Forecasting Exchange Rates Research Articles

Understanding exchange rate shocks during COVID-19

Using a dynamic VAR model fitted to hourly data, we evaluate the evolution of spillover shocks from exchange rates returns of EURO, Yen, CAD and GBP. We find that over the COVID-19 sample: (a) total exchange rate shock spillovers explain around 37.7% of the forecast error variance in the exchange rate market compared to only 26.1% in the pre-COVID-19 period; and (b) exchange rate own shocks explain between 56% to 75% of own exchange rate movements. These results hold in multiple robustness tests. The implication is that exchange rates predict most of their own changes. We confirm this through an economic significance test where we show that the shock spillovers predict exchange rate returns and these predicted exchange rates can be useful in extracting buy and sell trading signals.

Testing the efficiency of inflation and exchange rate forecast revisions in a changing economic environment

We use the Banco de la República expectations survey of external economic analysts to study whether fixed-event individual forecasts of inflation and exchange rate are updated efficiently when new information becomes available. To this end, we test for weak-form and strong-form efficiency. The novel aspects of this paper are that we relax the individual homogeneity assumption, and consider a forecasters’ information set that contains a large number of empirically relevant variables. We address model selection using two of the most popular methods available in the penalised regression literature, and a new form of multiple testing. Our results show that more than half of the analysts’ revisions are independent of one another (weakly efficient). Also, conditional on passing weak efficiency, we find that analysts use past values of inflation and exchange rate changes to revise their forecasts and a broader array of variables during periods of market instability.

Forecasting Exchange Rates: An Empirical Application to Pakistani Rupee

Forecasting Exchange Rates: An Empirical Application to Pakistani Rupee

The Story of the Real Exchange Rate

The real exchange rate (RER) measures relative price levels across countries, capturing deviations from purchasing power parity (PPP). RER is a key variable in international macroeconomic models as it is central to equilibrium conditions in both goods and asset markets. It is also one of the most starkly behaving variables empirically, tightly comoving with the nominal exchange rate and virtually uncorrelated with most other macroeconomic variables, nominal or real. This review lays out an equilibrium framework of RER determination, focusing separately on each building block and discussing corresponding empirical evidence. We emphasize home bias and incomplete pass-through into prices with expenditure switching and goods market clearing, imperfect international risk sharing, country budget constraint, and monetary policy regime. We show that RER is inherently a general equilibrium variable that depends on the full model structure and policy regime, and therefore partial theories like PPP are insufficient to explain it. We also discuss issues of stationarity and predictability of exchange rates.

Commodity prices and exchange rates: evidence from commodity-dependent developed and emerging economies

PurposeIn this paper the authors examine evidence on exchange rate predictability through commodity prices for a set of countries categorized as commodity import- and export-dependent developed and emerging countries.Design/methodology/approachThe authors perform in-sample and out-of-sample forecasting analysis. The commodity prices are modeled to predict the exchange rate and to analyze whether this commodity price model can perform better than the random walk model (RWM) or not. These two models are compared and evaluated in terms of exchange rate forecasting abilities based on mean squared forecast error and Theil inequality coefficient.FindingsThe authors find that primary commodity prices better predict exchange rates in almost two-thirds of export-dependent developed countries. In contrast, the RWM shows superior performance in the majority of export-dependent emerging, import-dependent emerging and developed countries.Originality/valuePrevious studies examined the exchange rate of commodity export-dependent developed countries mainly. This study examines both developed and emerging countries and finds for which one the changes in prices of export commodities (in case of commodity export-dependent country) or prices of major importing commodities (in case of import-dependent countries) can significantly predict the exchange rate.

How much does economic news influence bilateral exchange rates?

We investigate the role of economic news in predicting US dollar vis-à-vis the British Sterling Pound exchange rate. We find that it is the negative news (and not positive news) that predicts exchange rate most successfully. This predictability is evident during both recessions and expansions but is stronger during: (a) recessions; and (b) extreme depreciations and appreciations. In additional analysis, we use data for 26 OECD countries and discover that both positive and negative news are important, predicting rates for 62% and 46% of countries, respectively. A range of robustness tests confirm the importance of news in predicting exchange rates.

