Academic literature on the topic 'Foreign exchange rates – Forecasting – Stastistical methods'
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Journal articles on the topic "Foreign exchange rates – Forecasting – Stastistical methods"
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Sewell, Martin, and John Shawe-Taylor. "Forecasting foreign exchange rates using kernel methods." Expert Systems with Applications 39, no. 9 (July 2012): 7652–62. http://dx.doi.org/10.1016/j.eswa.2012.01.026.
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HUANG, WEI, K. K. LAI, Y. NAKAMORI, and SHOUYANG WANG. "FORECASTING FOREIGN EXCHANGE RATES WITH ARTIFICIAL NEURAL NETWORKS: A REVIEW." International Journal of Information Technology & Decision Making 03, no. 01 (March 2004): 145–65. http://dx.doi.org/10.1142/s0219622004000969.
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Forecasting exchange rates is an important financial problem that is receiving increasing attention especially because of its difficulty and practical applications. Artificial neural networks (ANNs) have been widely used as a promising alternative approach for a forecasting task because of several distinguished features. Research efforts on ANNs for forecasting exchange rates are considerable. In this paper, we attempt to provide a survey of research in this area. Several design factors significantly impact the accuracy of neural network forecasts. These factors include the selection of input variables, preparing data, and network architecture. There is no consensus about the factors. In different cases, various decisions have their own effectiveness. We also describe the integration of ANNs with other methods and report the comparison between performances of ANNs and those of other forecasting methods, and finding mixed results. Finally, the future research directions in this area are discussed.
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Gardner, Nicholas R., Jonathan D. Ritschel, Edward D. White, and Andrew T. Wallen. "Forecasting foreign currency exchange rates for department of defense budgeting1." Journal of Public Procurement 17, no. 3 (April 1, 2017): 315–36. http://dx.doi.org/10.1108/jopp-17-03-2017-b002.
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This paper examines the opportunity cost of applying simple averages in formulating the Department of Defense (DoD) budget for foreign exchange rates. Using out-of-sample validation, we evaluate the status quo of a center-weighted average against a Random Walk model, ARIMA, forward rates, futures contracts, and a private firm's forecasts over two time periods extending from Fiscal Year (FY) 1991 to FY 2014. The results strongly indicate that four of the alternative methods outperform the status quo over the shorter time period, and three methods for both time periods. Furthermore, a non-parametric comparison of the median error demonstrates statistical similarities between the four alternative methods over the short term. Overall, the paper recommends using the futures option prices to decrease forecast error by 3.23% and avoiding a $34 million opportunity cost.
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Chen, An-Sing, and Mark T. Leung. "Dynamic Foreign Currency Trading Guided by Adaptive Forecasting." Review of Pacific Basin Financial Markets and Policies 01, no. 03 (September 1998): 383–418. http://dx.doi.org/10.1142/s0219091598000247.
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The difficulty in predicting exchange rates has been a long-standing problem in international finance as most standard econometric methods are unable to produce significantly better forecasts than the random walk model. Recent studies provide some evidence for the ability of multivariate time-series models to generate better forecasts. At the same time, artificial neural network models have been emerging as alternatives to predict exchange rates. In this paper we propose a nonlinear forecast model combining the neural network with the multivariate econometric framework. This hybrid model contains two forecasting stages. A time series approach based on Bayesian Vector Autoregression (BVAR) models is applied to the first stage of forecasting. The estimates from BVAR are then used by the nonparametric General Regression Neural Network (GRNN) to generate enhanced forecasts. To evaluate the economic impact of forecasts, we develop a set of currency trading rules guided by these models. The optimal conditions implied by the investment rules maximize the expected profits given the expected changes in exchange rates and the interest rate differentials between domestic and foreign countries. Both empirical and simulation experiments suggest that the proposed nonlinear adaptive forecasting model not only produces better forecasts but also results in higher investment returns than other types of models. The effect of risk aversion is also considered in the investment simulation.
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Szóstakowski, Robert. "The use of the Hurst exponent to investigate the quality of forecasting methods of ultra-high-frequency data of exchange rates." Przegląd Statystyczny 65, no. 2 (January 30, 2019): 200–223. http://dx.doi.org/10.5604/01.3001.0014.0536.
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Over the last century a variety of methods have been used for forecasting financial time data series with different results. This article explains why most of them failed to provide reasonable results based on fractal theory using one day tick data series from the foreign exchange market. Forecasting AMAPE errors and forecasting accuracy ratios were calculated for statistical and machine learning methods for currency time series which were divided into sub-segments according to Hurst ratio. This research proves that the forecasting error decreases and the forecasting accuracy increases for all of the forecasting methods when the Hurt ratio increases. The approach which was used in the article can be successfully applied to time series forecasting by indicating periods with the optimal values of the Hurst exponent.
