Journal articles: 'Exchange rate forecasting'

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Reddy, Dr T. Koti. "Exchange Rate Forecasting." Indian Journal of Applied Research 1, no. 6 (October 1, 2011): 120–24. http://dx.doi.org/10.15373/2249555x/mar2012/41.

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Stallings, David. "Exchange rate forecasting." International Journal of Forecasting 8, no. 1 (June 1992): 116–17. http://dx.doi.org/10.1016/0169-2070(92)90019-6.

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Salah, Abbas Mohamed, Tarik Ahmed Rashid, and Shareef Maulod Shareef. "A Novel Hybrid Technique for Exchange Rate Forecasting." Journal of Zankoy Sulaimani - Part A 17, no. 4 (June 25, 2015): 165–80. http://dx.doi.org/10.17656/jzs.10434.

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Vilasuso, Jon. "Forecasting exchange rate volatility." Economics Letters 76, no. 1 (June 2002): 59–64. http://dx.doi.org/10.1016/s0165-1765(02)00036-8.

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Boothe, Paul, and Debra Glassman. "Comparing exchange rate forecasting models." International Journal of Forecasting 3, no. 1 (January 1987): 65–79. http://dx.doi.org/10.1016/0169-2070(87)90079-3.

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Cai, Charlie X., and Qi Zhang. "High-Frequency Exchange Rate Forecasting." European Financial Management 22, no. 1 (August 12, 2014): 120–41. http://dx.doi.org/10.1111/eufm.12052.

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Ortiz, Riz Rupert L. "The Accuracy Rate of Holt-Winters Model with Particle Swarm Optimization in Forecasting Exchange Rates." Journal of Computers 11, no. 3 (May 2016): 216–24. http://dx.doi.org/10.17706/jcp.11.3.216-224.

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Kouwenberg, Roy, Agnieszka Markiewicz, Ralph Verhoeks, and Remco C. J. Zwinkels. "Model Uncertainty and Exchange Rate Forecasting." Journal of Financial and Quantitative Analysis 52, no. 1 (February 2017): 341–63. http://dx.doi.org/10.1017/s0022109017000011.

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Exchange rate models with uncertain and incomplete information predict that investors focus on a small set of fundamentals that changes frequently over time. We design a model selection rule that captures the current set of fundamentals that best predicts the exchange rate. Out-of-sample tests show that the forecasts made by this rule significantly beat a random walk for 5 out of 10 currencies. Furthermore, the currency forecasts generate meaningful investment profits. We demonstrate that the strong performance of the model selection rule is driven by time-varying weights attached to a small set of fundamentals, in line with theory.

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Copeland, Laurence. "Exchange Rate Forecasting. Techniques and Applications." International Journal of Forecasting 18, no. 1 (January 2002): 153–54. http://dx.doi.org/10.1016/s0169-2070(01)00127-3.

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Zorzi, Michele Ca’, and Michał Rubaszek. "Exchange rate forecasting on a napkin." Journal of International Money and Finance 104 (June 2020): 102168. http://dx.doi.org/10.1016/j.jimonfin.2020.102168.

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Colombo, Emilio, and Matteo Pelagatti. "Statistical learning and exchange rate forecasting." International Journal of Forecasting 36, no. 4 (October 2020): 1260–89. http://dx.doi.org/10.1016/j.ijforecast.2019.12.007.

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Ca’ Zorzi, Michele, Marcin Kolasa, and Michał Rubaszek. "Exchange rate forecasting with DSGE models." Journal of International Economics 107 (July 2017): 127–46. http://dx.doi.org/10.1016/j.jinteco.2017.03.011.

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Miciuła, Ireneusz. "The Concept of FTS Anylysis in Forecasting Trends of Exchange Rate Changes." Economics & Sociology 7, no. 2 (May 20, 2014): 172–82. http://dx.doi.org/10.14254/2071-789x.2014/7-2/14.

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Adisetiawan, R., Pantun Bukit, and Ahmadi Ahmadi. "Future Spot Rate: The Implications in Indonesia." Jurnal Ilmiah Universitas Batanghari Jambi 20, no. 1 (February 5, 2020): 155. http://dx.doi.org/10.33087/jiubj.v20i1.874.

