Political turmoil and financial market behaviour in reforming market-oriented economies: the case of Poland

The Interest Rate Parity Theorem: A Reinterpretation

ENMX: An elastic network model to predict the FOREX market evolution

Volatility Spillover Between the Stock Market and the Foreign Exchange Market in Pakistan

The foreign exchange (FX) market has many features including (1) Each trader’s payoff depends not only on his own behavior, but also on other traders’ decisions; (2) The number of traders is too large to make them all know the other dealers’ methods of decision making; and (3) The FX market has many levels. The FX market is complex because of these features. A diversity of techniques are required to deal with such complex problems. That is hybrid solutions are crucial for the FX market. On the other hand, research into the FX market has revealed that it demonstrates some characteristics of multi-agent systems such as autonomy, interaction, and emergence. To this end, an agent-based hybrid intelligent system was developed for FX trading, which is based on our proposed agent-based hybrid framework. This paper is to discuss the analysis, design, and implementation such a system. Some experimental results and comparisons with related works are also provided. The interest of this paper does not reside in improving the predictive capabilities of different FX models, but rather in how to integrate different models into one system under the unifying agent framework. The success of this system indicates that agent perspectives are very appropriate to model complex problems such as the FX trading.

In order to analyze the volatility spillover effect between foreign exchange and stock market, this paper adopts the wavelet multi-resolution analysis method of computer simulation. Firstly, aiming at the problem of high and low frequency oscillation and exchange rate de-noising, we adopts the generalized autoregressive conditional heteroskedasticity (GARCH) model to carry out the oscillation correction and exponential modification of the exchange rate denoising signal based on wavelet multi-resolution, and carries out the corresponding decomposition and fitting combined with the wavelet multi-resolution of the state transition GARCH. Then, through the computer simulation of the modified wavelet multi-resolution analysis, this paper studies the volatility spillover effect between the foreign exchange market and the stock market from different scales, so as to explore the simultaneous research from the time domain and frequency domain. The empirical results show that the low-frequency signals of RMB exchange rate volatility (RMB-ERV) and stock price volatility (SPV) have co-integration relationship. It is unique in that the volatility spillover effect in different trading cycles is inconsistent: in the short term, it is mainly manifested in the volatility spillover from the stock market (VS-SM) to the foreign exchange market (VS-FEM); and with the extension of the trading cycle, it shows both sides of effects on the VS.

Foreign exchange (FOREX) market is a decentralized global marketplace in which different participants, such as international banks, companies or investors, can buy, sell, exchange and speculate on currencies. This market is considered to be the largest financial market in the world in terms of trading volume. Indeed, the just-in-time price prediction for a currency pair exchange rate (e.g., EUR/USD) provides valuable information for companies and investors as they can take different actions to improve their business. The trading volume in the FOREX market is huge, disperses, in continuous operations (24 h except weekends), and the context significantly affects the exchange rates. This paper introduces a context-aware algorithm to model the behavior of the FOREX Market, called parallel elastic network model (PENM). This algorithm is inspired by natural procedures like the behavior of macromolecules in dissolution. The main results of this work include the possibility to represent the market evolution of up to 21 currency pair, being all connected, thus emulating the real-world FOREX market behavior. Moreover, because the computational needs required are highly costly as the number of currency pairs increases, a hybrid parallelization using several shared memory and message passing algorithms studied on distributed cluster is evaluated to achieve a high-throughput algorithm that answers the real-time constraints of the FOREX market. The PENM is also compared with a vector autoregressive (VAR) model using both a classical statistical measure and a profitability measure. Specifically, the results indicate that PENM outperforms VAR models in terms of quality, achieving up to $$930\times$$ speed-up factor compared to traditional R codes using in this field.

Introduction. Present scientific research resorts to experiments to identify the motives of a particular behavior, primarily in the financial sector. This article aims to reveal the dependence of students’ behavior in the simulated financial market on their level of mathematical knowledge. Materials and Methods. The data of the present study includes the results of an experimental session conducted among students of the economics program. At the first stage, a mathematical test was conducted with tasks of the school final exams. The second stage was a model of the financial market where participants could sell and buy shares in 10 rounds; at the end of the game, the incomes of the players were compared. Three mechanisms to analyze the obtained data were used. A cloud of players’ disposable funds distribution was constructed, along with income changes dynamics over time and the number of transactions in each round of the game. Next, correlation matrices were generated for the entire game and for its individual rounds. Finally, an econometric model was developed, with the player’s available funds as the explained variable and their score on a mathematical test as the regressor. Results and discussion. The results reveal that knowledge of mathematics can really influence income in the financial market, which is confirmed for our sample. The coefficient in the econometric model is significant at the 5% level and equals 48.84. In other words, an increase in a score in mathematics by 1 will entail an increase in the player’s income by 48.84 coins. The coefficient of determination is 0.15, which indicates the quality of the fit of the model at 15%. Considering that the analysis uses only one factor and does not take into account other aspects that could affect the behavior of players in the market, this is a fairly high value. Conclusion. The authors came to the conclusion that students demonstrate differences in income received in the financial market model, which, among other things, may be due to different levels of mathematical knowledge. Mathematical knowledge means the skills to use logical construction and more accurate forecasting, the probabilities of events. In the future, it is planned to scale the experiment for a larger sample and increase the representativeness of the results.