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EFENDI, RISWAN, ZUHAIMY ISMAIL, and MUSTAFA MAT DERIS. "IMPROVED WEIGHT FUZZY TIME SERIES AS USED IN THE EXCHANGE RATES FORECASTING OF US DOLLAR TO RINGGIT MALAYSIA." International Journal of Computational Intelligence and Applications 12, no. 01 (March 2013): 1350005. http://dx.doi.org/10.1142/s1469026813500053.
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Foreign exchange rate (forex) forecasting has been the subject of several rigorous investigations due to its importance in evaluating the benefits and risks of the international business environments. Many methods have been researched with the ultimate goal being to increase the reliability and efficiency of the forecasting method. However as the data are inherently dynamic and complex, the development of accurate forecasting method remains a challenging task if not a formidable one. This paper proposes a new weight of the fuzzy time series model for a daily forecast of the exchange rate market. Through this method, the weights are assigned to the fuzzy relationships based on a probability approach. This can be implemented to carry out the frequently recurring fuzzy logical relationship (FLR) in the fuzzy logical group (FLG). The US dollar to the Malaysian Ringgit (MYR) exchange rates are used as an example and the efficiency of the proposed method is compared with the methods proposed by Yu and Cheng et al. The result shows that the proposed method has enhanced the accuracy and efficiency of the daily exchange rate forecasting opportunities.
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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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Putri, Kristina Sanjaya, and Siana Halim. "Currency movement forecasting using time series analysis and long short-term memory." International Journal of Industrial Optimization 1, no. 2 (August 21, 2020): 71. http://dx.doi.org/10.12928/ijio.v1i2.2490.
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Foreign exchange is one type of investment, which its goal is to minimize losses that could occur. Forecasting is a technique to minimize losses when investing. The purpose of this study is to make foreign exchange predictions using a time series analysis called Auto-Regressive Integrated Moving Average (ARIMA) and Long Short-term memory methods. This study uses the daily EUR / USD exchange rates from 2014 to March 2020. The data are used as the model to predict the value of the foreign exchange market in April 2020. The model obtained will be used for predictions in April 2020, where the RMSE values obtained from time series analysis (ARIMA) with a window size of 100 days and LSTM sequentially as follows 0.00527 and 0.00509. LSTM produces lower RMSE values than ARIMA. LSTM has better prediction results; this is because the LSTM has the ability to learn so that it can utilize a large amount of data while ARIMA cannot use it. ARIMA does not have the ability to learn even though given a large amount of data it gives poor forecasting results. The ARIMA prediction is the same as the values of the previous day.
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Lin, Hualing, Qiubi Sun, and Sheng-Qun Chen. "Reducing Exchange Rate Risks in International Trade: A Hybrid Forecasting Approach of CEEMDAN and Multilayer LSTM." Sustainability 12, no. 6 (March 20, 2020): 2451. http://dx.doi.org/10.3390/su12062451.
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In international trade, it is common practice for multinational companies to use financial market instruments, such as financial derivatives and foreign currency debt, to hedge exchange rate risks. Making accurate predictions and decisions on the direction and magnitude of exchange rate movements is a more direct way to reduce exchange rate risks. However, the traditional time series model has many limitations in forecasting exchange rate, which is nonlinear and nonstationary. In this paper, we propose a new hybrid model of complete ensemble empirical mode decomposition (CEEMDAN) based multilayer long short-term memory (MLSTM) networks. It overcomes the shortcomings of the classic methods. CEEMDAN not only solves the mode mixing problem of empirical mode decomposition (EMD), but also solves the residue noise problem which is included in the reconstructed data of ensemble empirical mode decomposition (EEMD) with less computation cost. MLSTM can learning more complex dependences from exchange rate data than the classic model of time series. A lot of experiments have been conducted to measure the performance of the proposed approach among the exchange rates of British pound, the Australian dollar, and the US dollar. In order to get an objective evaluation, we compared the proposed method with several standard approaches or other hybrid models. The experimental results show that the CEEMDAN-based MLSTM (CEEMDAN–MLSTM) goes on better than some state-of-the-art models in terms of several evaluations.
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Попова and Anna Popova. "VAR As a Tool to Assess the Market Risk of Trading Positions of a Commercial Bank." Economics 4, no. 2 (April 18, 2016): 58–64. http://dx.doi.org/10.12737/18769.
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Forecasting of any risk is the crucial activity for any commercial bank. In current
situation market risk is an important element needed to be analyzed. The
probability of this type of risk may be affected by the change in the market value
of financial instruments and by the volatility of foreign exchange rates. Nowadays
in Russia each organization should conduct proper risk-management and be able
to predict possible losses. The article presents the assessment of the market risk
by the example of the price of the common share of the Bank of Moscow.
Forecasting is implemented by three models: ARIMA, Value-at-Risk and VAR.
Scientific novelty of this article is in comparison of the prediction procedures
of above mentioned methods. The result obtained during the analysis shows,
that the model Value-at-Risk is efficient for a short period of forecasting and
should be combined with others models in order to get more accurate results.
