatility, and capital flows in Indonesia. The findings indicate that the tariff policy exerts significant pressure on the export sector and potentially raises Non-Performing Loan (NPL) risks, although banking stability remains relatively resilient due to mitigation responses from the Financial Services Authority (OJK) and Bank Indonesia. Macroprudential policies and foreign exchange market interventions are key to maintaining banking liquidity and capital strength amid global uncertainties. The study recommends strengthening policy coordination and export market diversification as sustainable mitigation strategies

The supervision of and early warning about cross-border capital flows are crucial for maintaining financial stability. This study develops a sustainable risk warning framework that incorporates the heterogeneous exchange rate expectations of foreign exchange market participants into a comprehensive indicator system. Using the KLR signal analysis method and data for China covering the period from July 2005 to June 2022, the framework is empirically evaluated for its ability to predict short-term capital inflow and outflow risks. The results show that incorporating heterogeneous expectations significantly enhances the accuracy and robustness of early warning performance. Regardless of the specific estimation method, the proposed Weighted Heterogeneous Expectation Indicator demonstrates stable and effective predictive capacity across different market environments, underscoring its time-varying adaptability and robustness. Early warning indicators exhibit varying sensitivities, highlighting the importance of a holistic assessment that captures multiple market dimensions. Overall, the proposed sustainable framework strengthens the monitoring of short-term cross-border capital flow risks in China and provides methodological insights for improving risk warning systems in other economies.

Introduction. Most authors considered the use of only binary logic and technical analysis, which does not allow for effective consideration of market uncertainty and rapid dynamic. Other researchers considered the use of fuzzy logic, but these studies are limited to local markets or do not provide integration with more flexible types of analysis such as ML. It was also found that no comparative analysis of the effectiveness of different logical approaches (fuzzy, classical, probabilistic logic) is conducted, which creates a gap in the scientific justification of the choice of a particular method. The potential of multi-timeframe analysis is also practically not taken into account, although it can increase the accuracy and stability of decisions made. The above indicates the need for a comprehensive study that would combine the advantages of various logical approaches, machine learning and multi-timeframe analysis within a single hybrid DSS. This would also allow a reasonable approach to the choice of a specific method. Research objective. The aim of this work is to develop multi-timeframe hybrid DSS for algorithmic trading based on fuzzy and classical binary logic with probabilistic elements. This will make it possible to increase the efficiency of algorithmic trading systems. Results. The study consisted in the development of multi-timeframe hybrid DSS based on binary and fuzzy logic with probabilistic elements, as well as their comparative analysis. As a source of signals for further decision-making, the system uses forecasts made by the Random Forest model. Cross-Validation was used to train the model to predict not only the opening, maximum, minimum and closing values ??of the position (Open, High, Low, Close – OHLC), but also the level of confidence of these predictions. The Mamdani fuzzy logic system [13, 14] was used as a fuzzy logic system for DSS. Both DSS were implemented in the MQL5 programming language. The backtest was carried out on the MT5 platform. As a result, the decision support system based on fuzzy logic showed a significant advantage over the decision support system based on classical binary logic with a Win Rate of 60.81%, and an annual return of 58% and a Sharpe ratio of 1.33. While the decision support system based on binary logic showed the following results: Win Rate of 34.16%, and an annual return of –95.46% and a Sharpe ratio of –5. An applied aspect of using the obtained scientific result is the possibility of improving DSS for making trading decisions. Conclusions. The study showed that multi-timeframe hybrid DSS based on fuzzy logic with probabilistic elements allows making more effective decisions than DSS based on binary logic. This study allows for a reasoned approach to choosing a specific method. In addition, the proposed methodology and constructed models can be used by other researchers in the field of financial technologies for the further development of decision support systems in financial markets. Future research will be aimed at improving time series forecasting methods in order to improve the quality of input signals for the trading system. Keywords: algorithmic trading, FOREX, Machine Learning, fuzzy logic, Mamdani.

This study examines the dynamics of structural breaks in Turkey's foreign exchange market and exchange rate regimes using the index of foreign exchange market pressure (EMP) from January 2006 to October 2024. The low negative correlation between the nominal exchange rate and central bank reserves suggests a managed exchange rate regime during this period. The cointegration model with structural breaks identifies three key breaks: November 2008 (global financial crisis), February 2012 (European debt crisis), and February 2022 (post-pandemic economic transformation). From January 2006 to October 2008, a market-oriented floating exchange rate regime was in place. After November 2008, foreign exchange market interventions increased due to the global crisis, deviating from the floating exchange rate regime. Between February 2012 and January 2022, EMP peaked, and policies resembling a fixed exchange rate regime were pursued. In the most recent period (February 2022 to October 2024), foreign exchange interventions decreased, but exchange rate uncertainties remained. Furthermore, post-October 2024, the Japanese Yen was not used for arbitrage. Policy recommendations include strengthening the floating exchange rate regime, better management of foreign exchange reserves, and using reserves only during crises.

Economic momentum remains strong despite continued global uncertainty and inflation is largely receding. Fiscal performance during the first half of 2025 exceeded expectations and strong tax revenue collection is expected to continue through the end of the year. The foreign exchange market continues to function smoothly, and foreign reserves remain at a comfortable level. Structural reforms are advancing at a higher pace. The economic outlook is subject to large downside risks owing to global geopolitical tensions and uncertainty. Exposure to climate risks is significant, primarily through The Gambia’s low elevation and reliance on rain-fed agriculture.