Spot Exchange Research Articles

Exchange rate dynamics and the central bank's balance sheet

Are nominal exchange rate variations linked to the central bank's balance sheet, in particular to remunerated domestic liabilities? We use two metrics of implied exchange rates based on central bank balance sheet data: one is a traditional metric that includes the monetary base, and the other adds remunerated domestic liabilities. We first estimate a VAR model to investigate the endogenous interactions between central bank balance sheet components for a set of seven Latin American countries for the 2006:01-2019:12 period. Then, we use a pairwise cointegration framework to compare these two metrics of implied exchange rate with the spot (observed) exchange rate. We find that the implied exchange rates and the spot exchange rate are cointegrated for most of the set of Latin American countries. We also find that for a subset of our sample, the spot exchange rate adjusts to the metric that adds remunerated domestic liabilities. We conclude that remunerated domestic liabilities matter for understanding exchange rate dynamics, and explore a simple theoretical setup to better understand the mechanism.

An unbounded intensity model for point processes

We develop a model for point processes on the real line, where the intensity can be locally unbounded without inducing an explosion. In contrast to an orderly point process, for which the probability of observing more than one event over a short time interval is negligible, the bursting intensity causes an extreme clustering of events around the singularity. We propose a nonparametric approach to detect such bursts in the intensity. It relies on a heavy traffic condition, which admits inference for point processes over a finite time interval. With Monte Carlo evidence, we show that our testing procedure exhibits size control under the null, whereas it has high rejection rates under the alternative. We implement our approach on high-frequency data for the EUR/USD spot exchange rate, where the test statistic captures abnormal surges in trading activity. We detect a nontrivial amount of intensity bursts in these data and describe their basic properties. Trading activity during an intensity burst is positively related to volatility, illiquidity, and the probability of observing a drift burst. The latter effect is reinforced if the order flow is imbalanced or the price elasticity of the limit order book is large.

Nominal exchange rates and heterogeneous beliefs

We propose a two-country model with heterogeneous beliefs to understand dynamics of nominal exchange rate. Facing a shock to monetary policy, disagreement between domestic and foreign investors shifts the relative wealth of investors – an essential part of the stochastic discount factor in our model – which then moves the foreign exchange rate. Calibrated to U.S. and U.K. data, our model reveals that dispersion in beliefs predicts the future spot exchange rate, associates with the cross-section of currency risk premia, and comoves with the time-varying volatility in currency returns. Furthermore, our model suggests that domestic investors would hold fewer foreign currency-denominated bonds in countries with greater disagreements on monetary policy.

The Economic Consequence of Feds Monetary Policies in 2022: An Internal Perspectives

US interest rate hikes due to COVID-19 and high inflation have affected the country's stock market and the international foreign exchange market. The authors analyze the willingness of U.S. interest rate hikes to cause inflation at the micro level through changes in the amount of money held by residents because of U.S. policies. This paper analyzes the impact of the U.S. interest rate hike by collecting various information on rental housing, GDP and spot exchange rates of countries affected by the U.S. According to the findings of this paper: 1. US interest rate hikes lead to a rise in the amount of currency held by citizens. 2. Interest rate hikes affect the GDP growth rate, and the stock market are positively correlated. 3. US monetary policy affects the world economy. The point of studying the Fed's rate hikes is to analyze the strengths and weaknesses of the policy and to find better ways to control inflation and make the economy stable. According to this paper, the authors propose that the United States should stop raising interest rates at the right time when the inflation rate falls to the right value and let interest rates return to their previous levels, otherwise it will stagnate the country's economic development and cause turmoil in the country's stock and foreign exchange markets.

The Bitcoin volume-volatility relationship: A high frequency analysis of futures and spot exchanges

We examine the volume-volatility relationship across Bitcoin futures and spot markets, using daily realised volatility measures estimated from high frequency intraday data. We estimate realised spot volatility across five major exchanges using both the standard volume weighted price and using a new approach, inspired by the CME Bitcoin Reference Rate methodology. We find that unexpected trading volume is the most important explanatory variable for BRR spot volatility, explaining 20% of variation in price volatility at exchange level. Conversely, we find that both expected and unexpected CME Bitcoin futures volumes play a very limited or even calming role in systemic volatility. Our findings suggest that CME Bitcoin futures are not independently contributing to systemic risk in Bitcoin over the period studied.