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Investors, multinational companies and governments require a rate forecasting to make informed decisions about the hedging of debts and receivables, funding and short-term investments, capital budgeting and long-term financing. The process of making forecasting from market indicators, known as market-based forecasting, is usually developed based on spot rates and forward rates. The current spot rate can be used as forecasting, as the exchange rate reflects the market estimate of the spot rate in a short period of time. The forward rate is used in forecasting, as the exchange rate reflects the market estimate of the spot rate at the end of the forecasting period. Based on the research conducted by Chiang (1986) of the samples used, empirical evidence indicates spot rates and forward rates are significant as predictors of future spots. Empirical evidence suggests that spot rates provide better forecasting results compared to forward rates. The research uses regression models for market-based forecasting methods. The variables used in this study are spot rates, forward rates and future spots. The samples used are from Bank Indonesia for spot rates in January – March 2019 and future spot in April – June 2019, and from Jakarta Futures exchange for forward rates in January – March 2019. The Stochastic and Chow Test models are selected and their use has been evaluated using quality and precise testing measures. Based on the sample period used, empirical evidence suggests that spot rates and forward rates are significant in predicting future spots for EUR, JPY and AUD currencies. Current spot rates provide better forecasting results in predicting Future spot compared to the forward rate. Both the 15Ft"> and 15St"> coefficient are sensitive to new information from the variation of the coefficient and time, it can increase the forecasting of the equation to each currency exchange rate used. The study states that variables from time series should be effectively utilized and utilized in predicting currency exchange rates, as this research demonstrates the absence of dependence on time series Can be concluded that foreign exchange rates in each country follow a pattern that is not stationary. The spot Euro exchange rate turns out to be statistically more accurate with an error rate of 0.004144% forecasting with the value of regression coefficient of Euro exchange rate is a Future Spot = 21.504,88 – 0.341229Spot + 15et+1"> .

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Mačerinskienė, Irena, and Andrius Balčiūnas. "Exchange Rate Forecasting with Information Flow Approach." Verslas: Teorija ir Praktika 17, no. 2 (June 20, 2016): 109–16. http://dx.doi.org/10.3846/btp.2016.554.

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The purpose of this article is to assess exchange rate forecasting possibilities with an information flow approach model. In the model the three types of information flows are distinguished: fundamental analysis information flow through particular macroeconomic determinants, microstructure approach information flow through dealer clients’ positioning data, technical analysis information flow through technical indicators. By using regression analysis it is shown that the composed model can forecast the exchange rate, the most significant information flows are distinguished. The results lead to further development of the information flow approach as a tool to forecast exchange rate fluctuations.

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Plakandaras, Vasilios, Theophilos Papadimitriou, Periklis Gogas, and Konstantinos Diamantaras. "Market sentiment and exchange rate directional forecasting." Algorithmic Finance 4, no. 1-2 (2015): 69–79. http://dx.doi.org/10.3233/af-150044.

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Jurado-Sánchez, Omar Shatagua, Cornelio Yáñez-Márquez, Oscar Camacho-Nieto, and Itzamá López-Yáñez. "Currency Exchange Rate Forecasting using Associative Models." Research in Computing Science 78, no. 1 (December 31, 2014): 67–76. http://dx.doi.org/10.13053/rcs-78-1-6.

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Moosa, Imad A., and John J. Vaz. "Cointegration, error correction and exchange rate forecasting." Journal of International Financial Markets, Institutions and Money 44 (September 2016): 21–34. http://dx.doi.org/10.1016/j.intfin.2016.04.007.

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Lanne, Markku. "Forecasting realized exchange rate volatility by decomposition." International Journal of Forecasting 23, no. 2 (April 2007): 307–20. http://dx.doi.org/10.1016/j.ijforecast.2007.02.001.

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HUI, LIM XIN, and BINYAMIN YUSOFF. "EXCHANGE RATE FORECASTING USING FUZZY TIME SERIES-MARKOV CHAIN." Universiti Malaysia Terengganu Journal of Undergraduate Research 3, no. 3 (July 31, 2021): 183–94. http://dx.doi.org/10.46754/umtjur.v3i3.230.

