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Chala, A. V. "Classified forecasting exchange rate." Thesis, Видавництво СумДУ, 2012. http://essuir.sumdu.edu.ua/handle/123456789/26081.
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Marsh, Ian William. "Exchange rate forecasts and forecasting." Thesis, University of Strathclyde, 1994. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=21506.
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This thesis is concerned with forecasting key floating exchange rates. The first half is based on the predictions of almost two hundred forecasters, working in banks, industrial companies, chambers of commerce and specialist forecasting agencies. It demonstrates that individual forecasters interpret commonly available information differently, and that these differences of opinion translate into economically meaningful heterogeneity in forecast performance - some forecasters are significantly more accurate than others. It also shows that the dispersion of forecasts helps to explain turnover in the foreign exchange futures market. The notion that the best predictive model of the exchange rate is a random walk has stood the test of time. In chapter three we evaluate the forecasts of our panellists based on a variety of metrics, using the random walk as a benchmark. Over short horizons (three months) the random walk remains preeminent, but over a one year horizon several forecasters demonstrate an ability to outperform. In an attempt to overturn the short horizon results we combine forecasts using several techniques in chapter four, but to no avail. It would appear that we are unable to find any information that is not discounted into the current spot rate but which is relevant over short forecast intervals. The second half of the thesis builds three exchange rate models based on an augmented theory of purchasing power parity, with which we forecast key rates. The five variable, simultaneous equation models each incorporate long-run equilibria characterised by economically meaningful restrictions, and complex short term dynamics. The thesis demonstrates that these models are capable of generating fully dynamic forecasts which rank very favourably when compared to our panellists. More tellingly, it also shows that the forecasts are significantly better than a random walk over all but the shortest of horizons.
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Sager, Michael. "Exchange rate modelling and forecasting." Thesis, University of Warwick, 2004. http://wrap.warwick.ac.uk/1222/.
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The objective of this thesis is to assess the current state of exchange rate modelling and forecasting. The thesis has four distinct essays, each one analysing a current interest topic in this wide and vibrant area of economic research. But a common thread runs through all four: to determine whether it is possible to use the results of this research to develop trading strategies that can add persistent value to international investment portfolios with significant exposure to the foreign exchange market. This market has a daily turnover of $1.9 trillion (BIS, 2004) and is the most liquid financial exchange in the world, by some distance. Nonetheless, we argue that the market is also inefficient, in the sense that profitable trading opportunities persist and that prices do not reflect all available public information on a continuous basis. If we are correct-and we present simulation results that suggest we are-then the opportunity to derive and test plausible trading rules for the management of international investment portfolios though rigorous academic research is enormous. Yet all too often academic exchange rate research appears to be conducted in a cocoon, with the result that conclusions are sometimes difficult to apply in a practical context by portfolio managers. These difficulties reflect the computational requirements of implementing highly intensive trading strategies, associated trading costs and size limitations, and the practical limitations on implementation raised by publication lags and general data limitations. We aim to address these difficulties throughout this thesis. By assessing the merits of various theoretical models that collectively encompass all of the main themes on the current research agenda, we will be in a position to appreciate both the statistical and economic value of existing academic research, isolating areas of real merit for the investment community, and suggesting areas for further attention.
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Kim, Chung-Han. "Empirical studies of real exchange rates : heteroskedasticity, cross exchange rate correlation, forecasting /." Thesis, Connect to this title online; UW restricted, 1998. http://hdl.handle.net/1773/7396.
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Dror, Marika. "Forecasting of exchange rates." Doctoral thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-202335.
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The thesis investigates different exchange rate models and their forecasting performance. The work takes previous literature overview and summarize their findings. Despite the significant amount of papers which were done on the topic of exchange rate forecast, basically none of them cannot find an appropriate model which would outperform a forecast of a simple random walk in every horizon or for any currency pair. However, there are some positive findings in specific cases (e.g. for specific pair or for specific time horizon). The study provides up-to-date analysis of four exchange rates (USD/CZK, USD/ILS, USD/GBP and USD/EUR) for the period of time from January 2000 to August 2013 and analyse forecasting performance of seven exchange rate models (uncovered interest rate parity model, purchasing power parity model, monetary model, monetary model with error correction, Taylor rule model, hidden Markov model and ESTAR model). Although, the results are in advantage of Taylor rule model, especially for the exchange rate of USD/CZK, I cannot prove that the forecasting performance is significantly better than the random walk model. Except of the overall analysis, the work suppose instabilities in the time. Stock and Watson (2003) found that the forecast predictability is not stable over time. As a consequence, the econometric model can give us better forecast than random walk process at some period of time, however at other period, the forecasting ability can be worse than random walk. Based on Fluctuation test of Giacomini and Rossi (2010a) every model is analysed how the out-of-sample forecast ability changes over time.
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Yablonskyy, Karen. "Exchange Rate Predictions." Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-161875.
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The aim of this thesis is to analyze the foreign exchange currency forecasting techniques. Moreover the central idea behind the topic is to develop the strategy of forecasting by choosing indicators and techniques to make own forecast on currency pair EUR/USD. This thesis work is a mixture of theory and practice analyses. The goal during the work on this project was to study different types of forecasting techniques and make own forecast, practice forecasting and trading on Forex platform, based on acquired knowledge.
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Pathirana, Vindya Kumari. "Nearest Neighbor Foreign Exchange Rate Forecasting with Mahalanobis Distance." Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/5757.
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Foreign exchange (FX) rate forecasting has been a challenging area of study in the past. Various linear and nonlinear methods have been used to forecast FX rates. As the currency data are nonlinear and highly correlated, forecasting through nonlinear dynamical systems is becoming more relevant. The nearest neighbor (NN) algorithm is one of the most commonly used nonlinear pattern recognition and forecasting methods that outperforms the available linear forecasting methods for the high frequency foreign exchange data. The basic idea behind the NN is to capture the local behavior of the data by selecting the instances having similar dynamic behavior. The most relevant k number of histories to the present dynamical structure are the only past values used to predict the future. Due to this reason, NN algorithm is also known as the k-nearest neighbor algorithm (k-NN). Here k represents the number of chosen neighbors.
In the k-nearest neighbor forecasting procedure, similar instances are captured through a distance function. Since the forecasts completely depend on the chosen nearest neighbors, the distance plays a key role in the k-NN algorithm. By choosing an appropriate distance, we can improve the performance of the algorithm significantly. The most commonly used distance for k-NN forecasting in the past was the Euclidean distance. Due to possible correlation among vectors at different time frames, distances based on deterministic vectors, such as Euclidean, are not very appropriate when applying for foreign exchange data. Since Mahalanobis distance captures the correlations, we suggest using this distance in the selection of neighbors.
In the present study, we used five different foreign currencies, which are among the most traded currencies, to compare the performances of the k-NN algorithm with traditional Euclidean and Absolute distances to performances with the proposed Mahalanobis distance. The performances were compared in two ways: (i) forecast accuracy and (ii) transforming their forecasts in to a more effective technical trading rule. The results were obtained with real FX trading data, and the results showed that the method introduced in this work outperforms the other popular methods.
Furthermore, we conducted a thorough investigation of optimal parameter choice with different distance measures. We adopted the concept of distance based weighting to the NN and compared the performances with traditional unweighted NN algorithm based forecasting.
Time series forecasting methods, such as Auto regressive integrated moving average process (ARIMA), are widely used in many ares of time series as a forecasting technique. We compared the performances of proposed Mahalanobis distance based k-NN forecasting procedure with the traditional general ARIM- based forecasting algorithm. In this case the forecasts were also transformed into a technical trading strategy to create buy and sell signals. The two methods were evaluated for their forecasting accuracy and trading performances.