The Impact of RMB Exchange Rate Fluctuations on China's Outward Foreign Direct Investment: An Empirical Analysis

Recently, the RMB exchange rate has experienced significant fluctuations. On May 13th, the spot exchange rate of onshore RMB against the US dollar fell below 6.8, depreciating by over 1000 basis points compared to the opening rate of 6.6985 on May 9th. Against this backdrop, this paper follows the research paradigm of Zhang Weifu (2008) to empirically examine the impact of RMB real exchange rate changes on Chinese enterprises' outward foreign direct investment (OFDI). Drawing on OFDI research frameworks for developed and developing countries, it first reviews theories of internal forces, external forces, and environmental support for China's OFDI, and discusses real exchange rate impacts on OFDI through the environmental support channel. A standardized microeconomic model is developed to show, under certain assumptions, that real exchange rates affect OFDI via export channels. An empirical model then tests these effects. Finally, policy recommendations are provided based on the theoretical and empirical results.

Forecasting the US Dollar/Euro Exchange Rate Based on ARIMA Model

The exchange rate is an important indicator for investors and governments as well as a verdict on economic prospects. The euro reached its lowest point in 20 years on August 22, 2022, falling below parity with the dollar and terminating a one-to-one exchange rate with the American currency. The event's great significance provides paramount motivation for us to construct a suitable model capable to forecast it. Numerous modeling methods are proposed to predict and analysis the exchange rate. This paper discussed the applicability of a univariate ARIMA model. Moreover, a multivariable ARIMA model, i.e., four macroeconomic variables supposed influential to the change of exchange rate, were introduced in the AR part of the ARIMA model. For the training set, data is gathered on a monthly basis from January 1999 to December 2021, while the period forecasted is the plunge event from January to August, in 2022. Their 8-month long forecasting experiments reveal that the univariate ARIMA model has no ability to forecast the trend of the exchange rate. In general, the multivariate regression model with ARIMA errors is capable of forecasting the historical change of the USD/EURO spot exchange rate. However, its performance totally depends on the quality of the predicted predictors, which are the four macro-economic variables in this study.

Analysis of influencing factors and prediction of China’s Containerized Freight Index

China, as a major maritime nation, the China Containerized Freight Index (CCFI) serves as an objective reflection of the Chinese shipping market and an important indicator for understanding China’s shipping industry globally. The shipping market is a complex ecosystem influenced by various factors, including vessel supply and demand, cargo supply and demand relationships and prices, fuel prices, and competition from substitute and complementary markets. To analyze and study the state of the Chinese shipping market, we selected the CCFI as an indicator and collected data on six factors that may affect the overall shipping market. These factors include “ the China Coastal Bulk Freight Index(CCBFI)”, “the Baltic Dry Index(BDI)”, “the Yangtze River Container Freight Index”, “Global: Aluminum (minimum purity of 99.5%, London Metal Exchange (LME) spot price): UK landed price”, “Major Ports: Container Throughput”, and “Coal Price: US Central Appalachia: Coal Spot Price Index”. Then, we constructed an analyticaland predictive framework using Deep Neural Network (DNN), CatBoost regression model, and robust regression model to study the CCFI. Based on the R2 results of the three models, it is evident that DNN provides the best analytical and predictive performance for the CCFI, accurately forecasting its changes. Additionally, the robust regression model indicates that “Global: Aluminum (minimum purity of 99.5%, LME spot price): UK landed price” has the greatest impact on the CCFI. Finally, from a business perspective, we provide some suggestions for China’s container shipping industry.

Forward Looking Exporters

This paper studies the role of expectations in driving export adjustment. We assemble bilateral data on spot exchange rates, one year ahead exchange rate forecasts and HS2-product export data for 11 exporting countries and 64 destinations, covering the 2006–2014 period. Results from fixed effects regressions and an instrumental variables approach show that expectations of exchange rate changes are an important channel for export adjustment. A one percent expected exchange rate depreciation over the next year is associated with a 0.96 percent increase in the extensive margin (entry of new exporters) in the 2SLS regression, with statistically insignificant effects on total exports or the intensive margin. We provide intuition for these findings with a simple model with heterogeneous firms and sticky prices, and use our model to discuss the implications of anticipation for subsequent export growth and trade elasticity measurement.