In this article, we search for the evidence of intraweek and intraday anomalies on the spot foreign exchange (FOREX) market. Having in mind the international scope of this market, empirical evidence against market efficiency (i.e. market anomalies) will have important consequences for the substantial number of FOREX investors all around the globe. We explore intraweek, intraday and interaction between days and hour trade anomalies on the FOREX market over the period of 10 years using hourly time-series data of Euro and US Dollar (EUR/USD) exchange rate on Swiss FOREX market from 1 January 2004 to 11 January 2014. We compare by analysis of variance test all pairs of mean returns on a daily, hourly and daily/hourly basis. t-Test is used to test whether intraday returns are significantly different from zero. We employ Tukey’s honestly significant difference test to explore which intraday pairs of hourly mean returns are significantly greater than zero. We find that intraday and interaction between day and hour anomalies are present in trading EUR/USD on the spot FOREX market over the period of 10 years. The best arbitrage opportunity is evidenced on Fridays, when selling USD and buying EUR at 00:00 and selling EUR and buying USD at 03:00 the same day.

Speculator activity and the cross-asset predictability of FX returns

Herd Behavior and Aggregate Fluctuations in Financial Markets

The Role of Implied Volatility in Forecasting Future Realized Volatility and Jumps in Foreign Exchange, Stock, and Bond Markets

This article has two aims. First, I revisit Khemiri (2009 Khemiri, R. 2009. Parameter stability in foreign exchange market microstructure: a Markov switching approach. la Revue Tunisienne d'Economie et de Gestion (Tunisian Journal of Economics and Business), 29: 171–200. [Google Scholar]) and I find support to Lyons (1995 Lyons, RK. 1995. Tests of microstructural hypotheses in the foreign exchange market. Journal of Financial Economics, 39: 321–51. [Crossref], [Web of Science ®] , [Google Scholar]) seminal dealer level specification with a still richer picture of the conditional volatility dynamics. For this reason, I develop an estimation procedure for a variety of Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and Markov Switching Exponential GARCH (MSGARCH) models which provide a richer modelling of volatility dynamics and I find that the Markov Switching Exponential GARCH (MSEGARCH) model fits the intraday data better. In addition, the second aim of this article is to study the relationship between trading volume and volatility in the Foreign Exchange (FX) market microstructure by using a Markov switching approach that captures asymmetry and regime shifts in the Lyons (1995 Lyons, RK. 1995. Tests of microstructural hypotheses in the foreign exchange market. Journal of Financial Economics, 39: 321–51. [Crossref], [Web of Science ®] , [Google Scholar]) dataset. In this context, the empirical results support the Mixture of Distribution Hypothesis (MDH) where I find a new result showing that that there is a positive correlation between volume and volatility of the Deutsche Mark (DM)/$ prices as well as a positive effect of order flow on returns. This confirms the role of order flow as a mean of transmission of information in the new microexchange rate economics.

This study delves into the intricate dynamics of China's foreign exchange (FX) market, emphasizing its essential role in the global financial ecosystem and China's economic architecture. Recognizing the FX market's critical importance, the Chinese government has carefully managed its transaction from a tightly controlled system to one progressively influenced by market forces since its foundation in the early 1990s. Despite significant advancements, the RMB FX market confronts challenges including limited responsiveness of the RMB exchange rate to market demands, capital flow restrictions, and external vulnerabilities, especially the impact of U.S. monetary policies and global economic uncertainties. Through comprehensive analysis, this research identifies these core issues and proposes targeted strategies for addressing them, such as gradual liberalization of the exchange rate mechanism, enhancing the openness of capital accounts, and promoting RMB internationalization. These solutions aim not only to address the current challenges but also to strengthen RMB FX market against future external shocks, contributing to its stability and integration into the global financial system. This thesis offers valuable insights for policymakers and stakeholders, guiding endeavors to enhance the functionality and adaptability of China’s foreign exchange market in the face of changing national and global landscapes.

The joint response of stock and foreign exchange markets to macroeconomic surprises: Using US and Japanese data

The regularity of price fluctuations in exchange rates plays a crucial role in foreign exchange (FX) market dynamics. In this paper, we quantify the multiply irregular fluctuation behaviors of exchange rates in the last 10 years (November 2006–November 2016) of eight world economies with two nonlinear approaches. One is a recently proposed multiscale weighted permutation entropy (MWPE) and another is the typical quantification recurrence analysis (RQA) technique. Furthermore, we utilize the RQA technique to study the different intrinsic mode functions (IMFs) that represents different frequencies and scales of the raw time series via the empirical mode decomposition algorithm. Complexity characteristics of abundance and distinction are obtained in the foreign exchange markets. The empirical results show that JPY/USD (followed by EUR/USD) implies a a higher complexity and indicates relatively higher efficiency of the Japanese FX market, while some economies like South Korea, Hong Kong and China show lower and weaker efficiency of their FX markets. Meanwhile, it is suggested that the financial crisis enhances the market efficiency in the FX markets.