Multi-step ahead forecasting is an important aspect of time series forecasting. Even though many researchers claim that the k-Nearest Neighbor forecasting procedure outperforms the linear forecasting methods for financial time series data, and the available work in the literature supports this claim with one step ahead forecasting. One of our goals in this work was to improve FX trading with multi-step ahead forecasting. A popular multi-step ahead forecasting strategy was adopted in our work to obtain more than one day ahead forecasts. We performed a comparative study on the performance of single step ahead trading strategy and multi-step ahead trading strategy by using five foreign currency data with Mahalanobis distance based k-nearest neighbor algorithm.
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Crespo, Cuaresma Jesus, Ines Fortin, and Jaroslava Hlouskova. "Exchange rate forecasting and the performance of currency portfolios." Wiley, 2018. http://dx.doi.org/10.1002/for.2518.
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We examine the potential gains of using exchange rate forecast models and forecast combination methods in the management of currency portfolios for three exchange rates: the euro versus the US dollar, the British pound, and the Japanese yen. We use a battery of econometric specifications to evaluate whether optimal currency portfolios implied by trading strategies based on exchange rate forecasts outperform single currencies and the equally weighted portfolio. We assess the differences in profitability of optimal currency portfolios for different types of investor preferences, two trading strategies, mean squared error-based composite forecasts, and different forecast horizons. Our results indicate that there are clear benefits of integrating exchange rate forecasts from state-of-the-art econometric models in currency portfolios. These benefits vary across investor preferences and prediction horizons but are rather similar across trading strategies.
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Costantini, Mauro, Cuaresma Jesus Crespo, and Jaroslava Hlouskova. "Can Macroeconomists Get Rich Forecasting Exchange Rates?" WU Vienna University of Economics and Business, 2014. http://epub.wu.ac.at/4181/1/wp176.pdf.
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We provide a systematic comparison of the out-of-sample forecasts based on multivariate macroeconomic models and forecast combinations for the euro against the US dollar, the British pound, the Swiss franc and the Japanese yen. We use profit maximization measures based on directional accuracy and trading strategies in addition to standard loss minimization measures. When comparing predictive
accuracy and profit measures, data snooping bias free tests are used. The results indicate that forecast combinations help to improve over benchmark trading strategies for the exchange rate against the US dollar and the British pound, although the excess return per unit of deviation is limited. For the euro against the Swiss franc or the Japanese yen, no evidence of generalized improvement in profit measures over the benchmark is found. (authors' abstract)Series: Department of Economics Working Paper Series
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Antonakakis, Nikolaos, and Julia Darby. "Forecasting volatility in developing countries' nominal exchange returns." Routledge, 2013. http://dx.doi.org/10.1080/09603107.2013.844323.
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This article identifies the best models for forecasting the volatility of daily exchange returns of developing countries. An emerging consensus in the recent literature focusing on industrialized countries has noted the superior performance of the Fractionally Integrated Generalized Autoregressive Conditionally Heteroscedastic (FIGARCH) model in the case of industrialized countries, a result that is reaffirmed here. However, we show that when dealing with developing countries' data the IGARCH model results in substantial gains in terms of the in-sample results and out-of-sample forecasting performance. (authors' abstract)
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Yongtao, Yu. "Exchange rate forecasting model comparison: A case study in North Europe." Thesis, Uppsala universitet, Statistiska institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-154948.
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In the past, a lot of studies about the comparison of exchange rate forecasting models have been carried out. Most of these studies have a similar result which is the random walk model has the best forecasting performance. In this thesis, I want to find a model to beat the random walk model in forecasting the exchange rate. In my study, the vector autoregressive model (VAR), restricted vector autoregressive model (RVAR), vector error correction model (VEC), Bayesian vector autoregressive model are employed in the analysis. These multivariable time series models are compared with the random walk model by evaluating the forecasting accuracy of the exchange rate for three North European countries both in short-term and long-term. For short-term, it can be concluded that the random walk model has the best forecasting accuracy. However, for long-term, the random walk model is beaten. The equal accuracy test proves this phenomenon really exists.
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Sanabria, Montañez José Antonio. "A contribution to exchange rate forecasting based on machine learning techniques." Doctoral thesis, Universitat Ramon Llull, 2011. http://hdl.handle.net/10803/48492.
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El propòsit d'aquesta tesi és examinar les aportacions a l'estudi de la predicció de la taxa de canvi basada en l'ús de tècniques d'aprenentatge automàtic. Aquestes aportacions es veuen facilitades i millorades per l'ús de variables econòmiques, indicadors tècnics i variables de tipus ‘business and consumer survey’. Aquesta investigació s’organitza entorn d’una recopilació de quatre articles. L'objectiu de cadascun dels quatre treballs de recerca d'aquesta tesi és el de contribuir a l'avanç del coneixement sobre els efectes i mecanismes mitjançant els quals l'ús de variables econòmiques, indicadors tècnics, variables de tipus ‘business and consumer survey’, i la selecció dels paràmetres de models predictius són capaços de millorar les prediccions de la taxa de canvi.
Fent ús d'una tècnica de predicció no lineal, el primer article d'aquesta tesi es centra majoritàriament en l'impacte que tenen l'ús de variables econòmiques i la selecció dels paràmetres dels models en les prediccions de la taxa de canvi per a dos països. L'últim experiment d'aquest primer article fa ús de la taxa de canvi del període anterior i d'indicadors econòmics com a variables d'entrada en els models predictius.
El segon article d'aquesta tesi analitza com la combinació de mitjanes mòbils, variables de tipus ‘business and consumer survey’ i la selecció dels paràmetres dels models milloren les prediccions del canvi per a dos països. A diferència del primer article, aquest segon treball de recerca afegeix mitjanes mòbils i variables de tipus ‘business and consumer survey’ com a variables d'entrada en els models predictius, i descarta l'ús de variables econòmiques. Un dels objectius d'aquest segon article és determinar el possible impacte de les variables de tipus ‘business and consumer survey’ en les taxes de canvi.
El tercer article d'aquesta tesi té els mateixos objectius que el segon, però amb l'excepció que l'anàlisi abasta les taxes de canvi de set països. El quart article de la tesi compta amb els mateixos objectius que l'article anterior, però amb la diferència que fa ús d'un sol indicador tècnic.
En general, l'enfocament d'aquesta tesi pretén examinar diferents alternatives per a millorar les prediccions del tipus de canvi a través de l'ús de màquines de suport vectorial. Una combinació de variables i la selecció dels paràmetres dels models predictius ajudaran a aconseguir aquest propòsit.El propósito de esta tesis es examinar las aportaciones al estudio de la predicción de la tasa de cambio basada en el uso de técnicas de aprendizaje automático. Dichas aportaciones se ven facilitadas y mejoradas por el uso de variables económicas, indicadores técnicos y variables de tipo ‘business and consumer survey’. Esta investigación está organizada en un compendio de cuatro artículos. El objetivo de cada uno de los cuatro trabajos de investigación de esta tesis es el de contribuir al avance del conocimiento sobre los efectos y mecanismos mediante los cuales el uso de variables económicas, indicadores técnicos, variables de tipo ‘business and consumer survey’, y la selección de los parámetros de modelos predictivos son capaces de mejorar las predicciones de la tasa de cambio. Haciendo uso de una técnica de predicción no lineal, el primer artículo de esta tesis se centra mayoritariamente en el impacto que tienen el uso de variables económicas y la selección de los parámetros de los modelos en las predicciones de la tasa de cambio para dos países. El último experimento de este primer artículo hace uso de la tasa de cambio del periodo anterior y de indicadores económicos como variables de entrada en los modelos predictivos. El segundo artículo de esta tesis analiza cómo la combinación de medias móviles, variables de tipo ‘business and consumer survey’ y la selección de los parámetros de los modelos mejoran las predicciones del cambio para dos países. A diferencia del primer artículo, este segundo trabajo de investigación añade medias móviles y variables de tipo ‘business and consumer survey’ como variables de entrada en los modelos predictivos, y descarta el uso de variables económicas. Uno de los objetivos de este segundo artículo es determinar el posible impacto de las variables de tipo ‘business and consumer survey’ en las tasas de cambio. El tercer artículo de esta tesis tiene los mismos objetivos que el segundo, pero con la salvedad de que el análisis abarca las tasas de cambio de siete países. El cuarto artículo de esta tesis cuenta con los mismos objetivos que el artículo anterior, pero con la diferencia de que hace uso de un solo indicador técnico. En general, el enfoque de esta tesis pretende examinar diferentes alternativas para mejorar las predicciones del tipo de cambio a través del uso de máquinas de soporte vectorial. Una combinación de variables y la selección de los parámetros de los modelos predictivos ayudarán a conseguir este propósito.
