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PurposeThe purpose of this study is to investigate the effects of uncertainty, namely, macroeconomic uncertainty (MU) and financial uncertainty (FU) on foreign exchange market stability, specifically on foreign exchange market pressure (EMP) and jump risk (RJV).Design/methodology/approachThe latent threshold time-varying parameter VAR (LT-TVP-VAR) econometric approach is used in estimations to solve structural breaks.FindingsThe relationship of uncertainties and China's foreign exchange market stability is latent threshold nonlinear dynamic time-varying. In China's renminbi (RMB) appreciation stage, both MU and FU weaken the appreciation pressure of RMB. Moreover, MU and FU significantly increase the RJV, while MU significantly affects the RJV of the foreign exchange market. In the RMB depreciation stage, both MU and FU strengthen the EMP.Research limitations/implicationsFindings based on data in China's foreign exchange market can be considered for other global markets in future research.Practical implicationsAn increase in MU and FU has a negative effect on foreign exchange stability. Regulators can prevent the economic system uncertainty shocks on foreign exchange market stability through observation and judgment of MU and FU, which helps prevent and relieve financial risks. Investors can reduce foreign exchange risk as the exchange rate rebounds after hedging behavior during high uncertainty periods.Originality/valueThe effect of MU on the foreign exchange market stability is greater than that of FU, regardless of whether EMP or RJV occurs in the foreign exchange market.

Orientation This study investigated the relationship between the equity markets and foreign exchange markets in Brazil, Russia, India, China and South Africa (BRICS). Research purpose This study examined the financial connectedness through volatility spillovers and co-movements among equity and foreign exchange markets in the BRICS countries to better understand market interdependencies. Motivation for the study The literature mainly focused on volatility transmission from developed countries. Research approach This research, used the Diebold and Yilmaz spillover index approach (DY index). The DY index is based on variance decompositions (VD) and impulse response functions that use a vector autoregressive (VAR) modelling framework. The study period was from 02 January 1997 to 31 December 2018. Main findings Shocks from the equity markets dominate the foreign exchange markets, while foreign exchange markets dominate their equity markets at an individual level. There are interdependencies between BRICS equity markets and foreign exchange markets, except for China, whose markets are relatively isolated from other BRICS markets. Brazil is the largest contributor of volatility spillovers to other BRICS markets. The South African rand is the most integrated within BRICS. Practical implications Foreign exchange markets provided better diversification opportunities. Significant increases in volatility spillovers associated with turmoil periods in domestic and global markets provide evidence for contagion effects in BRICS markets. Contribution The current account (flow-oriented model) is crucial in exchange rate determination at the individual country level. In contrast, the capital account (stock-oriented model) is a significant factor in exchange rate determinations at an aggregate level.

Based on intraday high-frequency data, this paper investigates the effect of sterilized interventions on the Slovak koruna/euro exchange rate for different time windows during a period that coincides with Slovakia’s preparation for EU accession and euro adoption. Results confirm a significant relationship between intervention and exchange rate change. The maximum effect of intervention is reflected in the exchange rate change within a couple of hours, and the effect over longer time windows weakens only gradually. The initial impact of sale interventions is stronger than that of purchase interventions.

In the present paper, we investigate the complex dynamics arising from a behavioral exchange rate discontinuous model with heterogeneous agents. Unlike previous works explaining the emergence of chaos in the exchange rate models as the resulting of nonlinearity, our model is able to produce endogenous exchange rate dynamics due to the presence of discontinuity induced by a sentiment index, which affects the way investors take their trading decisions. In particular, it affects the level of optimism/pessimism of fundamentalists regarding their perception on the value of fundamental. Moreover, it also affects the strength to which one kind of chartists places her buying/selling orders. We show that our model, represented by a two-dimensional discontinuous map, has the ability to produce interesting endogenous exchange rate dynamics. In addition, when each component of the map is buffeted by a stochastic component, the model closely replicates the stylized facts of the EUR/USD and EUR/JPY exchange rate markets.

Recently there has been an exponential increase in the use of artificial intelligence for trading in financial markets such as stock and forex. Reinforcement learning has become of particular interest to financial traders ever since the program AlphaGo defeated the strongest human contemporary Go board game player Lee Sedol in 2016. We systematically reviewed all recent stock/forex prediction or trading articles that used reinforcement learning as their primary machine learning method. All reviewed articles had some unrealistic assumptions such as no transaction costs, no liquidity issues and no bid or ask spread issues. Transaction costs had significant impacts on the profitability of the reinforcement learning algorithms compared with the baseline algorithms tested. Despite showing statistically significant profitability when reinforcement learning was used in comparison with baseline models in many studies, some showed no meaningful level of profitability, in particular with large changes in the price pattern between the system training and testing data. Furthermore, few performance comparisons between reinforcement learning and other sophisticated machine/deep learning models were provided. The impact of transaction costs, including the bid/ask spread on profitability has also been assessed. In conclusion, reinforcement learning in stock/forex trading is still in its early development and further research is needed to make it a reliable method in this domain.