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Exchange rate forecasting plays an important role in financial management. However, it is a complex process with high nonlinearity and data irregularity. Moreover, the forecasting of exchange rate is highly involved with imprecise and uncertain data. Analysis of forecasting models which corresponds to the exchange rate has always experienced fluctuations. Therefore, exchange rate forecasting becomes a challenging task in finance. Several studies have shown that stand-alone forecasting models such as time series, fuzzy time series, and Markov chain have their own drawbacks and are not successful enough in forecasting accurately. In this study, we propose a hybrid model of fuzzy time series-Markov chain to forecast the future exchange rate. Fuzzy time series-Markov chain is a combination of the classic fuzzy time series model with Markov chain model used to analyse a set of time series data. The main motivation for this study is to improve the accuracy in exchange rate forecasting. The selected currencies are Malaysian Ringgit (MYR) and Singapore Dollar (SGD). The proposed model was then evaluated by the Mean Absolute Percentage Error (MAPE) performance metric to test the robustness of the model. Lastly, a comparison between the proposed model and fuzzy time series model was conducted with respect to the MAPE. The results showed that the MAPE value for fuzzy time series-Markov chain was 0.9895% which fell under the criterion of highly accurate forecasting. Meanwhile, the MAPE value for fuzzy time series was 3.4306%. Thus, the forecasting performance of the proposed model was better than the fuzzy time series model. This study reveals the potential benefits of the proposed model as a highly accurate forecasting model.

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Trimono, Trimono, Abdulah Sonhaji, and Utriweni Mukhaiyar. "FORECASTING FARMER EXCHANGE RATE IN CENTRAL JAVA PROVINCE USING VECTOR INTEGRATED MOVING AVERAGE." MEDIA STATISTIKA 13, no. 2 (December 28, 2020): 182–93. http://dx.doi.org/10.14710/medstat.13.2.182-193.

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Farmer Exchange Rate (FER) is an indicator that can be used to measure the level of farmers welfare. For every agriculture sector, FER is affected by the historical price of harvest from the corresponding sector and historical prices of other agriculture sectors. In Central Java Province, rice & palawija, horticulture, and fisheries are the largest agriculture sectors which is the main livelihood for most of the population. FER forecasting is a crucial thing to determine the level of farmers welfare in the future. One method that can be used to predict the value of a variable that is influenced by the historical value of several variables is Vector Time Series. An empirical study was conducted using FER data from the rice & palawija, horticulture and fisheries sectors for January 2011-June 2017 in Central Java Province. The results obtained show that by using the VIMA(2.1) model, the FER prediction was very accurate, with MAPE values were 1.91% (rice & palawija sector), 2.44% (horticulture sector), and 2.18% (fisheries sector).

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Lemonjava, Givi. "TIME SERIES MODELS FOR FORECASTING EXCHANGE RATES." Globalization and Business 4, no. 8 (December 27, 2019): 149–60. http://dx.doi.org/10.35945/gb.2019.08.020.

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This paper investigates the behavior of daily exchange rate of the Georgian Currency LARI (GEL) exchange rate against the USDand EUR. To forecast exchange rates there are numerous models, which tend from very simple to very complicated models for analysis of GEL/USD and GEL/EUR time series variable. The objective of this paper is to com- pare the performance of individual time series models for predictingexchange rates. We will investigate the application of following time series analysis models: moving average, ex- ponential smoothing, double exponential smoothing adjust- ed for trend, time-series decomposition models, and ARIMA class models. The forecasting ability of these models is subsequently assessed using the symmetric loss functions which are the Mean Absolute Percentage Error (MAPE), the Mean Absolute deviation (MAD), and the Mean Squared error /deviation (MSE/MSD). In some cases, predicting the direction of exchange rate change may be valuable and profitable. Hence, it is reasonable to look at the frequency of the correctpredicted direction of change by used models, for short - FCPCD. An exchange rate represents the price of one currency in terms of another. It reflects the ratio at which one currency can be exchanged with another currency. Exchange rates forecasting is a very important and challenging subject of finance market, to determine optimal government policies as well as to make business decisions. This is important for all that firms which having their business spread over different countries or for that which raise funds in different currency. Business people mainly use exchange rates forecasting results in following types of decisions like choice currency for invoicing, pricing transactions, borrowing and landing currency choice, and management of open currency positions. The forex market is made up of banks, commercial companies, central banks, investment management firms, hedge funds, and retail forex brokers and investors. Forecasting the short- run fluctuations and direction of change of the currency ex- change rates is important for all these participates. The main goal of this study is to forecast of future ex- change rate trends by using currency rates time-series, rep- resenting past trends, patterns and waves. The monetary policy of the National Bank of Georgia since 2009 have been followed the inflation targeting regime, where exchange rate regime is floating - change of exchange rate is free. The offi- cial exchange rate of the Georgian GEL against the USD is cal- culated each business day. The official exchange rate of GEL against USD is calculated as the average weighted exchange rate of the registered spot trades on the interbank market functioning within the Bloomberg trade platform. Then, the official exchange rate of GEL against other foreign currencies is determined according to the rate on international markets on the basis of cross-currency exchange rates.