The purpose of this thesis is to examine the contribution made by machine learning techniques on exchange rate forecasting. Such contributions are facilitated and enhanced by the use of fundamental economic variables, technical indicators and business and consumer survey variables as inputs in the forecasting models selected. This research has been organized in a compendium of four articles. The aim of each of these four articles is to contribute to advance our knowledge on the effects and means by which the use of fundamental economic variables, technical indicators, business and consumer surveys, and a model’s free-parameters selection is capable of improving exchange rate predictions. Through the use of a non-linear forecasting technique, one research paper examines the effect of fundamental economic variables and a model’s parameters selection on exchange rate forecasts, whereas the other three articles concentrate on the effect of technical indicators, a model’s parameters selection and business and consumer surveys variables on exchange rate forecasting. The first paper of this thesis has the objective of examining fundamental economic variables and a forecasting model’s parameters in an effort to understand the possible advantages or disadvantages these variables may bring to the exchange rate predictions in terms of forecasting performance and accuracy. The second paper of this thesis analyses how the combination of moving averages, business and consumer surveys and a forecasting model’s parameters improves exchange rate predictions. Compared to the first paper, this second paper adds moving averages and business and consumer surveys variables as inputs to the forecasting model, and disregards the use of fundamental economic variables. One of the goals of this paper is to determine the possible effects of business and consumer surveys on exchange rates. The third paper of this thesis has the same objectives as the second paper, but its analysis is expanded by taking into account the exchange rates of 7 countries. The fourth paper in this thesis takes a similar approach as the second and third papers, but makes use of a single technical indicator. In general, this thesis focuses on the improvement of exchange rate predictions through the use of support vector machines. A combination of variables and a model’s parameters selection enhances the way to achieve this purpose.
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De, Boyrie Maria Eugenia. "Out-of-sample exchange rate forecasting structural and non-structural nonlinear approaches." FIU Digital Commons, 1994. http://digitalcommons.fiu.edu/etd/2727.
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Forecasting foreign exchange rates is a perennial dilemma for exporters, importers, foreign exchange rate traders, and the business community as a whole. Foreign exchange rate models using popular linear and non-linear specifications do not produce particularly accurate forecasts. In point of fact, these models have not improved much upon the random walk model, especially in out-of-sample forecasting. Given these results, this dissertation constructs and evaluates new forecasting models to generate as accurate as possible out-of-sample forecasts of foreign exchange rates.
The information content of futures contracts on foreign exchange rates is investigated and used to forecast future exchange rates using alternative techniques, both structural (econometric) and non-structural (fuzzy) models. The results of two specifications of a structural model are compared against the well-known random walk model. The first specification assumes future exchange rates are determined by futures prices and a lagged structure of spot rates. The second specification assumes that future spot rates are a function of only a lagged structure of the futures prices.
The forecasting accuracy of the models is tested for both in-sample and out-of-sample periods; out-of-sample tests range from the short term to the long term (30- to 180-day forecasts). The results indicate that the random walk model remains a competitive alternative. In out-of-sample predictions, however, we can improve upon it in certain cases. The results also show that the predictive accuracy of the models is better in the short term (30 to 60 days) than in the longer term (180 days).
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Vasiljeva, Polina. "Combining Unsupervised and Supervised Statistical Learning Methods for Currency Exchange Rate Forecasting." Thesis, KTH, Matematisk statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-190984.
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In this thesis we revisit the challenging problem of forecasting currency exchange rate. We combine machine learning methods such as agglomerative hierarchical clustering and random forest to construct a two-step approach for predicting movements in currency exchange prices of the Swedish krona and the US dollar. We use a data set with over 200 predictors comprised of different financial and macro-economic time series and their transformations. We perform forecasting for one week ahead with different parameterizations and find a hit rate of on average 53%, with some of the parameterizations yielding hit rates as high as 60%. However, there is no clear indicator that there exists a combination of the methods and parameters that outperforms all of the tested cases. In addition, our results indicate that the two-step approach is sensitive to changes in the training set. This work has been conducted at the Third Swedish National Pension Fund (AP3) and KTH Royal Institute of Technology.I denna uppsats analyserar vi det svårlösta problemet med att prognostisera utvecklingen för en valutakurs. Vi kombinerar maskininlärningsmetoder såsom agglomerativ hierarkisk klustring och Random Forest för att konstruera en modell i två steg med syfte att förutsäga utvecklingen av valutakursen mellan den svenska kronan och den amerikanska dollarn. Vi använder över 200 prediktorer bestående av olika finansiella och makroekonomiska tidsserier samt deras transformationer och utför prognoser för en vecka framåt med olika modellparametriseringar. En träffsäkerhet på i genomsnitt 53% erhålls, med några fall där en träffsäkerhet så hög som 60% kunde observeras. Det finns emellertid ingen tydlig indikation på att det existerar en kombination av de analyserade metoderna eller parametriseringarna som är optimal inom samtliga av de testade fallen. Vidare konstaterar vi att metoden är känslig för förändringar i underliggande träningsdata. Detta arbete har utförts på Tredje AP-fonden (AP3) och Kungliga Tekniska Högskolan (KTH).
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Aljandali, Abdulkader. "Exchange rate forecasting : regional applications to ASEAN, CACM, MERCOSUR and SADC countries." Thesis, London Metropolitan University, 2014. http://repository.londonmet.ac.uk/675/.
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This thesis contributes to knowledge concerning the volatility and forecasting of exchange rates in the emerging world. It investigates the exchange rates of the leading trading blocs in that part of the world. This thesis examines exchange rates of selected emerging countries across continents and fills gaps in the literature pertaining to local and regional analyses of exchange rates, with an investigation of the determinants of their fluctuations in selected common markets in Africa, Asia, Central and Latin America. Exchange rates of countries from the four different regions are investigated separately, followed by an analysis within and across regions to identify common patterns of exchange rates fluctuations. Monthly forecasts are generated for a period of 24 months to test the performance of the times series, cointegration and combination techniques used in this thesis. The results show that exchange rates of countries in the same region behave similarly following a shock to the system. Additionally, exchange rates of countries at the same stage of development albeit in different geographical location (Central America, Southern Africa, Latin America and Southeast Asia) share some similarities. This thesis found that all exchange rates examined have been volatile. Furthermore, asymmetric volatility was particularly relevant in the modelling process mainly for countries that suffered from the aftermath of a financial or debt crisis, especially in Asia and Latin America. Exponential smoothing time series models provided the most accurate forecasts for the sampled exchange rates, while combination models outperformed single time series models in about 70% of the cases. ARDL cointegration models had limited success in the forecasting exercise but were particularly relevant as a composite method and were the best performing models when combined with time series techniques.
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Jiang, Ying. "Essays on forecasting exchange rate volatility, central bank interventions and purchasing power parity." Thesis, University of Essex, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.496272.
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Su, Xiaojing. "Essays on financial and international economics." Thesis, [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-1474.
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Ziegler, Christina. "Exchange Rate Stability and Wage Determination in Central and Eastern Europe." Doctoral thesis, Universitätsbibliothek Leipzig, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-81237.
