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The development of China's foreign exchange market and the reform of Chinese yuan (hereinafter “CNY”) exchange rate are closely linked with each other. Their respective journey through the past 70 years can both be divided into three historical periods; as follows: China's foreign exchange market underwent a difficult exploration period, a formation and development period and an innovative development period; in the meanwhile, the formation mechanism of CNY exchange rate also witnessed three periods marked successively by a single exchange rate system with administrative pricing, an explorative formation mechanism of CNY exchange rate and a reformed, marketized CNY exchange rate mechanism.

In the present world, the development of almost every country is closely linked to the international community, which is the result of the heterogeneity in system, market, humanity and history, in addition to the differences in natural resource endowments and the diversity in technology, administration, information, experience and diplomacy. International economic exchanges require foreign exchange, which gives rise to the existence and development of the foreign exchange market.

The 70-year history of China's foreign exchange market has proven the need to continue safeguarding national sovereignty and interests of the people, stick to the general direction of serving economic development, adhere to the strategy of steadily and orderly promoting the construction of the foreign exchange market, keep on making innovation in monetary policy operation and unbendingly stay away from any systemic financial risks.

During the 70-year history of the new China, as an indispensable economic resource in China's economic development, the foreign exchange mechanism bolstered each stage of economic development and was always an important manifestation of China's economic sovereignty. It is argued that during the 30-year planned economy that preceded reform and opening-up, China pursued a closed-door policy with few international economic exchanges. The subtext of such argument is that China did not have (or hardly had much of) a foreign exchange mechanism during this period, which is clearly in conflict with historical evidence. In fact, although China did not have an open foreign exchange market before the reform and opening-up, it had a clear foreign exchange management system and exchange rate system.

The purpose of this paper is to present the essential role that currency order flow plays in the foreign exchange markets of emerging economies in the determination of their currencies in the short and the long-run against major currencies of the world, which cannot be over emphasized, most especially against the US dollar. Insomuch that, if some of these emerging economies can be successfully transmitted into full development, it would be a good model for other emerging economies and the world at large.

A hybrid model (portfolio shift model) proposed by Evans and Lyons (2002a, 2002b) is extended to analyze a data set of every quarter of an hour currency order flow and currency exchange rate fluctuations of Thai Baht (THB) against the US$ for the period of six years (January 2010 to December 2015). To reflect the pressure of currency excess demand, the authors construct a measure of currency order flow in the Thailand currency exchange market. Vector autoregression model is applied to estimate the effectual role of currency order flow in the determination of exchange rate for the THB against the US$.

Currency order flow indeed accounted for a sizeable and significant portion of the fluctuations in the THB and the US$ exchange rate.

Insomuch that, the results show that currency order flow has significant explanatory power in the emerging markets economy to capture the THB exchange rate variability, and it then brings to the attention of the Thailand Monetary Authority the importance that should be attached to the market microstructure.

This study examines the volatility spillover effect and forward pricing effect between futures and spot markets, using the daily data of January 1988~April 2013 and Bounds test, ARDL model, DCC-GARCH model and the new method of spillover index calculation. In particular, the comparison between the developed and emerging markets will shed a light on a difference between the efficiencies of the two groups of markets. Our results show that the volatility spillover effect in the developed market was less in magnitude, compared to that effect in the emerging market. The causal influence from the future market to the spot market was greater in the developed market than in the emerging markets. This indicates that the foreign exchange markets (future and spot both) were much more efficient in the developed markets than in the emerging markets. This also implies very fruitful guides for the foreign exchange intervention policy, including signaling effect, portfolio effects, and direct and indirect intervention effects.

To develop an integrated approach to forecasting spot foreign exchange rates by incorporating some principles underlying long‐term dependence.

The paper utilises the random‐walk framework to develop a stochastic forecast model wherein the sign (positive or negative) and magnitude (strong or weak) of dependence can be separately controlled. The integrated model demonstrates superior forecast performance over a conventional random walk.

Using spot log prices and log price changes (returns) for the USD/AUD exchange rate, the initial outcomes of the study suggest that a priori knowledge of the underlying sign and magnitude of long‐term dependence yields out‐of‐sample forecasts superior to those of a random walk model.

Independent assessment of the contribution to forecast accuracy of controlling for the sign of dependence between successive price changes only shows little additional improvement in out‐of‐sample forecast performance over the random walk null.

The findings of the study have important ramifications for managerial finance as they provide important insights on expected future currency returns with potential advantages in currency hedging and/or timing of international capital flows.

The contribution of this paper is to develop an original forecast model explicitly incorporating the conceptual and theoretical characteristics of long‐term dependent time series. By separating the key characteristics and modelling each individually, the contribution of each to forecast accuracy can be evaluated.

Offering an example of a small open developing economy, the purpose of this paper is to explore the reasons for relative stability in Armenia’s foreign exchange market. Relying on a single currency and derived cross-currency exchange rates, the paper models short-term effects between exchange market pressure and financial and macroeconomic factors.