This study explores how uncertainties in domestic financial markets, including stocks, bonds, and exchange rates, as well as global crises like the subprime mortgage and European sovereign debt crises, affect the dynamic correlation between the Australian stock market index (ASX 300) and foreign exchange rates. Using quantile regression estimation and analyzing high-frequency data from 1999 to 2021, we uncover distinct relationships influenced by currency-specific uncertainties. Our findings reveal that Australian stock market concerns impact co-movements with financial instruments, showing different effects on the correlations with the Euro, British Pound, US Dollar, Japanese Yen, and Chinese Yuan. Surprisingly, uncertainties in the Australian bond market have a negative impact on co-movement with the Euro and British Pound but a positive impact with the US Dollar, Japanese Yen, and Chinese Yuan. Additionally, we observe that volatility in the Australian currency’s exchange rate with various currencies positively influences dynamic co-movements. However, the strength of this connection varies based on the volatility of the Australian dollar against the Japanese Yen. Global financial crises, especially the subprime mortgage crisis, significantly impact dynamic co-movements, supporting both Flow and Stock-oriented theories. In summary, our research sheds light on the diverse impacts of domestic financial market uncertainties on co-movement, providing valuable insights for portfolio managers and foreign investors aiming to understand the intricate relationship between the Australian stock market and exchange rates. JEL Classification: C32, F31, G01, G15 Plain language summary How financial market uncertainty and crises affect the relationship between stocks and foreign exchange Study focuses on stock market and foreign exchange rate correlation under market uncertainties. Utilizes quantile regression to analyze impact of domestic financial factors and global crises. Uncertainties in Australian stock market negatively influence correlation. Uncertainties in Australian bond market negatively affect Euro and British Pound, but positively impact US Dollar, Japanese Yen, and Chinese Yuan. Australian exchange rate uncertainties positively impact correlation with Euro, US Dollar, British Pound, and Chinese Yuan; negatively with Japanese Yen. Results support both Flow and Stock-oriented models, revealing varied effects of domestic financial uncertainties. Global financial crises, particularly subprime mortgage crisis, negatively influence dynamic co-movement. Validity of Stock-oriented model confirmed, indicating uniform negative effects of global uncertainties on dynamic correlation.

This study examines the long memory properties in the volatility of the foreign exchange markets of Egypt, Ghana, Kenya, Nigeria and South Africa. Applying the FIEGARCH model to daily data from June 2, 1997, to December 31, 2021, we find long memory in the second moment of return innovations across all five countries' foreign exchange markets and significant first-order positive autocorrelation. To isolate spurious long memory, we perform a structural break test and find that structural breaks in all five foreign exchange markets do not affect long memory. The findings may have implications for risk management. Historical volatility-based investment methods can generate risk-adjusted returns innovations. Long memory may indicate unexploited profit for risk-seeking speculators and international investors in these countries' financial assets. Also, official intervention should be random and rule-changing to reduce currency market predictability.

This study employs the feature ranking network method to investigate the foreign exchange (FX) market to uncover the underlying structural transition by observing the dependencies and stability of currencies. For this purpose, the FX market’s time series of 50 currencies is examined from January 2020 to October 2023 against the US dollar, covering the COVID‐19 pandemic and the Russia–Ukraine war. Using the random forest regressor, the feature ranking matrix is determined by utilizing the returns of currencies on a given day to predict the feature ranks for the following day. The dependency network is constructed using the threshold method, revealing that the topological properties of the networks undergo significant changes, especially during the war. Asian currencies grab the central positions of the dependency network, indicating their high reliance. We select four representative currencies to provide a clearer and more focused analysis of currency dependency, stability, and entropic trends. It is observed that the war triggers instability in currencies and increases the developing countries’ currency dependence. The global entropy increases with minor fluctuations during the war, and a sharp decline in entropy was observed at the beginning of 2023, indicating an extremely high dependence of the currencies of Russia (RUB), the Philippines (PHP), and Bangladesh (BDT) on others. For comparative analysis, we discuss the topological properties of the EUR‐based network alongside those of the USD‐referred market. The proposed dependency network–based analytical framework provides valuable and sustainable insights for observing currency resilience and contagion in pandemic and geopolitical events.

Originally published in 1984. This book examines two important dimensions of efficiency in the foreign exchange market using econometric techniques. It responds to the macroeconomics trend to re-examining the theories of exchange rate determination following the erratic behaviour of exchange rates in the late 1970s. In particular the text looks at the relation between spot and forward exchange rates and the term structure of the forward premium, both of which require a joint test of market efficiency and the equilibrium model. Approaches used are the regression of spot rates on lagged forward rates and an explicit time series analysis of the spot and forward rates, using data from Canada, the United Kingdom, the Netherlands, Switzerland and Germany. 1. Introduction 2. Foreign Exchange Market Efficiency 3. The Term Structure of the Forward Premium 4. Conclusions. Appendices