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In Folge der Osterweiterung der europäischen Union (EU) und der steigenden Arbeitsmarktintegration zwischen den EU15 und den neuen Mitgliedsstaaten ist die Lohnfindung in Mittel- und Osteuropa zu einem Schwerpunkt der europäischer Wirtschaftspolitik geworden. Zugleich wird das optimale Wechselkursregime für mittel- und osteuropäische Staaten kontrovers diskutiert. Die Dissertation befasst sich mit der Fragestellung, welche Wechselkursstrategie in Mittel- und Osteuropa vorzuziehen ist, um zum einen den Lohnfindungsprozess zu optimieren und zum anderen den Anpassungsprozess (Konvergenzprozess) an europäische Lohnstandards zu beschleunigen. Diese kumulierte Arbeit besteht aus vier unabhängigen Fachaufsätzen. Zuerst wird der Frage nachgegangen, welche Wechselkursstrategie einen optimalen Rahmen für die Lohnsetzung während des Aufholprozesses mittel- und osteuropäischer Staaten ermöglicht (Kapitel zwei). Im Kapitel drei wird die Rolle der Geldpolitik in Bezug auf die Lohnfindung in Staaten mit flexiblen Wechselkursen untersucht. Die Evaluierung der Prognosefähigkeit alternativer Konjunkturindikatoren für die Euro Zone sowie deren Implikationen für den Lohnverhandlungsprozess in Mittel-und Osteuropa ist Gegenstand der Analyse in Kapitel vier. Im fünften Kapitel wird der Rolle der Lohnpolitik auf Leistungsbilanz(un)gleichgewichte in Mittel- und Osteuropa nachgegangenAfter the Eastern enlargement of the European Union (EU) and increasing participation of labor between the EU15 and the new member states, wage determination in Central and Eastern Europe (CEE) has become a key issue in European economic policy making. At the same time there are controversial discussions regarding the appropriate exchange rate regime for the CEE countries. In this thesis it is examined which exchange rate strategy provides a more favorable framework for wage setting in CEE and leads to faster wage convergence in Europe. This thesis has four parts. First, it is analyzed which exchange rate strategy provides a more favorable framework for wage setting during the economic catch-up process of CEE (section two). Second, the role of monetary policy in wage determination in countries with flexible exchange rate regimes is examined in section three. Third, the predictive power of different euro area business cycle indicators is analyzed in section four. Fourth, the impact of wage determination on the balance of payments in CEE is scrutinized (section five)
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Johansson, Sam, and Shayan Nafar. "Effective Sampling and Windowingfor an Artificial Neural Network Model Used in Currency Exchange Rate Forecasting." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210858.
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Financial forecasting is a field of great interest in academia and economy. The subfield of exchangerate prediction is of considerable value to practically every entity operating within the financialmarket. Ranging from private hedgers, speculators or arbitrageurs to entire financial institutions suchas international banks or insurance companies, the ability to predict exchange rate movementsprovides major benefits for organizations in contact with these. A great multitude of research has beenconducted to construct methods to aid firms and investors to better anticipate on potentialdevelopments in the foreign exchange market. Much of the research has been focusing on a promisingprediction model within computational intelligence developed in recent decades, namely the ArtificialNeural Network (ANN). However, a review of existing literature suggests that the time step, predictionhorizon and window size have not been of central essence. Hence, this paper attempts to provide amore formal analysis of the actual impact of the three mentioned parameters on the prediction resultsof ANNs. Through literature studies, modeling and experimentation it is found that no specificcombination of time step, prediction horizon and window size results in more exact forecasts, but thatcertain combinations of the three parameters generally result in superior performance.
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Costantini, Mauro, Cuaresma Jesus Crespo, and Jaroslava Hlouskova. "Forecasting errors, directional accuracy and profitability of currency trading: The case of EUR/USD exchange rate." Wiley, 2016. http://dx.doi.org/10.1002/for.2398.
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We provide a comprehensive study of out-of-sample forecasts for the EUR/USD exchange rate based on multivariate macroeconomic models and forecast combinations. We use profit maximization measures based on directional accuracy and trading strategies in addition to standard loss minimization measures. When comparing predictive accuracy and profit measures, data snooping bias free tests are used. The results indicate that forecast combinations, in particular those based on principal components of forecasts, help to improve over benchmark trading strategies, although the excess return per unit of deviation is limited.
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Ripkauskas, Rolandas. "Užsienio valiutų kurso prognozės programėlė mobiliems Android OS įrenginiams." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2013. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2013~D_20130617_165455-99852.
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Magistro darbo tikslas yra ištirti prognozės modelius, leidžiančius prognozuoti valiutos kurso vertę į ateitį bei ištirti gautų rezultatų atitikimą realiai rinkos situacijai. Ištyrus prognozės metodus ir atradus patikimą algoritmą - jį užrašyti Java kalba ir pritaikyti Android OS valiutos kurso prognozei. Taip pat įgyvendinti programėlės funkcijas, kurios vartotojui leis pilnai atlikti norimas operacijas: konvertuoti valiutas viena kitos atžvilgiu, stebėti rinkoje pokytį, peržiūrėti istorinius valiutos duomenis, stebėti rinkos situaciją, kurti savo valiutos sąrašą. Rezultatai: ištirtas ir atrinktas prognozės algoritmas, pritaikytas Android OS programėlėje penkių dienų valiutos kursų prognozei. Sukurtos papildomos programėlės funkcijos panaudojant Android OS teikiamas sistemines galimybes. Suderinta vartotojo sąsaja su skirtingais įrenginiais egzistuojančiais rinkoje.The research objective is to investigate the models for currency exchange rates forecast and examine the compliance of the observed forecast results with the real market situation. The study of prediction methods and the discovery of a reliable algorithm, are programmed in Java and Android OS to allow currency exchange rate forecasts on demand. Once forecasting model is developed, additional functionalities for Android OS device are created allowing the user to fully perform such operations as: to convert one currency to the other, monitor the change in the market, view historical currency data, to monitor the market situation and customize favorite currency list. Results: investigated and selected forecasting algorithm which was applied to Android OS mobile with a five-day forecast of exchange rates duration. Created additional app capabilities using Android system’s resources and functions. Designed user interface to work with multiple Android devices existing on the market today.
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Sun, Wenyi. "Exchange rate forecasting : do linear combinations of exchange rate forecasts outperform?" Thesis, 2005. http://spectrum.library.concordia.ca/8848/1/MR14378.pdf.
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In recent years, a limited amount of work has been done on the medium-term linear composite method of forecasting. One common finding in the existing literature is that the consensus forecast measure is a biased predictor of future exchange rates. A widely accepted point of view in exchange rate forecasting research is that no theoretical model should be able to outperform a simple random walk. In this paper, recent exchange rate data and the Granger-Ramanathan linear estimation method are used to test medium-term forecasts. The currencies considered in this study are the most actively traded in the world and include: euros, Japanese yen, Canadian dollars, British pounds and Swiss francs. All currencies are examined relative to the US dollar. The major finding is that the linear composite model does in fact outperform a random walk model and an average forecast for Japanese yen, British pounds and Swiss francs. This evidence suggests that additional research should be conducted on exchange rate forecasting in general and on the linear composite forecast model in particular.
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Tsai, Huo-lien, and 蔡火蓮. "Forecasting Exchange Rate , New Taiwan Dollar." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/83099867420793947016.
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Moldovan, Paula Cristina Ciurean. "Forecasting the euro dollar exchange rate." Master's thesis, 2015. http://hdl.handle.net/10071/11649.
