FX Market Research Articles

Predicting FX Market Movements Using GAN with Limit Order Event Data

Predicting FX Market Movements Using GAN with Limit Order Event Data

Multivariate additive subordination with applications in finance

We introduce a tractable multivariate pure jump process in which the trading time is described by an additive subordinator. The multivariate process retains the additivity property, and therefore is time inhomogeneous, i.e., its increments are independent but non stationary. We provide the theoretical framework of our process, perform a sensitivity analysis with respect to the time inhomogeneity parameters, and design a Monte Carlo scheme to simulate the trajectories of the process. We then employ the model in the context of option pricing in the FX market. We take advantage of the specific features of currency triangles to extract the joint dynamics of FX log-rates. Extensive tests based on observed market data show that our model outperforms well established pure jump benchmarks.

Central Banking in Financially Subordinated Countries During Crises: The Central Bank of the Argentine Republic’s Response to the Pandemic in 2020

Central banks in financially subordinated countries face structural limits derived from the position of the currencies that they issue in the international monetary hierarchy. These constraints become particularly acute during crises when financially subordinated countries face capital outflows triggered by both foreign and domestic residents. In crises, peripheral central bank intervention is characterized by the selling of international reserves. Issuing national currency can add fuel to the outflow fire, as shown by the response to the pandemic of the Central Bank of the Argentine Republic (BCRA). The BCRA implemented several countercyclical measures, including financing fiscal policies, lowering interest rates, increasing the liquidity of the financial system, targeted policies to boost lending, and FX market intervention. These were quantitatively significant and relatively successful at the beginning. However, from August, a growing exchange rate pressure and an acceleration of inflation forced the BCRA to change its strategy to face the outflow and withdraw national currency from circulation. The BCRA implemented exchange controls, reduced the financing to the Treasury, issued remunerated liabilities, and increased interest rates without being able to avoid a greater devaluation of the currency or reduce inflation.

An unconventional FX tail risk story

We examine how the tail risk of currency returns over the past 20 years were impacted by central bank monetary and liquidity measures across the globe with an original and unique dataset that we make publicly available. Using a standard factor model, we derive theoretical measures of tail risks of currency returns which we then relate to the various policy instruments employed by central banks. We find empirical evidence for the existence of a cross-border transmission channel of central bank policy through the FX market. The tail impact is particularly sizeable for asset purchases and swap lines. The effects last for up to 1 month, and are proportionally higher for joint QE actions. This cross-border source of tail risk is largely undiversifiable, even after controlling for the U.S. dollar dominance and the effects of its own monetary policy stance.

Do subsidized export credits affect firms’ behavior in the FX market? Micro evidence from Türkiye

Through its export-led growth strategy, Türkiye intends to increase its exports by subsidizing exporters via a rediscount credit scheme, a form of subsidized export credit, that is mostly financed by the Central Bank of the Republic of Türkiye (CBRT). This paper aims to investigate whether benefiting from such cost-effective financial support causes unintended consequences by focusing on the foreign exchange purchases of treated firms. Using firm-level data and a propensity score matching (PSM) difference-in-differences (DD) estimator, we find evidence of a positive and significant impact of using rediscount credits on the net FX purchases of treated firms. Furthermore, we find that being a net importer increases the sensitivity of net FX purchases to the use of rediscount credit. We also show that the impact of using rediscount credits on net FX purchases is higher in SMEs than in large firms.