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Ignatyuk, Anzhela, Valerii Osetskyi, Mykhaylo Makarenko, and Alina Artemenko. "Ukrainian hryvnia under the floating exchange rate regime: diagnostics of the USD/UAH exchange rate dynamics." Banks and Bank Systems 15, no. 3 (September 18, 2020): 129–46. http://dx.doi.org/10.21511/bbs.15(3).2020.12.

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The study identifies the features of the USD/UAH exchange rate dynamics for the period from January 2014 to May 2020. The main purpose of the empirical analysis is to determine the current trend of the USD/UAH exchange rate (is it random or permanent), indicate the presence of seasonality in foreign exchange rate dynamics and evaluate its sensitivity to external shocks. Three hypotheses are tested using several methods of time series analysis (autocorrelation analysis, ADF, Phillips-Perron and Granger tests), including a trend-season model using a time series of one variable (ARMA), a multifactor VAR-model, impulse functions. The results show that, the movement of the hryvnia exchange rate against the US dollar is a stochastic process. Its trend has a random component and tends to change sharply over time. Moreover, exchange rate fluctuations are seasonal. It depreciates in the first and second quarters, and strengthens in the third and fourth. Some macroeconomic indicators cause a positive or negative reaction of the USD/UAH exchange rate. This indicates that today the Ukrainian foreign exchange market is relatively efficient, but stable, since its reaction to external shocks is short-term, insignificant and tends to fade out. Although the findings are controversial, they support the generally accepted view that the exchange rate formation is a multifactorial process that depends on several macroeconomic factors. However, high volatility and random walk specification indicate that it is almost impossible to predict its future value at this time. AcknowledgmentThe material was prepared within the framework of the scientific research Modeling and Forecasting the Behavior of Financial Markets as an Information Base for Ensuring Financial Stability and Security of the State, No. 0117U003936 (supervisor Alex Plastun).

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Amran, Ikhwan Muzammil, and Anas Fathul Ariffin. "Forecasting Malaysian Exchange Rate using Artificial Neural Network." Jurnal Intelek 15, no. 2 (July 28, 2020): 136–45. http://dx.doi.org/10.24191/ji.v15i2.323.

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In todays fast paced global economy, the accuracy in forecasting the foreign exchange rate or predicting the trend is a critical key for any future business to come. The use of computational intelligence based techniques for forecasting has been proved to be successful for quite some time. This study presents a computational advance for forecasting the Foreign Exchange Rate in Kuala Lumpur for Ringgit Malaysia against US Dollar. A neural network based model has been used in forecasting the days ahead of exchange rate. The aims of this research are to make a prediction of Foreign Exchange Rate in Kuala Lumpur for Ringgit Malaysia against US Dollar using artificial neural network and determine practicality of the model. The Alyuda NeuroIntelligence software was utilized to analyze and to predict the data. After the data has been processed and the structural network compared to each other, the network of 2-4-1 has been chosen by outperforming other networks. This network selection criteria are based on Akaike Information Criterion (AIC) value which shows the lowest of them all. The training algorithm that applied is Quasi-Netwon based on the lowest recorded absolute training error. Hence, it is believed that experimental results demonstrate that Artificial Neural Network based model can closely predict the future exchange rate.

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Pfahler, Jonathan Felix. "Exchange Rate Forecasting with Advanced Machine Learning Methods." Journal of Risk and Financial Management 15, no. 1 (December 21, 2021): 2. http://dx.doi.org/10.3390/jrfm15010002.