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O objectivo principal desta tese é obter valores futuros fidedignos da taxa de câmbio mensal entre o Euro e o Dolar Americano. Para obter isto utilizamos modelos econometricos lineares e não-lineares, nomeadamente, ARMA (Auto Regressive Moving Average) e STAR (Smooth Transition Auto Regression). Obtemos que para curto e médio prazo os modelos lineares tem uma performance melhor do que os modelos não-lineares. A qualidade de forecast foi avaliada pelo valor do erro quadrático médio (RMSE).O objectivo principal desta tese é obter valores futuros fidedignos da taxa de câmbio mensal entre o Euro e o Dolar Americano. Para obter isto utilizamos modelos econometricos lineares e não-lineares, nomeadamente, ARMA (Auto Regressive Moving Average) e STAR (Smooth Transition Auto Regression). Obtemos que para curto e médio prazo os modelos lineares tem uma performance melhor do que os modelos não-lineares. A qualidade de forecast foi avaliada pelo valor do erro quadrático médio (RMSE).
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HO, HUI-HUI, and 何慧慧. "The Forecasting Model of Euro Exchange Rate." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/35904715980854485286.
Full textAbstract:
碩士國立臺北大學
企業管理學系
89
ABSTRACT Euro had a debut on January 4th in 1999 and the object money will be current on the market on January 1st in 2002. Since Europe keeps a good economic and political status on the international stage, whether Euro is stable and will be strong currency has been open to a hot issue. With the international trade prevails, the influence of exchange rate increases to both enterprises and individuals. Taiwan is a small island and used to rely on the trades with foreign countries. The emergence of Euro will certainly change the content of exchange position in Taiwan. As a result, the historical data from January 1st 1999 to March 31st 2000 was examined and the forecasting model was brought up. The data was divided into two parts, which were from January 1st 1999 to December 31st 2000 and from January 1st 2001 to March 31st 2001. The former was used to form a suitable model and the latter was out-samples, which was to be forecasted. The average model was modified and employed to do the job. To be compared, the data included Pound and Yen. The findings are as follow: 1.The outcome of R/S method indicated the three exchange rate series are persistent ones. 2.The original average model could not beat random model. 3.The modified average model had better forecasting effect than original one. 4.The calculated H value had no positive relationship with forecasting effect of the average model.
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KE, LI-JUNG, and 柯俐榕. "A Study of Exchange Rate Forecasting Model." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/83952404545792613015.
Full textAbstract:
碩士國立臺北大學
合作經濟學系
95
The purpose of this paper is to utilize special form of econometric model as an exchange rate forecaster. We Use the monthly exchange rate between US dollar and Taiwanese NT dollar as our primary variable in the research. The sample period extends from January 1996 to December 2004. We adopt Ordinary Least Square (OLS) method to build a multiple regression model and add GARCH model to observe which of these models will perform better on the exchange rate forecasting ability. By comparing the out-of—sample forecasts to detect the advantage or disadvantage of using different forecasting models. This paper has employed MSE, RMSE, U, UB, UR and UD as the criteria for evaluating principles. We can find the results that when we consider Taiwan and US interacting value of monetary price, consumer price and interest rate separately they can be useful indicators while we forecast the trend of dollar. But in the model with these three variables, only the interacting value of interest rate doesn’t influence exchange rate significantly. The forecasting ability of these models to US dollar and Taiwanese NT dollar had led us to conclude that following principles are employed (MSE, RMSE, UB, UR and UD), the second model including the interacting value of monetary price possesses the better forecasting ability. But according to Taylor U principle, the first model which includes three variables possesses the better forecasting ability.
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邱靖惠. "A study of exchange rate forecasting model." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/dy24m3.
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lin, Jun-hun, and 林俊宏. "Forecasting Foreign Exchange Rate-Comparisons Among Dfferent Models." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/47740859872532185531.
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Jou, Iu-ru, and 周育如. "Exchange Rate Forecasting Using Weighted Fuzzy Time Series." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/94jbeb.
Full textAbstract:
碩士國立臺灣科技大學
資訊管理系
95
Due to the liberalization of the financial market and the diminishment of the government’s intervention on the foreign exchange market, we have witnessed severe fluctuations of the exchange rates of the NT Dollars against different foreign currencies. Since the exchange rates of the NT Dollars against other foreign currencies have significant effects on the international trade of Taiwan, how to forecast the exchange rate variations becomes an important issue for Taiwan. If the government, an enterprise or an individual can accurately predict the exchange rate variations, then the capital loss due the exchange rate variations can be reduced. Recently, many researchers have proposed to use the fuzzy time series to model and predict many real life time series applications, such as predicting university enrollments or daily temperatures. In this thesis, we propose a weighted fuzzy time series (abbreviated as WFTS) to predict the exchange rate of the NT Dollars against the US Dollars. We consider two factors in the proposed method. The first factor is the historical exchange rates of the NT Dollars against the US Dollars. The second factor is derived, through the Principal Components Analysis (PCA), of several variables affecting the exchange rates including the exchange rates of the trading competition countries and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). In the proposed method, we adjust the relative weight between the first factor and the second factor to find the better predicting rules to predict the future exchange rates. The experiment shows that the proposed weighted fuzzy time series model has a better forecasting accuracy rate compared to the random walk model and the FLAR model. Furthermore, the proposed method shows better directional symmetry than the random walk model and the FLAR model for predicting long term exchange rates.
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Liu, Hsiao-Chi, and 劉曉齊. "Market Fundamentals, Factor Model, and Exchange Rate Forecasting." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/10832810469462162049.
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碩士國立中央大學
經濟學研究所
98
In this study, we combined factor models with traditional fundamental models (including purchasing power parity model, Taylor rule model, monetary model, uncovered interest parity model), using out of sample forecast and bootstrap test to find that RMSPE of exchange rate forecast for factors combined with fundamental models lower than traditional fundamental models. In particular, factors combined with purchasing power parity model outperform other factors combined with fundamental models. Moreover, this study examined five horizons (h = 1, 4, 6, 8, 12). Out of short horizon (h = 1), there are Canada, Denmark, Euro, Norway, New Zealand, South Korea, Singapore,and United Kingdom ever have significant forecast from our object countries. Finally, if we focused on middle horizon (h = 4, 6), out of factors combined with monetary model, there are more countries have evidence to support that forecast of exchange rate for factors combined with fundamental models outperform random walk model.
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Kou, Hsiao-Fen, and 郭孝芬. "A Study of the Exchange Rate Forecasting Model." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/98544423777583747824.
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碩士淡江大學
財務金融學系碩士在職專班
92
The two exchange rate determination models from Monetarists adopted in this study are the flexible-price monetary model and the sticky-price monetary model. In regards to the forecasting methods, OLS、AR1、GARCH and Kalman filter are used for parameterized estimation inside samples and forecasting value outside samples of exchange rate forecasting models. This study calculates Theil’s U as forecasting performance indicator for measuring forecasting performance outside samples of the econometric model. The empirical evidence shows that the exchange rate of NTD in the previous period dominates the development of the exchange rate in Taiwan. As to the comparison during different periods among models, the random walk model has a relatively low forecasting error in the short term while the OLS model delivers a relatively minor degree of forecasting error in the long term.
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蔡鍾屏. "A comparison of alternative exchange rate forecasting models." Thesis, 1990. http://ndltd.ncl.edu.tw/handle/69337798592997855855.
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Chang, Pei-wei, and 張培瑋. "Forecasting the Exchange Rate by Rough Set Theory." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/40139761921220624958.