The impact of the US employment report on the gold spot rate

Purpose- Considering the various financial markets, it can be observed that macroeconomic events such as announcement releases might affect the volatility and the direction of price movements in the related markets. While some announcements might play a substantial role in this subject, some might be categorized as unessential announcements in the economic calendars. Reports related to the employment situation, inflation, growth of the domestic product, and commodity reservations of a country are crucial points on the schedule of investors and traders all around the globe. However, reports coming from countries with a major economic share have a much more significant effect on the market. In that regard, researchers are more interested in the evaluation of economic events of countries like the United States, United Kingdom, Germany, and China. In that regard, this study focuses on the impact of the U.S. employment situation report on the XAU/USD spot exchange rate. Methodology- In the first part of the study, the significance of relevant factors of the announcement has been evaluated to specify the importance of the elements included in the employment report. In that interest, an OLS regression model has been developed in the first step. Furthermore, the face and statistical validity phases have been controlled to improve the efficiency of the model. The second part of the study focuses on the direction of the price movement respectively after specific periods from the report’s release. To satisfy the desired goal of the study, two various models have been applied to the data to evaluate the two models and their performances. The first model is based on logistic regression approaches while the second model benefits from XGboost regression. Accuracy metrics have been evaluated for both models to decide on the healthiness of the performances. Findings- Findings demonstrate that the gold spot exchange rate reacts strongly to the announced nonfarm payroll employment figure, while the market takes its revision of the prior month and unemployment rate as additional data around the release of the announcement. Results suggest that employment reports labeled as “bad news” for the U.S. economy caused an increase in the exchange rate of the gold spot. Price discovery for different time intervals after the announcement release shows that the first 10 minutes are the most crucial. Time intervals before the announcement release imply that exchange rate changes are regular and there is not any recognizable pattern for price movements before the announcement release, while abnormal returns start to show up just after the release of the announcement. Conclusion- To sum up, the impact of the announcement report on the price movement of the gold spot is undeniable. However, uncertainties increase before the announcement, and volatility increases after the announcement. Various statuses lead to specific movements in the market. While the uncertainties are lower before the announcement, the price movement of the gold spot would be diverse to the status of the announcement. Keywords: U.S. Nonfarm payrolls, employment report, macroeconomic event study, gold spot, machine learning, decision tree JEL Codes: F31, F62, G15

Multivariate probabilistic forecasting of intraday electricity prices using normalizing flows

Electricity is traded on various markets with different time horizons and regulations. Short-term intraday trading becomes increasingly important due to the higher penetration of renewables. In Germany, the intraday electricity price typically fluctuates around the day-ahead price of the European Power EXchange (EPEX) spot markets in a distinct hourly pattern. This work proposes a probabilistic modeling approach that models the intraday price difference to the day-ahead contracts. The model captures the emerging hourly pattern by considering the four 15min intervals in each day-ahead price interval as a four-dimensional joint probability distribution. The resulting nontrivial, multivariate price difference distribution is learned using a normalizing flow, i.e., a deep generative model that combines conditional multivariate density estimation and probabilistic regression. Furthermore, this work discusses the influence of different external impact factors based on literature insights and impact analysis using explainable artificial intelligence (XAI). The normalizing flow is compared to an informed selection of historical data and probabilistic forecasts using a Gaussian copula and a Gaussian regression model. Among the different models, the normalizing flow identifies the trends with the highest accuracy and has the narrowest prediction intervals. Both the XAI analysis and the empirical experiments highlight that the immediate history of the price difference realization and the increments of the day-ahead price have the most substantial impact on the price difference.

A Quantity-Driven Theory of Term Premia and Exchange Rates

AbstractWe develop a model in which specialized bond investors must absorb shocks to the supply and demand for long-term bonds in two currencies. Since long-term bonds and foreign exchange are both exposed to unexpected movements in short-term interest rates, a shift in the supply of long-term bonds in one currency influences the foreign exchange rate between the two currencies, as well as bond term premia in both currencies. Our model matches several important empirical patterns, including the comovement between exchange rates and term premia, and the finding that central banks’ quantitative easing policies affect exchange rates. An extension of our model links spot exchange rates to the persistent deviations from covered interest rate parity that have emerged since 2008.