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Historically, exchange rate forecasting models have exhibited poor out-of-sample performances and were inferior to the random walk model. Monthly panel data from 1973 to 2014 for ten currency pairs of OECD countries are used to make out-of sample forecasts with artificial neural networks and XGBoost models. Most approaches show significant and substantial predictive power in directional forecasts. Moreover, the evidence suggests that information regarding prediction timing is a key component in the forecasting performance.

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Gudan, Jovita. "Modeling and Forecasting Exchange Rates." Lietuvos statistikos darbai 55, no. 1 (December 20, 2016): 19–30. http://dx.doi.org/10.15388/ljs.2016.13864.

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This paper investigates models for the euro exchange rate against the currencies of Denmark, Poland, theUnited States, and the United Kingdom. The objective of this paper is to compare different methods of modeling andout-of-sample forecasting. One of the techniques is cointegration relation, which is implemented through a vector errorcorrection model. The existence of cointegration supports the long-run relationship between the nominal exchange rateand a number of fundamental variables. The evidence presented in this paper shows that a simple multivariate randomwalk model tends to have superior predictive performance, compared to other exchange rate models, for a period of lessthan one year.

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Helhel, Yesim, and Seref Kalayci. "Exchange Rate Forecasting Based on Fundamental Macroeconomic Variables in a Floating Exchange Rate Regime." International Journal of Social Ecology and Sustainable Development 3, no. 3 (July 2012): 15–21. http://dx.doi.org/10.4018/jsesd.2012070102.

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Developing countries had a fixed exchange rate regime and avoided financial liberalization until the 1990’s. In the early 2000’s however, most of the developing countries abandoned their fixed exchange rate regimes in favor of floating rate regimes which in turn increased the importance of exchange rate forecasting in the emerging market economies. This paper intends to explain TR/USD (Turkish Lira/American Dollar) exchange rates by using macroeconomic fundamentals for the period between February 2001 and December 2009 on a monthly basis. A Vector Auto Regression (VAR) method is used. Among the macroeconomic Fundamentals, United States Federal Reserve Benchmark interest rates, one month Turkish Treasury Bill yields, Turkish import/export rates, m2 money supply and foreign direct investment explain the changes in TR / USD exchange rates.

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KIANI, KHURSHID M. "FORECASTING FORWARD EXCHANGE RATE RISK PREMIUM IN SINGAPORE DOLLAR/US DOLLAR EXCHANGE RATE MARKET." Singapore Economic Review 54, no. 02 (June 2009): 283–98. http://dx.doi.org/10.1142/s0217590809003288.

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In this research, monthly forward exchange rates are evaluated for possible existence of time varying risk premia in Singapore forward foreign exchange rates against US dollar. The time varying risk premia in Singapore dollar is modeled using non-Gaussian signal plus noise models that encompass non-normality and time varying volatility. The results from signal plus noise models show statistically significant evidence of time varying risk premium in Singapore forward exchange rates although we failed to reject the hypotheses of no risk premium in the series. The results from Gaussian versions of these models are not much different and are in line with Wolff (1987) who also used the same methodology in Gaussian settings. Our results show statistically significant evidence of volatility clustering in Singapore forward exchange rates. The results from Gaussian signal plus noise models also show statistically significant evidence of volatility clustering and non-normality in Singapore forward foreign exchange rates. Additional tests on the series show that exclusion of conditional heteroskedasticity from the signal plus noise models leads to false statistical inferences.

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Rasheed, Abdul, Muhammad Asad Ullah, and Imam Uddin. "PKR Exchange Rate Forecasting Through Univariate and Multivariate Time Series Techniques." NICE Research Journal 13, no. 4 (December 25, 2020): 49–67. http://dx.doi.org/10.51239/nrjss.v13i4.226.

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This study aims to examine and compare the accuracy of time series and econometric forecasting models in the context of the exchange rate as we know that fluctuation in the exchange rate may affect the economic activities at the macro – level. For this purpose, the author has chosen the Pakistani Rupee exchange rate against United States Dollars with the annual data from 1980 to 2018. The results revealed that the exponential model provides the most effective accuracy in forecasting rather than the Naive, ARIMA and ARDL Co-integration model. This paper has also covered the gap of unavailability of literature regarding the application of ARDL and Exponential Smoothing model for the forecasting of the exchange rate in Pakistan. It is also anticipated that historical data do not play a vital role in the forecasting of the future trend of time series i.e. Pakistani Rupees against US Dollars. However, all three-time series anticipated that the recent observations play a significant role in the speculation of the upcoming future trend. Keywords: Forecasting, Exchange Rate, Naïve Model, ARDL Co-Integration model, Econometrics

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Wei, Yunjie, Shaolong Sun, Kin Keung Lai, and Ghulam Abbas. "A KELM-Based Ensemble Learning Approach for Exchange Rate Forecasting." Journal of Systems Science and Information 6, no. 4 (September 26, 2018): 289–301. http://dx.doi.org/10.21078/jssi-2018-289-13.