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碩士朝陽科技大學
財務金融系碩士班
98
Because of the global liberalization of international trade, fund between nations flows more frequently day by day. Besides, after the collapse of Bretton Woods Agreement, many countries had given up the fixed exchange rate system and used the floating exchange rate system instead. Under the floating exchange rate system, the prediction of actual exchange rate becomes uncertainty. Therefore, if one could fully understand and control the factors that caused the exchange rate movements, we would able to reduce the losses caused by exchange rate fluctuations and risks, and provide some relevant information for government agencies, banks, international companies and investors in the exchange rate of reference. In this study, we are engaged in predicting monthly exchange rate of New Taiwan dollar to U.S. dollar, Japanese Yen, Hong Kong dollars, Canadian dollars, Korean won and Thai baht based on Rough Set Theory combined with Exhaustive Algorithm and Genetic Algorithm, building the decision rules and adding shortening ratio to investigate the prediction accuracy, and finally we also compare the prediction performance with Local Transfer Function Classifier. According to the empirical results, the prediction performance of Exhaustive Algorithm and Genetic Algorithm outperform Local Transfer Function Classifier. In the Korean won, Japanese yen and Thai baht, the forecasting accuracy under Exhaustive Algorithm and Genetic Algorithm has better performance. In addition, the performance of Genetic Algorithms under the shortening ratio of 0.9 has the highest prediction accuracy among all countries; while this phenomenon doesn’t exist if we use by Exhaustive Algorithm.
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Yang, Tze-Chen, and 楊慈珍. "Volatility Forecasting of USD/NTD Exchange Rate and Its Relationship with Forward Exchange Rate: Effects of Forecasting Performance and Trading Volume." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/65789549836510718269.
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碩士國立臺灣海洋大學
應用經濟研究所
94
Abstract The purpose of this study is to establish the relationships between the spot and forward exchange rate and to forecast the volatility of both USD/NTD exchange rates. This study applies the following six single variate models, such as stochastic volatility model, GARCH model, GARCH-M model, EGARCH model, TGARCH model and GJR-GARCH model, to forecast the volatility of the return rate for both spot and forward exchange rate. Comparing the forecasting performance of the above six models, the VEC-TGARCH model is chosen to specify the bi-variates relationship between the spot and forward exchange markets. The sample period is from January 2, 2001 to November 30, 2005. Major conclusions of this study are shown as follows. First, the result of the unit root test shows that both of the USD/NTD spot exchange rate and the USD/NTD forward exchange rate are non-stationary series and are integrated order one. Second, by using Johansen co-integration test, there is a single co-integration relationship between the spot and forward exchange markets. Third, there exists the volatility clustering phenomenon and an asymmetric effect in the spot and forward exchange markets. Fourth, after taking the return of the forward exchange rate and the trading volume into account, the volatility clustering effect will be reduced and the forecasting of the volatility will perform better. Fifth, there exists reciprocal cause and effect relationship between spot and forward exchange markets and the reaction of the forward exchange market to any new intervention is larger than that of the spot exchange market. Sixth, by comparing the forecasting performance of the volatility from the above six models metioned and the VEC-TGARCH model, the stochastic volatility model ranked the best, the VEC-TGARCH model ranked the second, and the TGARCH model ranked the third.
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Kao, Chi-Wen, and 高啟文. "The Analysis on Forecasting of Foreign Exchange Rate Model." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/79458922635524175250.
Full textAbstract:
碩士淡江大學
財務金融學系
89
Title of Thesis：The Analysis on Forecasting of Foreign Exchange Rate Model Total Pages：72 Name of Institute：The Graduate Institute of Money Banking and Finance Tamkang University Graduate Date：June,2001 Name of Student：Chi-Wen, Kao Advisor：Ming-Zhi, Li Abstract：: With the steps of liberalization and internationalization developing in Taiwan, the research on exchange rate reflects the time series model traditionally through integrated, autoregressive and moving average parameter. Therefore, Engle(1982) proposed ARCH model, moreover, Bolleralev(1986) proposed further GARCH model to solute the problem that expected returns on the financial assets or their random error degree will be changed in accordance with the timing or investment horizon. Additionally, Granger(1980)、Granger and Joyeux(1980) and Hosking(1981) proposed fractionally differenced model to modify the bias of dichotomy and found the character presented in the processes of fractionally differenced time series. Fractionally differenced model can be considered as the generalization of the ARIMA model. The model can not only describe the processes of ARIMA but also can display the processes of time series with long memory. Long memory means that the time series impacted by past or external influence is relatively far from the series of I(0) and like I(0) it owns the charateristic of mean-reverting. However, the interdependent between serial observations is showing a slower decay and persistence with longer and longer time horizon. As for external impact, the serial of I(0) presents a faster geometric decay compared with the serial with long memory which presents a slower hyperbolic decay. The purpose of this paper is to study the characters and practice for the model of ARIMA, ARIMA-GARCH and ARFIMA by using the variables of relative foreign exchange movement among the currencies of Japan, Hongkong, Korea, Taiwan and Singapore (four little Asia Dragons) to US dollar. Moreover, outside of the model sample we adopt a mode of time rolling to predict the trend of foreign exchange. According to the final results, we can choose the best foreign exchange prediction model. The result shows that ARFIMA (1,0.133,1) model can be explained well for the foreign exchange rate movement of NT dollar to US dollar and the best result is deduced from the time rolling prediction of one forward period. The best model to explain the exchange rate movement of Japanese Yen to US dollar is ARIMA (1,0,0), and the best result of time rolling prediction is to move forward for four periods. As for the movement of exchange rate for Hong Kong dollar to US dollar, the model of ARIMA(2,0,1)-GARCH(1,1) shows there is no apparent difference among the time rolling predictions moving forward. The both results of exchange rate movements for Singapore dollar to US dollar and Korea won to US dollar are ARFIMA(2,0.138,0) and ARFIMA(2,-0.396,0) models; and both of them have the best time rolling prediction results running by
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LI, JHIH-CHENG, and 黎致呈. "Dynamic Intraday Exchange Rate Forecasting using Machine Learning Methods." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/9d3wcx.
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碩士輔仁大學
統計資訊學系應用統計碩士班
105
This study applies Random Forest model to forecasting the exchange rate of USD、JPY、EUR、CNY. This study uses spot buying rate of mean, standard deviation, maximum, minimum, starting value, and end value every 30 minutes as the research variables.The empirical interval is from May 12, 2016 to September 25, 2016. The neural network and support vector regression are used as the benchmarks. The empirical results show that the use of the intraday data as the training sample can reduce the prediction error, and the random forest is better than the neural network and the support vector regression.
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Lin, Yuan-Hsin, and 林源馨. "A Heuristic Investigation of NTD/USD Exchange Rate Forecasting." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/42034239324751941244.
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碩士中原大學
國際貿易研究所
96
In traditional economic theory, exchange-rate forecasting models focus on long-term prediction, and use macroeconomic data as the source of information. Additionally, compared to the random-walk model, these models generally do not perform well in terms of its performance on out-sample prediction (Meese and Rogoff, 1983). In light of the above issue and the need of short-term prediction in practice, we alternatively develop a dynamic multivariate regression model of the spot NTD-USD exchange rates, based on the large combinations of one to thirty lag periods. Our model also includes three exogenous variables based on the following consecutive daily price data in recent months: (1) gold futures price, (2) light sweet crude oil price (NYMEX), (3) Taiwan index futures prices. The observation period spans from January 2002 to December 2006. We examine numerous models that use different combinations of exogenous variables, and select the best model according to the significance of its parameters and goodness-of-fit. Finally we compare the selected model with the random-walk model according to their performance on out-sample prediction. The empirical result shows that our model outperforms the random-walk model in terms of Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). The selected best model also indicates a tractable relationship between the NT-USD exchange rate and the variables except light sweet crude oil price and Taiwan index futures prices. This model also shows that the NT-USD exchange rate correlates to its lag exchange rates, and has a negative correlation with the lagged gold price.
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Rong, Fu Li, and 傅麗容. "Forecasting exchange rate by the method of decision tree." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/64746816116041977395.
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Chung-HanChen and 陳琮翰. "Forecasting Exchange Rate with Text Mining and Financial Indicators." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/6gwp53.