Quantification of Air Change Rate by Selected Methods in a Typical Apartment Building

An important parameter that affects indoor climate of buildings and also ventilation heat losses and gains is the speed of air change between the outdoor environment and the interior of buildings. Indoor air quality is therefore significantly associated with ventilation. Quantification of air change rate is complicated, because it is impacted by many parameters, the most variable of which is air flow. This study focuses on the determination and comparison of air change rate values in two methods by quantification of the aerodynamic coefficient Cp = Cpe − Cpi, so-called “aerodynamic quantification of the building” and the methodology based on “experimental measurements of carbon dioxide”. The study describes and takes into account the effect of wind, building parameters and air permeability for the building using “aerodynamic quantification of the building”. The paper compares these calculated results with the values obtained from experimental measurements method of carbon dioxide in a selected reference room in apartment building and evaluates the accuracy of the prediction of the air exchange rate obtained by these methods. At higher wind speeds the values of air change rate with considering the effect of openings are closer to the values obtained based on experimental measurements of carbon dioxide and the difference between the values without considering the effect of openings increases significantly.

Artificial Neural Network Model with PSO as a Learning Method to Predict Movement of the Rupiah Exchange Rate against the US Dollar

The movement of currency exchange rate can be predicted in the next few days, this is used by economic actors to get profit. Artificial Neural Network with the backpropagation learning method is good enough to use for forecasting time series data, it's just that in its application this method was considered to have shortcomings such as a long training time to achieve convergence. The purpose of this research is to form a Multilayer Perceptron Artificial Neural Network model with the Particle Swarm Optimization (PSO) algorithm as a learning method in the case of currency exchange rate prediction. This research produced a model that can predict the movement of the Rupiah exchange rate against the US Dollar, while the model formed was the MLP-PSO model with an error rate of 5.6168 x 10-8, slightly better than the MLP-BP model with an error rate of 6.4683 x 10-8. These results indicated that the PSO algorithm can be used as a learning algorithm in the Multilayer Perceptron Artificial Neural Network.

Non-stationary exchange rate prediction using soft computing techniques

Non-stationary exchange rate prediction using soft computing techniques

Web Intelligent for Forecasting Exchange Rate Currency using Clever Extraction Agent Combine with Financial Data Mining

From the current economic climate results in fluctuations of currency exchange rates in all countries. Since the most countries use USD as the reference exchange rate. The exchange rate will change from day to day so variety of factors which affect the exchange rate forecasting in the exchange rates in advance are critical to evaluate for the impact of the economic system of each country. It is important for investment decisions, exports, and profitability in the money market. It was reported on website (www) in the daily exchange rate changes. We use clever search agent (CSA) gather information from financial website generate to financial data mining. Kohonen Neural Networks is the method to determine similarity of internet documents using pattern index of financial document. And Ontology Structure of Sentence is the method to determine keyword using pattern index of financial content. Both are important components of Financial Data Mining. It is analyzed for exchange rate forecasting about USD/ Pounds. Our experimental forecast exchange rates for currency's USD / Great Britain Pounds by compare three algorithms as fallows GA, Meiosis Genetic Algorithms (MGA). This research propose new algorithm is called Dash Predator Swarm Optimization (DP2SO) which are accurate in prediction than other methods in generation of Genetic algorithm (GA) 35.83-41.52% which it depend on the accuracy of the information in each factor which are important finance dataset. It will present the future trends of exchange rate to the individual website.

Co-movement, fractal behaviour and forecasting of exchange rates

In this paper we analyse the fractal behaviour and correlations of exchange rates and develop a new approach to obtain better forecast results with small errors. Multifractal de-trended fluctuation analysis (MFDFA) is used to get fractal time series and their Hurst Exponents in specific time period and based on these results. We established Vector Autoregressive Fractionally Integrated Moving Average (VARFIMA) models of exchange rates on certain dates. Hurst Exponents allows us to detect small fluctuations (persistent characteristic) of time series and their multifractal behaviour. We consider, USD/CAD, USD/GBP, USD/EUR and USD/CHF exchange rates prices are as time series. Our theory is to show that if a time series has the highest multifractal degree then it has the most succesfull prediction results for the time interval which four time series have highest co-movement. We use Wavelet coherence analysis to understand co-movement of the exchange rates. The findings of Wavelet Coherence showed correlation between these exchange rates, the relationships among the two and four rates (with multiple wavelet coherence-MWC) and how these connections differ in the space of frequency-time. These relations and MFDFA results authorize us to construct VARFIMA models of exchange rate data which produce effective forecast outcomes with small errors.