Following a literature review, the paper sets the macroeconomic context with an initial variance comparison of standard currency pairs and derived cross-currency exchange rates. Then, the core analysis is carried out with a vector error correction model, focusing on short-term cross-dynamics in monthly data. The orthogonal impulse response function analyses help solidify and further inform relevant conclusions.

Three broad factors influence Armenia’s foreign exchange market: external push factors; domestic banking sector competition, and foreign currency risk perceptions; and domestic macroeconomic and dual, cross-pair, exchange rate target priorities. The central bank’s implicit management of the foreign exchange market’s expectations, pull factor, is consistent with trader market power’s contribution to lower volatility. Yet, the risk of financial and real-sector decoupling remains.

The results are relevant for emerging markets attempting to leverage the global liquidity and low interest rates, while being exposed to external pressures in the post-crisis environment, in which international reserves may be scarce while currency stability is an implied priority. This study can be further adapted to a more comprehensive structural short-term analysis of currency determination or similar dynamics in other small open economies.

The authors explore the relationship between the exchange rate, bond yield and the stock market as well as the effect of capital market dynamics on the exchange rate before and during the COVID-19 pandemic.

The authors employ a non-linear autoregressive distributed lag (NARDL) methodology using daily data of the Indonesian economy over the period 2012–2021.

Whilst, over the full sample period, the authors find no cointegration between the exchange rate, the 10-year bond yield and stock market, for the COVID-19 period, evidence of cointegration is present. Furthermore, the results suggest that asymmetric effects are evident both in the short as well as the long run.

To the best of the authors’ knowledge, this is the first time that the relationship between the exchange rate, bond yield and the stock market as well as the effect of capital market dynamics on the exchange rate before and during the COVID-19 pandemic has been explored in the case of the Indonesian economy.

This study investigates the effect of volatility scaling on valuing financial assets by examining the long-term return properties of the spot USD/AUD. Tests are conducted for evidence of a scaling law in USD/AUD returns. The economic implications of dependence and non-normality of the distribution of returns are explored using the Garman and Kohlhagen modified Black–Scholes model for valuing foreign currency options. The results suggest that the USD/AUD does not conform to a stable distribution and that as a result of differential scaling laws, Garman and Kohlhagen option values using implied annual volatility will be consistently too high or too low.

The purpose of this paper is to examine whether the banking crisis in the USA and Western Europe that began in August 2007 precipitated a change in the relationship between the currencies of the Baltic States and the Euro, such that it could be described as shift contagion. The paper also considers whether the “hardness” of the currency peg affects the market reaction to that crisis.

Shift contagion is said to be revealed if there a change in the co‐movements of exchange rates after August 2007 compared with before. Change is revealed by coherence and phase shifts. Both are drawn from cross‐spectral analysis.

Rather than weaken, the bonds between the currency board‐managed Kroon and the Litas, in a similar way to the Lat, exhibited greater bonding after the banking crisis began compared with before. The phase angles suggest some shift in money flows between the Baltic currencies and the Euro. With the Lat, the delays appear to be the same but at longer periodicies. The other two appear be subject to a reversal of money flows at various periodicies.

Spectral analysis reveals that bonding between currencies of ERMII countries and the Euro increased, but the structure of money flows changed as a result of the Western banking crisis in related geographical and financial markets, before a local crisis became evident. To what extent this is an improvement over correlation methods could be the basis of further research. The phase switch is a structural change that other techniques could not have revealed.

The paper shows that spectral analysis could be more widely used in financial economics to reveal the impact of events on term structures.

Considering the frequency of extreme events, enhancing the global financial system's stability has become crucial. This study aims to investigate the contagion effects of extreme risk events in the international commodity market on China's financial industry. It highlights the significance of comprehending the origins, severity and potential impacts of extreme risks within China's financial market.

This study uses the tail-event driven network risk (TENET) model to construct a tail risk spillover network between China's financial market and the international commodity market. Combining with the characteristics of the network, this study employs an autoregressive distributed lag (ARDL) model to examine the factors influencing systemic risks in China's financial market and to explore the early identification of indicators for systemic risks in China's financial market.

The research reveals a strong tail risk contagion effect between China's financial market and the international commodity market, with a more pronounced impact from the latter to the former. Industrial raw materials, food, metals, oils, livestock and textiles notably influence China's currency market. The systemic risk in China's financial market is driven by systemic risks in the international commodity market and network centrality and can be accurately predicted with the ARDL-error correction model (ECM) model. Based on these, Chinese regulatory authorities can establish a monitoring and early warning mechanism to promptly identify contagion signs, issue timely warnings and adjust regulatory measures.

This study provides new insights into predicting systemic risk in China's financial market by revealing the tail risk spillover network structure between China's financial and international commodity markets.