Dissertations / Theses on the topic "Foreign exchange rates – Forecasting – Stastistical methods"
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Du, Toit Cornel. "Non-parametric volatility measurements and volatility forecasting models." Thesis, Stellenbosch : Stellenbosch University, 2005. http://hdl.handle.net/10019.1/50401.
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Assignment (MComm)--Stellenbosch University, 2005.ENGLISH ABSTRACT: Volatilty was originally seen to be constant and deterministic, but it was later realised that return series are non-stationary. Owing to this non-stationarity nature of returns, there were no reliable ex-post volatility measurements. Subsequently, researchers focussed on ex-ante volatility models. It was only then realised that before good volatility models can be created, reliable ex-post volatility measuremetns need to be defined. In this study we examine non-parametric ex-post volatility measurements in order to obtain approximations of the variances of non-stationary return series. A detailed mathematical derivation and discussion of the already developed volatility measurements, in particular the realised volatility- and DST measurements, are given In theory, the higher the sample frequency of returns is, the more accurate the measurements are. These volatility measurements referred to above, however, all have short-comings in that the realised volatility fails if the sample frequency becomes to high owing to microstructure effects. On the other hand, the DST measurement cannot handle changing instantaneous volatility. In this study we introduce a new volatility measurement, termed microstructure realised volatility, that overcomes these shortcomings. This measurement, as with realised volatility, is based on quadratic variation theory, but the underlying return model is more realistic.
AFRIKAANSE OPSOMMING: Volatiliteit is oorspronklik as konstant en deterministies beskou, dit was eers later dat besef is dat opbrengste nie-stasionêr is. Betroubare volatiliteits metings was nie beskikbaar nie weens die nie-stasionêre aard van opbrengste. Daarom het navorsers gefokus op vooruitskattingvolatiliteits modelle. Dit was eers op hierdie stadium dat navorsers besef het dat die definieering van betroubare volatiliteit metings 'n voorvereiste is vir die skepping van goeie vooruitskattings modelle. Nie-parametriese volatiliteit metings word in hierdie studie ondersoek om sodoende benaderings van die variansies van die nie-stasionêre opbrengste reeks te beraam. 'n Gedetaileerde wiskundige afleiding en bespreking van bestaande volatiliteits metings, spesifiek gerealiseerde volatiliteit en DST- metings, word gegee. In teorie salopbrengste wat meer dikwels waargeneem word tot beter akkuraatheid lei. Bogenoemde volatilitieits metings het egter tekortkominge aangesien gerealiseerde volatiliteit faal wanneer dit te hoog raak, weens mikrostruktuur effekte. Aan die ander kant kan die DST meting nie veranderlike oombliklike volatilitiet hanteer nie. Ons stel in hierdie studie 'n nuwe volatilitieits meting bekend, naamlik mikro-struktuur gerealiseerde volatiliteit, wat nie hierdie tekortkominge het nie. Net soos met gerealiseerde volatiliteit sal hierdie meting gebaseer wees op kwadratiese variasie teorie, maar die onderliggende opbrengste model is meer realisties.
Books on the topic "Foreign exchange rates – Forecasting – Stastistical methods"
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Manzur, Meher. Exchange rates, prices, and world trade: New methods, evidence, and implications. London: Routledge, 1993.
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Manzur, Meher. Exchange Rates, Prices and World Trade: New Methods, Evidence and Implications. Routledge, 1992.
Find full textBook chapters on the topic "Foreign exchange rates – Forecasting – Stastistical methods"
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Chen, Yuehui, Peng Wu, and Qiang Wu. "Foreign Exchange Rate Forecasting Using Higher Order Flexible Neural Tree." In Artificial Higher Order Neural Networks for Economics and Business, 94–112. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-59904-897-0.ch005.
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Forecasting exchange rates is an important financial problem that is receiving increasing attention especially because of its difficulty and practical applications. In this chapter, we apply Higher Order Flexible Neural Trees (HOFNTs), which are capable of designing flexible Artificial Neural Network (ANN) architectures automatically, to forecast the foreign exchange rates. To demonstrate the efficiency of HOFNTs, we consider three different datasets in our forecast performance analysis. The data sets used are daily foreign exchange rates obtained from the Pacific Exchange Rate Service. The data comprises of the US dollar exchange rate against Euro, Great Britain Pound (GBP) and Japanese Yen (JPY). Under the HOFNT framework, we consider the Gene Expression Programming (GEP) approach and the Grammar Guided Genetic Programming (GGGP) approach to evolve the structure of HOFNT. The particle swarm optimization algorithm is employed to optimize the free parameters of the two different HOFNT models. This chapter briefly explains how the two different learning paradigms could be formulated using various methods and then investigates whether they can provide a reliable forecast model for foreign exchange rates. Simulation results showed the effectiveness of the proposed methods.