Cross-market spoofing

Since 2012, regulatory investigations have revealed widespread manipulation and collusive practices among banks active in over-the-counter (OTC) markets. These discoveries have resulted in fines and settlements amounting to billions of US dollars, criminal proceedings and stricter regulation worldwide. However, recent legal cases and regulatory reports indicate that authorities have stepped up their efforts to crack down on so-called “cross-market spoofing”. The manipulative tactic involves a combination of a genuine order in one market and a spoof order in another, which is notoriously difficult to detect. In this paper, we use a high-frequency data set of limit order book snapshots from the foreign exchange (FX) spot market to develop and test a methodology to assess the feasibility, and hence potential prevalence, of cross-market spoofing. Our findings show that predictable reactions follow potential single-market spoofs that a market manipulator may exploit. Crucially, we also find that predictability may be observed in closely related markets. In particular, we discover that EUR/JPY offers a reliable pathway for a manipulator to exploit via spoof orders at deeper levels in the EUR/USD or USD/JPY limit order books. The findings suggest that a manipulator is more likely to submit a spoof order in a liquid market and a resting order in a less liquid but closely related market, rather than vice versa. Moreover, cross-market spoof orders are more likely to be found in markets that act as hedging markets.

The Bitcoin Volume-volatility Relationship: a High Frequency Analysis of Futures and Spot Exchanges

The Bitcoin Volume-volatility Relationship: a High Frequency Analysis of Futures and Spot Exchanges

EXCHANGE RATE FORECASTING: THE FUNDAMENTAL FORECASTING MODEL

This paper is using the fundamental forecasting model, which is a monetarist theory of exchange rate determination, for the current forecasting. This theory is tested empirically by using data, spot and forward rates and a variety of macro-variables from seven different countries with respect the U.S., as our domestic country. A GARCH-M model is used to forecast the volatility of the spot exchange rate. The paper is also using a Vector Auto-regression (VAR) framework to forecast simultaneously spot (s_t) and forward (f_t) exchange rates by utilizing exogenous macro-variables, time trends, and policy instruments. Further, at the end an impulse response function and a Hodrick-Prescott filter are used to present visually the behavior of the spot exchange rate. The countries used in the empirical work are, U.S. with respect the Euro-zone, Mexico, Canada, U.K., Switzerland, Japan, and Australia. The results show that these methods are giving very good forecasting for these seven exchange rates by minimizing the standard error of the regression (SER) and the root mean squared error (RMSE). Of course, uncertainty exists always in the forecasting of any economic variables, due to unanticipated public policies (monetary, fiscal, and trade) and other “innovations” in our financial markets, plus the new philosophies (i.e., liberalism, lack of ethics, perversions, DEI, AI, wars, BRICS, etc.), official measurements, and value system in our markets, societies, and way of living.

Post-farmgate food businesses of India: The enterprises, the entrepreneurs, and the employees

In 2015–16, 21 million small and medium enterprises engaged in the business of manufacturing, trading, retailing, and serving food linked India's 146 million farmers to its nearly 1.3 billion consumers. These food enterprises employed 25 million men and 10 million women and had an annual turnover of $ 227 billion. Between 2010–11 and 2015–16, the gross value added (GVA) by the post-farmgate food businesses (PFFBs) increased by $16.3 billion (from $ 22.9 billion to $ 39.2 billion) which is comparable to the growth of $19.5 billion in the GVA by the primary food production (or the farm) sector which is nearly 10 times larger in size. Despite their growing contribution to the overall food economy, PFFBs are ignored in research and policy discussions on food systems. This paper tries to fill this gap by using two large nationally representative datasets from India—the Enterprise Survey and the Periodic Labor Force Survey (PLFS)—to assess the key characteristics of PFFBs and the entrepreneurs and employees who work in them. We show that despite rapid economic growth in the country, micro-enterprises with less than 5 employees continue to dominate India's food economy. Most of these firms are informal, rely purely on spot exchanges with their sellers and buyers, and do not borrow money from formal financial institutions. We find a strong positive association between the formal status and the productivity and profitability of PFFBs.