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碩士國立成功大學
資訊管理研究所
105
Because of the geographical environment in Taiwan, many resources rely on international trade. Trade profits are measured by the price of imports and exports, affected by exchange rate. Volatility of the exchange rate is the key to the amount of profit, but fluctuations of exchange rate are often affected by many factors, which can be known by the news. Most research done to date has used linear regression or Rule-Based method to forecast exchange rate, but some of studies show that the impact of news on exchange rate is significant in the short term. In order to forecast exchange rate in a short time, we build a forecasting model with text mining and try to find out financial indicators, including the Chinese and English news, by two features selection methods. According to the selected features, Pointwise Mutual Information (PMI) and sentiment analysis are used to evaluate feature word scores. Finally Support Vector Regression is adopted to build the forecasting model. The result of experiment can find the impact of Chinese news on the exchange rate compared to the English news is relatively lower. The impact of holiday news on the exchange rate is the same as the weekday news. The financial indicators has lowest impact on prediction is the worst. We also find using English news on the accuracy prediction is better than using Chinese news.
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Shih-Chieh, Lu, and 呂世傑. "Forecasting Foreign Exchange Rate with Chaos Theory :A Study of Multidimensional Vector Forecasting." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/66619901639511297269.
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碩士中原大學
企業管理研究所
94
Accurate predicting foreign exchange movement is an important topic for all Far Eastern countries, especially for an international trading base economy of Taiwan. During the era of the 21st century, the foreign exchange movement in the free economic system can not be fully governed or controlled by any single financial institute or government. Therefore, the trend and movement of foreign exchange are extremely difficult to forecast accurately. Owing to the fact that the forecasting performance on exchange prediction of traditional linear theory had not been performed well in this 21st century, accurate prediction of foreign exchange rate via non-linear models has been examined by governments, academics and practitioner. The famous is and most popular nonlinear models are option pricing model and various neural network models. The motivation of this study is to explore a new method of accurate prediction of the movement of foreign exchange rate accurately. Consequently, this investigation focuses on the movement of Taiwanese dollar vs. US dollar’s forecasting performance based on Chaos theory. This work uses three statistical methods to investigate whether the exchange rate’s movement fits the chaotic phenomenon. The findings indicate that there is indeed a chaotic phenomenon occurred within the foreign exchange rate data. Therefore, forecasting foreign exchange rate based on the Chaos theory has been applied. This study analyzes data using multidimensional vector analysis and predicts foreign exchange rate via Phase Space Neighbor Number Method. The empirical result shows that the Phase Space Neighbor Number Method performs well for short-term forecasting, yet it doesn’t work well for long-run prediction. Due to the sensitivity to the initial conditions of Chaos theory, the result demonstrates that this work confirms the “butterfly effect”. Finally, this study suggests that the government, the international enterprises and the investors to apply the Chaos theory to forecast the foreign exchange rate in order to reduce the transaction risk.
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Retief, Stefan Johan. "Comparing linear and non-linear benchmarks of exchange rate forecasting." Thesis, 2014. http://hdl.handle.net/10210/11129.
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M.Com. (Financial Economics)Exchange rate forecasting has been an important and complex field of study originating mainly from the introduction of floating exchange rates in the 1970s. Since then, various models have been developed to explain exchange rate behaviour, all contributing in their own way to the understanding of what economic and financial information reveal about the future price of exchange rates. To measure the performance of a variety of exchange rate models, researchers in exchange rate forecasting almost always use the random walk model as benchmark to evaluate the forecasting performance of exchange rate models. An exchange rate model is regarded as superior if it can outperform a random process. The random walk model, a special case of the unit root process, helps us to identify the kinds of disturbances that drive the exchange rate to follow an independent successive process. If the exchange rate follows a random walk process, it has no mean reversion tendency and a directional shock in the exchange rate will cause it to deviate from its long-run equilibrium. Conversely, if the exchange rate does not follow a random walk, it has mean reverting tendencies, and will follow a stationary process which allows us to accurately forecast the exchange rate based on historic observations (Lam, Wong and Wong, 2005:1). However, it seems unrealistic that exchange rates will follow either a random walk process or a stationary process. If we assume that the exchange rate follows a random walk, we also assume that the order flow information from exchange rate trades follows a random walk, and by implication that macroeconomic exchange rate information follows a random walk [see Lyons (2001) for the link between order flow and macroeconomic fundamentals]. It seems unrealistic that exchange rates will follow an identifiable mean reverting (stationary) process, as daily exchange rates are exposed to risk, news and speculation which functions independent from long-run exchange rate fundamentals. Ironically, Meese and Rogoff (who laid the foundation for the use of random walk models as benchmark in exchange rate forecasting) emphasize that exchange rates do not follow an exact random walk (Meese and Rogoff, 1983:14). However, if it is known that exchange rates do not follow a random process explicitly, alternative exchange rate benchmark models should be considered. Yet, judging by the universal...
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Wang, Shu-wen, and 王淑雯. "NTD-USD Exchange Rate Forecasting Models and Their Investment Performances." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/daktnt.
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碩士世新大學
財務金融學研究所(含碩專班)
103
The main objective of this paper is to build NTD-USD Exchange Rate Forecasting Models and evaluate their Investment performance. The research objects are NTD-USD Exchange Rate, 3-Month U.S. Treasury bill rate, U.S. Consumer Price Index, Taiwan M1B balance, Taiwan foreign exchange reserves, Taiwan Wholesale Price Index and Taiwan Capitalization Weighted Stock Index. The period is from July 1997 to March 2015 by using monthly data. This study applied Augmented Dickey-Fuller Test, Multiple Regression Analysis, Chow Test and Moving Windows Method. Finally identify two better subsamples of investment models and test their investment performance.
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AI, SUN, and 孫艾. "Research on Exchange Rate Forecasting Based on Information System Algorithm." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/65v4u5.
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博士國立高雄應用科技大學
國際企業研究所
105
Abstract Along with the rapid development of financial globalization, our country faces complicated financial risks and foreign exchange risks. The subprime mortgage crisis, the sovereign debt crisis in areas with the euro, etc., spurred a global financial crisis and an economic recession and hence caused exchange rate prediction to evolve into an important economic issue, drawing wide attention. However, the foreign exchange market is a non-linear system with multiple variables, in which correlations between all factors are perplexing, exacerbating the difficulty of exchange rate prediction. As a complex non-linear system, exchange rate prediction methods have developed into a time series prediction from a parametric regression. However, in real applications, exchange rate fluctuations and varying trends are very complex, and the execution speed of the algorithm must surpass the variation speed of exchange rate at the same time as the exchange rate is precisely predicted. Although numerous studies pertaining to exchange rate prediction methods are currently available, the majority of the algorithms have been constrained by their complexity, and relevant research analysis has not been conducted on the applicability to data sets of the algorithms commonly used in exchange rate prediction. On account of this, three major method types are selected in this dissertation as the methodological basis of the research: the algorithm based on the empirical risk minimization principle, the algorithm based on the structural risk minimization principle, and the statistical filtering algorithm. Methods representative of algorithms theoretically applicable to exchange rate prediction are separated from the three major methods, namely, the Radial Basis Function Neural Network (RBFNN), the Least Squares-Support Vector Machine (LS-SVM), and the Kalman Filter (KF). The three methods mentioned above are selected in this dissertation to represent the three major methods, and explore their precision, efficiency, and applicability concerning exchange rate prediction. In addition, we contrast the three major types of algorithms according to test results, analyze the applicability of the different algorithms to data sets, and offer a novel train of thought and technological research on solving the problem of exchange rate prediction. The main sections of the dissertation are as follows: 1. The widely-used type of neural network, RBFNN, is introduced into the field of exchange rate prediction based upon the empirical risk minimization principle. This method both inherits the empirical risk minimization principle and introduces the kernel functions of RBF, has a higher prediction accuracy, simple structure, fast training speed, and different from the ordinary feedforward neural networks, with the best approximation performance and overall optimization. 2. This dissertation takes LS-SVM to represent the methods based on the structural risk minimization principle used for exchange rate prediction, since the methods based on the empirical risk minimization principle have lower prediction accuracy in circumstances of insufficient data. Addressing the issue of slower computation and convergence speeds of the traditional SVM algorithm, this method solved the problem of quadratic programming with LS on the premise of ensured minimal structural risks. Therefore, adopting this method may ensure the accuracy of the algorithm in cases of small sample size, as well as completing the prediction faster. 3. Addressing the deviation existing in both the prediction results from each type of method and in the exchange rate data, this dissertation proposes an exchange rate method based on the Kalman Filter. This method is representative of statistical filtering algorithms and may internally reduce noise in the two models to acquire more accurate prediction values. Therefore, adopting this method may effectively utilize the accuracy of the two models and allow the acquisition of more precise prediction values by statistical means.