Current Situation, Problems and Countermeasures Analysis of China's Foreign Exchange Market

China, as the second-largest economy in the world, plays a vital role in the development of FX market. In recent years, China's FX market has been expanding continuously, liquidity has been enhanced, and the exchange rate mechanism has been perfected. However, there are still some problems in the market, such as preventing and controlling the market risk, the irrational structure of participants, and so on. To solve these problems, it is necessary to deepen the opening-up of the financial market, perfect the mechanism of the formation of the exchange rate, enhance the risk prevention and control, optimize the structure of the market participants, and enhance the infrastructure of the foreign exchange market. Therefore, the study of this thesis is of great importance, and it can offer some help to the problems which should be resolved in FX market. This article will analyse the current situation of Chinese FX market, the four existing problems and the corresponding countermeasures. This article discusses some problems in the foreign exchange market: China's FX market has been severely influenced by the increase of interest rate, while China has been in a disadvantageous position in the field of FX operations, the size of assets and the establishment of institutions. The types of FX derivatives in China are less, and the participants in the FX market are unitary, and the investors' acceptance is low.

Spot Arbitrage in FX Market and Algorithmic Trading: Speed is Not of the Essence

Spot Arbitrage in FX Market and Algorithmic Trading: Speed is Not of the Essence

Retraction Note: How Have the COVID-19 Pandemic and Market Sentiment Affected the FX Market? Evidence from Statistical Models and Deep Learning Algorithms

Retraction Note: How Have the COVID-19 Pandemic and Market Sentiment Affected the FX Market? Evidence from Statistical Models and Deep Learning Algorithms

International reserves, currency depreciation and public debt: New evidence of buffer effects in Africa

The paper adds to the literature on the issue of public debt in African economies, by investigating the role foreign exchange reserves play in improving the level of indebtedness and as buffer of the negative effect of exchange rate depreciation while considering the exchange rate policy. Our results show a direct link between the level of foreign currency reserves and that of external debt in Africa. Particularly, we demonstrate that higher foreign currency reserves tend to decrease the public debt stock to GDP. This effect is even more significant when countries go through high exchange rate depreciation episodes (10% or higher). This impact, however, is not homogenous among country groups, as only countries with a floating exchange regime tend to benefit from this buffer effect compared to anchored regimes. In a time where most African economies face severe exchange rate depreciation episodes following the U.S. monetary tightening policy, central bankers and policy makers need to consider a plethora of policy issues including interventions in the FX market to mitigate depreciations and maintain a sustainable public debt stock.

To fix or not to fix: The representativeness of the WM/R methodology that underpins the FX benchmark rates. A pre-registered report

This paper examines the effectiveness of the WM/Reuters (WM/R) methodology for FX benchmark rates since its revision in 2015. After a series of market manipulation scandals, the WM/R methodology underwent significant changes to enhance its reliability and minimise the risk of market manipulation. However, the dynamic nature of the FX market, characterised by evolving investor behaviour and continuous trading, necessitates a reassessment of this benchmark’s quality. Using a comprehensive dataset of proprietary order book data from Refinitiv, our empirical study critically evaluates the WM/R methodology’s current standing in the FX market. We focus on representativeness, attainability, and robustness to determine whether lengthening the time window of the benchmark increases its reliability. We then focus on liquidity, trading activity, transaction costs, and price volatility to determine the ability of the WM/R methodology to minimise market impact during the 4 pm calculation window. Our findings aim to inform future modifications to the FX benchmark, ensuring its alignment with the evolving market environment and maintaining its role as a crucial reference point for global financial activities. This study contributes to the ongoing discourse on effective financial benchmarking, particularly in the high-stakes and continuously active realm of foreign exchange markets.

Measuring the International Influence of the RMB and its Regional Heterogeneity: Theory and Evidence from the Global FX Market Network

ABSTRACT This study assesses the global and cross-regional influence of China’s currency, the renminbi (RMB). We first develop a theoretical framework to determine the channel through which progress in RMB internationalization can be mapped onto the market influence of the RMB exchange rate on that of other currencies. Using a volatility connectedness approach, we then construct a series of spillover indices reflecting the international influence of the RMB. We find that the RMB’s influence is worldwide but is smaller than that of major international currencies, such as the US dollar. Overall, the RMB has basically become a peripheral currency in Greater China, Central Asia, Eastern Europe, and some South Asian economies. Because of the Belt and Road initiative, regionalization of the RMB has also progressed in developing economies; however, China still has far to go in fully achieving internationalization of its currency.