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Abstract In this paper, a KELM-based ensemble learning approach, integrating Granger causality test, grey relational analysis and KELM (Kernel Extreme Learning Machine), is proposed for the exchange rate forecasting. The study uses a set of sixteen macroeconomic variables including, import, export, foreign exchange reserves, etc. Furthermore, the selected variables are ranked and then three of them, which have the highest degrees of relevance with the exchange rate, are filtered out by Granger causality test and the grey relational analysis, to represent the domestic situation. Then, based on the domestic situation, KELM is utilized for medium-term RMB/USD forecasting. The empirical results show that the proposed KELM-based ensemble learning approach outperforms all other benchmark models in different forecasting horizons, which implies that the KELM-based ensemble learning approach is a powerful learning approach for exchange rates forecasting.

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Kim, Hyeyoen, and Doojin Ryu. "Forecasting Exchange Rate from Combination Taylor Rule Fundamental." Emerging Markets Finance and Trade 49, sup4 (September 2013): 81–92. http://dx.doi.org/10.2753/ree1540-496x4905s406.

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Faust, Jon, John Harold Rogers, and Jonathan H. Wright. "Exchange Rate Forecasting : The Errors We've Really Made." International Finance Discussion Paper 2001, no. 714 (December 2001): 1–35. http://dx.doi.org/10.17016/ifdp.2001.714.

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Tsuji, Chikashi. "Exchange Rate Forecasting via a Machine Learning Approach." iBusiness 14, no. 03 (2022): 119–26. http://dx.doi.org/10.4236/ib.2022.143009.

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Evans, Martin D. D., and Richard K. Lyons. "Meese-Rogoff Redux: Micro-Based Exchange-Rate Forecasting." American Economic Review 95, no. 2 (April 1, 2005): 405–14. http://dx.doi.org/10.1257/000282805774669934.

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Xu, Mingjuan, and Zhengyu Liu. "An Improved Dynamic Bayesian for Exchange Rate Forecasting." TELKOMNIKA (Telecommunication Computing Electronics and Control) 14, no. 3A (September 1, 2016): 369. http://dx.doi.org/10.12928/telkomnika.v14i3a.4410.

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HOGAN, LINDSAY I. "A Comparison of Alternative Exchange Rate Forecasting Models." Economic Record 62, no. 2 (June 1986): 215–23. http://dx.doi.org/10.1111/j.1475-4932.1986.tb00897.x.

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Nabney, Ian, Christian Dunis, Richard Dallaway, Swee Leong, and Wendy Redshaw. "Leading edge forecasting techniques for exchange rate prediction." European Journal of Finance 1, no. 4 (December 1995): 311–23. http://dx.doi.org/10.1080/13518479500000022.

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Faust, Jon, John H. Rogers, and Jonathan H. Wright. "Exchange rate forecasting: the errors we’ve really made." Journal of International Economics 60, no. 1 (May 2003): 35–59. http://dx.doi.org/10.1016/s0022-1996(02)00058-2.

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Panda, Chakradhara, and V. Narasimhan. "Forecasting exchange rate better with artificial neural network." Journal of Policy Modeling 29, no. 2 (March 2007): 227–36. http://dx.doi.org/10.1016/j.jpolmod.2006.01.005.

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Martens, Martin. "Forecasting daily exchange rate volatility using intraday returns." Journal of International Money and Finance 20, no. 1 (February 2001): 1–23. http://dx.doi.org/10.1016/s0261-5606(00)00047-4.

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Huang, Alex YiHou, Sheng-Pen Peng, Fangjhy Li, and Ching-Jie Ke. "Volatility forecasting of exchange rate by quantile regression." International Review of Economics & Finance 20, no. 4 (October 2011): 591–606. http://dx.doi.org/10.1016/j.iref.2011.01.005.