Directional predictability in foreign exchange rates of emerging markets: New evidence using a cross-quantilogram approach

This study investigates the directional predictability of exchange rates in emerging markets. Using a cross-quantilogram model, we show that dependencies among emerging markets exchange rates are heterogeneous. Specifically, the Mexican peso, Brazilian real, and Turkish lira are leading emerging market currencies that provide hedging opportunities for currency investors. The structural dependencies across the pairs of exchange rates are evident at lag 1, and the relationships dissipate at longer lags. Secondly, the partial cross-quantilogram results indicate that oil is not a driving force of interrelationship among the exchange rates. Furthermore, the estimations of cross-quantile correlations from recursive subsamples reveal time-variant traits. If policymakers and financial regulators focus on comovements among emerging market currencies and distinguish net recipients from net transmitters in different environments, they can devise a surveillance system to adjust the market interdependence effects across emerging market foreign exchange rates. Therefore, they can promote the stability of emerging market currencies.

A machine learning-based framework for exchange rate analysis and prediction

Interests towards exchange rate (ER) analysis and prediction have increased in past decades. Theoretical and empirical literature support the relationship between ER and trading goods activity (imports and exports). However, there has not been any approach identifying the most important trading goods in order to forecast the ER based on these goods. Through a case study using the Guatemalan quetzal against the US dollar, this paper aims to predict the ER based on relevant trading goods and provide an organised database of the desired results. Using principal component analysis (PCA), we predict the ER with a low approximation error and determine which production sector should be the focal point for the country. We also provide theoretical foundations for a proper ER forecasting implementation using genetic algorithm (GA), lower–upper decomposition, and Lagrange and spline interpolation. The empirical results show manufacturing industries and exports to El Salvador and Honduras as the most relevant trading goods. This work could be beneficial and useful to national institutions, policy makers, and other academics or analysts who may utilise the data for further analysis.

An Effective Hybrid Approach for Forecasting Currency Exchange Rates

Accurately forecasting the movement of exchange rates is of interest in a variety of fields, such as international business, financial management, and monetary policy, though this is not an easy task due to dramatic fluctuations caused by political and economic events. In this study, we develop a new forecasting approach referred to as FSPSOSVR, which is able to accurately predict exchange rates by combining particle swarm optimization (PSO), random forest feature selection, and support vector regression (SVR). PSO is used to obtain the optimal SVR parameters for predicting exchange rates. Our analysis involves the monthly exchange rates from January 1971 to December 2017 of seven countries including Australia, Canada, China, the European Union, Japan, Taiwan, and the United Kingdom. The out-of-sample forecast performance of the FSPSOSVR algorithm is compared with six competing forecasting models using the mean absolute percentage error (MAPE) and root mean square error (RMSE), including random walk, exponential smoothing, autoregressive integrated moving average (ARIMA), seasonal ARIMA, SVR, and PSOSVR. Our empirical results show that the FSPSOSVR algorithm consistently yields excellent predictive accuracy, which compares favorably with competing models for all currencies. These findings suggest that the proposed algorithm is a promising method for the empirical forecasting of exchange rates. Finally, we show the empirical relevance of exchange rate forecasts arising from FSPSOSVR by use of foreign exchange carry trades and find that the proposed trading strategies can deliver positive excess returns of more than 3% per annum for most currencies, except for AUD and NTD.

Prediction of Rupiah Exchange Rate against Australian Dollar using the Chen Fuzzy Time Series Method

The exchange rate of the Rupiah (IDR) to the Australian Dollar is the price of one Rupiah translated to the Australian Dollar. Weakening or decreasing prices of the Rupiah to the Australian Dollar are influenced by various factors, such as government policies and expectations. Therefore, the government or related parties must be careful in predicting the value of the Rupiah exchange rate to the currency of the state, such as the Australian Dollar. This report will discuss the prediction of the Rupiah exchange rate to the Australian Dollar for 223 days from December 2018 to July 2019 using the Chen fuzzy time series method. The results of this report are the prediction of the value of the exchange rate of the Rupiah to the Australian Dollar in December 2018 to July 2019 is be again in the interval 10,450 IDR to 10,920 IDR.