Subjective Risk Premia in Bond and FX Markets

Subjective Risk Premia in Bond and FX Markets

Network analysis of the foreign exchange market using minimum spanning trees constructed from the dynamic time warping distance measure

This paper evaluates the changes that occurred in the topological structure of the foreign exchange market due to the COVID-19 pandemic and after the Russian invasion of Ukraine. A network of 17 currencies was analysed from 1.01.2019 to 31.07.2022 in four sub-periods: before the pandemic, in the year of the pandemic outbreak, in the further course of the pandemic, and the year the war started. Dynamic time warping (DTW) distances between pairs of time series for individual currencies were calculated, and based on these distances, minimum spanning trees (MST) were constructed whose topological characteristics were then analysed. The results show that the topological structure of the foreign exchange market varied in the sub-periods studied. The financial crises caused by the COVID-19 pandemic and the Russian invasion of Ukraine affected the FX network in different ways. However, the two strongest monetary clusters (i.e. clusters centred on the USD and EUR, respectively) have still emerged. Comparing the MSTs for the crisis sub-periods, it can be seen that the USD took a dominant position during the pandemic outbreak, while the EUR played a more significant role during the war. This study contributes to the literature by characterising the topological structure of the FX market in specific sub-periods – during the COVID-19 pandemic and after the Russian invasion of Ukraine. Thus, it represents a particularly relevant study of the impact of crises on the global financial market.

Are the intraday volatility estimations more representative than the conventional measures? Evidence from the EURTRL FX.

This paper aims to assess the accuracy and representativeness of Value at Risk (VaR) estimation models for the highly volatile Turkish lira (TRY) and Euro (EUR) FX rate. We compare the performance of various VaR models commonly used in the financial and banking industry and examine the effectiveness of intraday models, particularly the Garman and Klass (1980) and the Parkinson (1980) approaches, in improving VaR estimation relative to conventional models such as Variance Covariance and Exponential Weighted Moving Average. Additionally, we highlight certain shortcomings of the VaR legislation proposed by Vasileiou (2016, 2017). Our findings demonstrate that intraday volatility estimations not only enhance the accuracy of VaR estimations, but also provide a more representative assessment of real risk. The results of this study have significant implications for scholars, regulators, portfolio managers, and investors seeking to better understand and manage risk in the TRY-EUR FX market.

Do econometric advances lead to more accurate VaR estimations when the legislation requirements are applied? The case of Turkish Lira FX Market.

This paper empirically tests the benefits of econometric advances in the field of Value at Risk (VaR) within the existing legal framework that regulates the Banking (Basel III) and Asset Management industry (CESR (2010)). We test several VaR models, from the simplest and the most easily applied, i.e. the Historical VaR (HVaR), Variance Covariance VaR (VCVaR), and the Exponential Weighted Moving Average VaR (EWMAVaR), to advanced models such as GARCH(1,1). We test these models by examining the extremely volatile FX market of the Turkish Lira (TRY) and we evaluate the models according to the criteria set by the legal framework on VaR. The empirical findings suggest that the HVaR model is a very reliable model if the goal of a risk manager is to satisfy the legislation requirements. However, we should mention that models that make use advances in econometrics lead to more accurate and more representative VaR estimations than the HVaR. The law should give incentives to adopt more representative models in the financial industry in order to take advantage of advances in financial modeling.
 