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Ghodsi, Mansi, and Masoud Yarmohammadi. "Exchange rate forecasting with optimum singular spectrum analysis." Journal of Systems Science and Complexity 27, no. 1 (February 2014): 47–55. http://dx.doi.org/10.1007/s11424-014-3303-6.

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Heiden, Sebastian, Christian Klein, and Bernhard Zwergel. "Beyond Fundamentals: Investor Sentiment and Exchange Rate Forecasting." European Financial Management 19, no. 3 (June 2013): 558–78. http://dx.doi.org/10.1111/j.1468-036x.2010.00593.x.

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Rime, Dagfinn, Lucio Sarno, and Elvira Sojli. "Exchange rate forecasting, order flow and macroeconomic information." Journal of International Economics 80, no. 1 (January 2010): 72–88. http://dx.doi.org/10.1016/j.jinteco.2009.03.005.

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Rossi, Barbara. "Exchange Rate Predictability." Journal of Economic Literature 51, no. 4 (December 1, 2013): 1063–119. http://dx.doi.org/10.1257/jel.51.4.1063.

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The main goal of this article is to provide an answer to the question: does anything forecast exchange rates, and if so, which variables? It is well known that exchange rate fluctuations are very difficult to predict using economic models, and that a random walk forecasts exchange rates better than any economic model (the Meese and Rogoff puzzle). However, the recent literature has identified a series of fundamentals/methodologies that claim to have resolved the puzzle. This article provides a critical review of the recent literature on exchange rate forecasting and illustrates the new methodologies and fundamentals that have been recently proposed in an up-to-date, thorough empirical analysis. Overall, our analysis of the literature and the data suggests that the answer to the question: “Are exchange rates predictable?” is, “It depends”—on the choice of predictor, forecast horizon, sample period, model, and forecast evaluation method. Predictability is most apparent when one or more of the following hold: the predictors are Taylor rule or net foreign assets, the model is linear, and a small number of parameters are estimated. The toughest benchmark is the random walk without drift. (JEL C53, F31, F37, E43, E52)

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Dautel, Alexander Jakob, Wolfgang Karl Härdle, Stefan Lessmann, and Hsin-Vonn Seow. "Forex exchange rate forecasting using deep recurrent neural networks." Digital Finance 2, no. 1-2 (March 27, 2020): 69–96. http://dx.doi.org/10.1007/s42521-020-00019-x.

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Abstract Deep learning has substantially advanced the state of the art in computer vision, natural language processing, and other fields. The paper examines the potential of deep learning for exchange rate forecasting. We systematically compare long short-term memory networks and gated recurrent units to traditional recurrent network architectures as well as feedforward networks in terms of their directional forecasting accuracy and the profitability of trading model predictions. Empirical results indicate the suitability of deep networks for exchange rate forecasting in general but also evidence the difficulty of implementing and tuning corresponding architectures. Especially with regard to trading profit, a simpler neural network may perform as well as if not better than a more complex deep neural network.

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Safi, Samir, Salisu Aliyu, Kekere Sule Ibrahim, and Olajide Idris Sanusi. "Can Oil Price Predict Exchange Rate? Empirical Evidence from Deep Learning." International Journal of Energy Economics and Policy 12, no. 4 (July 19, 2022): 482–93. http://dx.doi.org/10.32479/ijeep.13200.

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This paper critically analyses the predictability of exchange rates using oil prices. Extant literature that investigates the significance of oil prices in forecasting exchange rates remains largely inconclusive due to limitations arising from methodological issues. As such, this study uses deep learning approaches such as Multi-Layer Perceptron (MLP), Convolution Neural Network (CNN), and Long Short-Term Memory (LSTM) to predict exchange rates. In addition, the Empirical Mode Decomposition (EMD) of time series dataset was utilized to ascertain its effect on the quality of prediction. To examine the efficacy of using oil prices in forecasting exchange rates, bivariate models were also built. Of the three bivariate models developed, the EMD-CNN model has the best predictive performance. Results obtained show that oil price information has a strong influence on forecasting exchange rates.

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Sari, Yolanda, and Etik Winarni. "Perbandingan Kinerja Peramalan Kurs di Indonesia." Ekonomis: Journal of Economics and Business 6, no. 1 (March 23, 2022): 60. http://dx.doi.org/10.33087/ekonomis.v6i1.487.