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Cheng, Chia-Hsin, and 鄭佳欣. "The Effectiveness of Stochastic (KD Indicator) in Forecasting Exchange Rate." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/35085178213699063793.
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碩士東海大學
工業工程與經營資訊學系
96
Economic fundamental factors were used to predict the variation of currency exchange rates. Many empirical studies, however, point out that the predictability of conventional economic models is often ineffective. In contrast, opponents of technical analysis predict the future price trend by evaluating the previous “prices”and“volume” in the market. The technical analysis has also been in common practice in the financial market. A number of researches in foreign countries have been done on the validity of technical analysis and confirmed that the application would bring in extra profits for the investors of foreign exchange market. Nevertheless, few empirical studies have been practically conducted on the effectiveness of technical analysis in the foreign exchange market in Taiwan. Based on the assumption that the technical indicators are valid, we try to construct the short-term RMN/NT exchange rate forecasting model, combining stochastic indicator trading rules and regression, GARCH and Neural Network model. It is our goal to check the feasibility of stochastic indicator to catch up with the trend of foreign exchange market, to effectively predict exchange rate trend, and to find out the best timing for trading. The empirical results show that the forecasting accuracy of back propagation neural networks(BPN) model performs better than multiple regression model and GARCH(1,1) model, and its direction accuracy also reaches 60%. BPN is proved to be an effective forecasting model in the short-term exchange rate. However, for investors, who can make a profit if they grasp the correct direction, the direction accuracy of these three models are all higher than 50%. Among them, GARCH(1,1) model performs the best and reaches 67%. So GARCH(1,1) model is also a proper forecasting model in exchange rate forecasting. According to the empirical results, the technical analysis is reference-worthy in foreign exchange market. Besides, we also verify that the designs of the proposed models are feasible for forecasting exchange rate. So this study would make its contributions to both the academics and corporations. It also suggests directions for possible future researches.
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Liu, Hsi-Chen, and 劉西真. "Forecasting exchange rate with asymmetric volatility-example of JPY、SGD." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/60796803364385903701.
Full textAbstract:
碩士淡江大學
財務金融學系碩士在職專班
95
In finance, volatility plays a key role in several sub-fields. Whether the construct of portfolio is optimal or not, partly depends on the control of volatility. GARCH family models have been used in the forecast of volatilities, and have performed well in many empirical studies. Recently, Chou (2005) proposed the CARR (Conditional Auto-Regressive Range) model. The main concept of the CARR model is to use a simple dynamic structure for range to characterize the volatility process. In Chou (2005), comparing the CARR model and traditional GARCH model, the former is better in the volatility forecasting based on the data of the S&P 500 index. We use both CARR and GARCH models to test JPY and SGD exchange rate. But we find that different data uses different models. In order to obtain the most accurate projection of volatility and improve the decision-making efficiency, it’s better to apply specific volatility forecast models to different products.
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Chen, Yi-Chang, and 陳宜昌. "Forecasting US/NT Exchange Rate with Various Artificial Neural Networks." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/75865067815209248912.
Full textAbstract:
碩士明志科技大學
工程管理研究所
94
Recently, there are quite a few researches concentrate on forecasting exchange rate via neural network. Among these researches, a best neural network models in forecasting exchange rate is not found yet. So this research tries to use various Back-Propagation Network’s algorithms, Radial Basis Functions network and Adaptive Network-based Fuzzy Inference System to forecast prices of US/NT exchange rate with the expectation of presenting a better forecasting model. The results are as follow; (1) In Back-Propagation Network, the number of hidden neurons doesn’t affect MAPE. As for Adaptive Network-based Fuzzy Inference System, Gauss Membership Function for the input variables, and the best parameters found. (2) Applying one or two input variables to get better performance. (3) Every neural network model which has different train samples. (4) Among these neural network, Bayesian Regularization Method performed best to forecast exchange rate, followed by Adaptive Network-based Fuzzy Inference System, Radial Basis Functions network, Levenberg-Marquardt Method, BFGS Quasi-Newton Method, Scaled Conjugate Gradient Method and One Step Secant Method in sequence. (5) Finally, Bayesian Regularization Method uses 222 train samples, two input variables and ten hidden neurons to obtain better performance.
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Lin, Han-Sheng, and 林翰泩. "A Study of Applying Genetic Algorithm to Exchange Rate Forecasting." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/93406149510508243137.
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碩士國立臺北大學
統計學系
91
This paper is the first research for using genetic algorithm in exchange rate forecasting in Taiwan. The purpose of the paper is to build a multiple regression model by using genetic algorithm and multiple interval rolling regression method. The first step is to transfer exchange rate price data to fifteen technical indicators. Each technical indicator has 10 types of parameters. The second step is to choose from one hundrand fifty independent variables a linear combination of ten independent variables which have the best fitness function value in sample by using genetic algorithm . The Final step is to build a multiple regression model with the ten independent variables which have the best fitness function value in sample to make a sample dynamic prediction by using multiple interval rolling regression method.
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Lee, Yumg-Ming, and 李源明. "NT/Dollar Exchange rate Forecasting at different time-frequency data." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/11032821799372266173.
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Ching-Yi, Lin, and 林靜怡. "Stock Market and Exchange Rate Forecasting–A Portfolio Balance Model." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/55570665066088276856.
Full textAbstract:
碩士國立高雄應用科技大學
金融資訊研究所
100
Reviewing the relative literature of portfolio balance model, most of these papers always ignored the relationship between stock market and exchange rate market as exchange rate being forecasted. Based on the Cushman’s model (Cushman, 2007), this paper will construct a two-country portfolio balance model with stock market to proceed theoretical analysis of exchange rate behavior. Besides, the empirical data of Canada and US are also used to examine and to predict the exchange rate behavior. With the consideration of stock market in portfolio balance model of exchange rate determination, the theoretical derivation of this model shows the increasing of interest rate and stock market value might cause exchange rate increasing or decreasing, depending on the wealth effect of foreign bonds holding by nationals and domestic bonds holding by foreigners. The empirical result shows that the exchange rate, interest rate and stock market value exists a long-term cointergration relationship. The increasing of domestic interest rate will cause the decreasing of exchange rate, while the increasing of stock market value will cause the increasing of exchange rate. Furthermore, comparing the performances of exchange rate forecasting applying different models, we also support that the performance of vector error correction model is better than the performance of random walk model.
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Galkhuu, Namkhaidorj, and Namkhaidorj. "Forecasting High Frequency exchange Rate Application of Artificial Neural Network." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/w38n9k.
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碩士國立東華大學
企業管理學系
106
Forecasting exchange rate has been regarded as one of the most challenging task of modern financial time series analyses for many years. This work has goal whether using Artificial Neural Network (ANN) in high frequency exchange rate is applicable or not. Then neural network predicting performances are compared with Support Vector Machine (SVM) and Long Short Term Memory Neural Network (LSTM). Exchange rates used in experiments are EURO/USD (euro, dollar), USD/JPY (dollar, Japanese, yen), GBP/USD (pound, dollar) USD/CHF (dollar, Swiss franc) and 1 min, 5 min, 10 min, 30 min, time frames have been chosen. Forecasting instrument is Artificial Neural Networks as a machine learning method