 JEL Classification: C53; G15
 Keywords: Value-at-risk; GARCH estimation; Historical VaR; Variance Covariance; Exponential Weighted Moving Average VaR; Back-testing; Legislation; FX Market
 

GENERATING CURRENCY EXCHANGE RATE DATA BASED ON QUANT-GAN MODEL

The aim of the research. This paper discusses the use of machine learning algorithms to generate data that meets the demands of academia and industry in the context of exchange rate fluctuations. Research results. The paper builds a Quant-GAN model using temporal convolutional neural networks (CNN) and trains it on end-of-day and intraday high-frequency rates of currency pairs in the global market. The generated data is evaluated using various statistical methods and is found to effectively simulate the real dataset. Experimental results show that data generated by the model effectively fits statistical characteristics and typical facts of real training datasets with good overall fit. The results provide effective means for global FX market participants to carry out various tasks such as stress tests and scenario simulations. Future work includes accumulating data and increasing computing power, optimizing and improving GAN models, and establishing evaluation standards for generating exchange rate price data. As computing power continues to grow, the GAN model’s ability to process ultra-large-scale datasets is expected to improve.

An International Study of Application of Long Short-Term Memory (LSTM) Neural Networks for the prediction of stock and forex markets

This study focuses on the use of Long Short-Term Memory (LSTM) neural networks for stock and currency market forecasting. Accurate projections are difficult to make because of the complex dynamics and non-linear interactions that characterise financial markets. A Recurrent Neural Networks (RNN) variation called LSTM is particularly good at identifying temporal dependencies and long-term patterns in sequential data. The goal of LSTM models is to discover significant insights and produce precise predictions using past price data. In addition to discussing data pretreatment methods, model creation, and assessment metrics related to stock and FX market prediction, this work examines the benefits of LSTM in capturing market dynamics. Case studies and empirical analysis are used to investigate the capabilities and constraints of LSTM models in forecasting market movements.

Do positive and negative variations in stock indexes lead to depreciation in local currencies? A beyond symmetrical evidence from ASEAN-5 region

Preliminary studies overlooked the importance of the financial crisis in the asymmetric transmission of financial market shocks to FX market returns. Moreover, existing studies also concentrated on the response of aggregate equity market returns to the forex market returns rather than the vice versa asymmetric effect for the ASEAN-5 region. We utilised the panel based ARDL and NARDL framework using pooled mean group method for conducting this study. There are 415 observations spanning the pre-crisis period of January 2001 to December 2007. Furthermore, 595 and 1135 observations are taken into consideration when post-recessionary, and overall sampling periods of January 2010 to December 2019 and January 2001 to December 2019 are considered, correspondingly. Overall, the findings indicated that in the short run, only negative equity market returns caused depreciation in the local currencies of ASEAN-5 member countries during the pre-crisis period, whereas only positive shocks during the post-crisis regime appreciated the local currencies of ASEAN-5 member countries. Furthermore, only longer-term negative financial market shocks contribute to post-crisis local currency deflation in the ASEAN-5 member nations. This demonstrates that investors and exporters must consider the importance of the particulate crisis period when formulating forward currency arrangements.

Does FX market in India integrated to exchange rate theories? a review amidst COVID-19

Does FX market in India integrated to exchange rate theories? a review amidst COVID-19

RETRACTED ARTICLE: How Have the COVID-19 Pandemic and Market Sentiment Affected the FX Market? Evidence from Statistical Models and Deep Learning Algorithms

This paper attempts to investigate the impact of the COVID-19 pandemic and market sentiment on the dynamics of USD/JPY, GBP/USD, and USD/CNY. We compose the market sentiment variable and incorporate the newly confirmed COVID-19 cases and sentiment variable into the traditional exchange rate forecasting model. We find that confirmed COVID-19 cases and sentiment variables in the US, Japan, UK, and China in the period of January 23rd, 2020 to September 14th, 2021 are significant in explaining the bilateral exchange rate movement. Recurrent neural network (RNN) and long short-term memory (LSTM) models outperform the other deep learning models and vector autoregressive (VAR) model in forecasting the bilateral exchange rate movement during the COVID-19 pandemic period. Further analysis using high-frequency intraday data and ensemble models shows that ensemble models significantly improve the accuracy of exchange rate prediction, as they are better at coping with the nonlinear and nonstationary features of exchange rate time series.