Application of Google Trends‐based sentiment index in exchange rate prediction

AbstractThis study explores the possibilities of applying Google Trends to exchange rate forecasting. Specifically, we construct a sentiment index by using Google Trends to capture market sentiment in Japan and the United States. We forecast the USD/JPY rates using three structural models and two autoregressive models and examine whether our sentiment index can improve the predictive power of these models. We also check the robustness of the main results using the Taylor rule‐based model and rolling regression methodology. The data we use run from January 2004 to August 2018, treating January 2004 to February 2011 as the training sample and March 2011 to August 2018 as the forecast sample. We find that the addition of the sentiment index into these models decreases the mean squared prediction error. We also test the sentiment indices of different word numbers and find that the 25‐ and 30‐word indices perform best; in particular, the 30‐word index improves all the models tested in this study.

Investigating Exchange Rate Dynamics in Mozambique: A Hybrid Model Approach

The purpose of this article is to explain the dynamics of the exchange rate in Mozambique. The applied VAR model reveals that, in fact, it is the exchange rate that determines the macroeconomic variables (fundamentals), and not the opposite (at least in the short run), as predicted in exchange rate monetary models. Variables based on market microstructure (Order Flow) have the potential to predict exchange rates, as the IRF shows a significant and consistent reaction of exchange rates to structural shocks on order flow (demand pressure), while the exchange rate reacts not only significantly less, but also in the opposite direction to that predicted in the models based on fundamentals. These results are consistent in the long run, as demonstrated by the VECM model. In addition, the exchange rate forecast, based on a hybrid model which combines macro and micro variables, exceeds the benchmark model (random walk). Furthermore, since agents’ interpretations of fundamentals are incorporated in order flows, this appears to be the transmission mechanism between macroeconomic indicators and exchange rates, and in this way, order flow helps to clarify the exchange rate puzzle identified in the literature. Finally, this article suggests the use of microstructure variables of the foreign exchange market to predict exchange rates, as well as using them as a proxy for expectations, which can contribute to effective implementation of forward-looking monetary policy.

Selective Attention in Exchange Rate Forecasting

We analyze the exchange rate forecasting performance under the assumption of selective attention. Although currency markets react to a variety of different information, we hypothesize that market participants process only a limited amount of information. Our analysis includes more than 100,000 news articles relevant to the six most-traded foreign exchange currency pairs for the period of 1979–2016. We employ a dynamic model averaging approach to reduce model selection uncertainty and to identify time-varying probability to include regressors in our models. Our results show that smaller sizes models accounting for the presence of selective attention offer improved fitting and forecasting results. Specifically, we document a growing impact of foreign trade and monetary policy news on the euro/dollar exchange rate following the global financial crisis. Overall, our results point to the existence of selective attention in the case of most currency pairs.

Forecasting exchange rates for Central and Eastern European currencies using country‐specific factors

AbstractThis study builds on two strands of the literature regarding exchange rates—developing methods to forecast them and attempting to find a link between exchange rates and macroeconomic fundamentals (i.e., addressing so called “exchange rate disconnect puzzle”). We propose looking separately at its global component (common for all the currencies) and the local component (country‐specific one) instead of modeling and forecasting the exchange rate directly. We demonstrate that in the last few years, local factors have been gaining importance in shaping the exchange rate returns for the Polish Zloty, Hungarian Forint, Czech Koruna, and Romanian Leu. We further show that the main drivers of the local component of exchange rate returns are the future values of the gross domestic product growth rate and consumer price index inflation. Using principal component analysis combined with linear regression, we exploit this tendency for forecasting purposes. Our novel approach yields superior results compared to the random walk in out‐of‐sample forecasting exercise at horizons of 1 month to over a year in the case of Central and Eastern European currencies. The results withstand the sensitivity and robustness analysis.

A SUGGESTION FOR A DYNAMIC MULTIFACTOR MODEL (DMFM)

We provide a new way of deriving a number of dynamic unobserved factors from a set of variables. We show how standard principal components may be expressed in state space form and estimated using the Kalman filter. To illustrate our procedure, we perform two exercises. First, we use it to estimate a measure of the current account imbalances among northern and southern euro area countries that developed during the period leading up to the outbreak of the euro area crisis, before looking at adjustment in the post-crisis period. Second, we show how these dynamic factors can improve forecasting of the euro exchange rate.