Trade Deficit And Currency Devaluation: Testing The J-Curve

This paper is testing empirically the effect of a devaluation of a currency on the trade account of the country, the J-curve effect, by using the trade between the U.S. and seven countries (Euro-zone, Mexico, Canada, United Kingdom, Switzerland, Japan, and Australia). A devaluation (depreciation) of the U.S. dollar is increasing the spot exchange rate ($/FC) and increases the price of imports and reduces the price of exports. Then, imports are falling and exports are increasing and the trade account is improved in the long-run. In the short-run, the trade account is deteriorated because the international trade transactions are pre-arranged and the invoices are in foreign currency, so it cannot be adjusted. This J-curve hypothesis is tested by using a regression equation and a VAR model, where the volatility of the real exchange rate (TOT) is specified with a GARCH-M process. Also, different stationary tests are taking place, like, unit root and cointegration ones. The empirical results mostly are supporting the J-curve effect.

Simultaneous Inference on the Korean Won-US Dollar Forward Premium Anomaly

The forward premium anomaly, which refers to the empirical failure of the uncovered interest parity (UIP), has been primarily examined by the forward premium regression of [Fama, E. (1984). Forward and spot exchange rates. Journal of Monetary Economics, 14(3), 319–338. https://doi.org/10.1016/0304-3932(84)90046-1]. Some studies apply the rolling-window regression to capture the time-varying coefficient on the forward premium, with difficulty in statistically testing the deviation of the coefficient from the UIP over time. We follow [Baillie, R., & Kim, K. (2015). Was it risk? Or was it fundamentals? Explaining excess currency returns with kernel smoothed regressions. Journal of Empirical Finance, 34, 99–111] to apply the simultaneous inference procedure to the Korean Won-US Dollar spot and forward exchange rates by estimating the time-varying coefficient from the kernel-smoothed local-linear regression and constructing the uniform confidence band to test the local deviation. We find that, while the UIP is not rejected from the baseline regression, the simultaneous inference shows that the deviation from the UIP is mainly observed during the early 2000s and 2010s. Time-variation of the forward premium coefficient tends to be significantly affected by economic uncertainties such as the interest rate, inflation, and stock return volatilities in Korea and US.

Impact of currency futures on spot rate volatility in Indian foreign exchange market

The work aimed going to ascertain the efficacy of the currency futures on cash-market or Spot exchange rates volatility with respect of USD/INR, EURO/INR, GBP/INR & JPY/INR. The daily closing of have been taken from RBI website. Also, the time series data is checked for stationarity using ADF test. Existence of ARCH effect examined in spot rate residual of the return series data and checked with Autoregressive conditional heteroskedasticity L.M. test by using GARCH (1,1) model. Findings of GARCH model shows that large volatility is persistent pre-introduction of currency futures and finding shows presence of time-varying conditional volatility of returns of currencies spot prices at pre-post and total period of currency futures introduction in currency exchange market. The variances in volatility of the exchange rate returns of pre and post introduction of currency futures time periods being analyzed by using statistical tools. Findings shows to be importantly varied and volatility has been came down in the post futures era also series were found more stable in terms of volatility.

Simulating the Changes of Invasive Phragmites australis in a Pristine Wetland Complex with a Grey System Coupled System Dynamic Model: A Remote Sensing Practice

Biological invasion has been one of the reasons that coastal wetlands gradually lose their ecological services. The current study investigates the spread of a commonly found invasive species in coastal wetlands in Northeastern US, the Phragmites australis. Within a relatively pristine wetland complex in coastal New Jersey, we collected high-resolution multispectral remote sensing images for eight years (2011–2018), in both winter and summer seasons. The land cover/land use status in this wetland complex is relatively simple, contains only five identifiable vegetation covers and water. Applying high accuracy machine learning algorithms, we are able to classify the land use/land cover in the complex and use the classified images as the basis for the grey system coupled system dynamics simulative model. The simulative model produces land use land cover change in the wetland complex for the next 25 years. Results suggest that Phragmites australis will increase in coverage in the future, despite the stable intensity of anthropogenic activities. The wetland complex could lose its essential ecological services to serve as an exchange spot for nekton species from the sea.