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Fluctuations in the exchange rate on the money market, both appreciating and depreciating, indicate the volatility that occurs in a country's currency with the currencies of other countries. To overcome the magnitude of the impact of exchange rate fluctuations on the economy, a forecasting model is needed that can predict the exchange rate effectively. This study aims to find the exchange rate forecast that produces the best model in analyzing the exchange rate using the Box-Jenkins/ARIMA, ARCH and GARCH models. The data used in this study is secondary data in a time series pattern in the form of Rupiah/USD exchange rate data obtained from Bank Indonesia in daily form (five days a week), starting from January 2, 2015 to December 31, 2021 with out of sample starting from 3 January 2022 to December 31, 2024. Some of these models are compared with each other so that the best model is obtained, and the forecasting results are 782 days ahead. This study shows that the ARIMA (1,1,0) model is better at predicting the exchange rate than the ARCH (1) model which has the smallest RMSE, MAE and MAPE values. Forecasting results on January 3, 2022 are Rp. 14,298.22/USD with actual data of Rp. 14,270.00/USD. There is shadow forecasting starting from January 3, 2022 to February 11, 2022, so the comparison can be seen with the actual data. For investors, companies or parties with an interest in forecasting the exchange rate, they can use the ARIMA (1,1,0) model in predicting the exchange rate for forecasting several periods in the future.

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Gyamerah, Samuel Asante, and Edwin Moyo. "Long-Term Exchange Rate Probability Density Forecasting Using Gaussian Kernel and Quantile Random Forest." Complexity 2020 (February 28, 2020): 1–11. http://dx.doi.org/10.1155/2020/1972962.

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In the midst of macro-economic uncertainties, accurate long-term exchange rate forecasting is crucial for decision-making and planning. To measure the uncertainty associated with exchange rate and obtaining additional information of future exchange rate, a hybrid model based on quantile regression forest and Gaussian kernel (GQRF) is constructed. Quarterly dataset of KSh/USD exchange rate and macro-economic variables from 2007 to 2016 are used. The forecast horizon spans from 2013 to 2016. With a prediction interval coverage probability and prediction interval average width of 95% and 29.6493%, the constructed model has a very high coverage probability. The method of determining the probabilistic forecasts is very significant to achieve forecasts with correct coverage. The probability density forecasting model for the exchange rate gave significant information–the probability distribution of the forecasted results. In this way, uncertainties around the forecast can be evaluated because the complete exchange rate distribution are forecasted. GQRF is efficient as it can uphold the uncertainty about the variance linked to each point, which is important for exchange rate forecasting. Using the constructed model, the probabilities of exceedance such as the probability of future exchange rate exceeding the average exchange rate for the year can be computed. This paper also adds to the scarce literature of exchange rate probability density forecasting using machine learning techniques.

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Akhtar, Sohail, Maham Ramzan, Sajid Shah, Iftikhar Ahmad, Muhammad Imran Khan, Sadique Ahmad, Mohammed A. El-Affendi, and Humera Qureshi. "Forecasting Exchange Rate of Pakistan Using Time Series Analysis." Mathematical Problems in Engineering 2022 (August 24, 2022): 1–11. http://dx.doi.org/10.1155/2022/9108580.

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Exchange rates are crucial in regulating the foreign exchange market's dynamics. Because of the unpredictability and volatility of currency rates, the exchange rate prediction has become one of the most challenging applications of financial time series forecasting. This study aims to build and compare the accuracy of various methods. The time series model Auto-Regressive Integrated Moving Average (ARIMA) and Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH) are utilized to forecast the daily US dollar to Pakistan rupee currency exchange rates (USD/PKR). Lagged observations of the data series and moving average technical analysis are used in both models. Explanatory factors were used as indicators, and the prediction performance was assessed using a variety of commonly known statistical metrics. These statistical metrics suggested the presence of conditional heteroscedasticity. Thus, the process turns to capture the volatility effect of conditional heteroscedasticity through GARCH modeling. It may be inferred based on the results of tentative models; that the ARCH model outperforms the GARCH model in terms of predicting the USD/PKR exchange rate.

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Journal articles on the topic 'Exchange rate forecasting'

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