The exchange rate forecasting has been an interesting topic for a long time. Beating the random walk model has been the goal of many researchers, who applied various techniques and used various datasets. We tried to beat it using bayesian model averaging technique, which pools a large amount of models and the final forecast is the average of forecasts of these models. We used quarterly data from 1980 to 2013 and attempted to predict the value of exchange rate return of five currency pairs. The novelty was the fact that none of these currency pairs included U.S. Dollar. The forecasting horizon was one, two, four and eight quarters. In addition to random walk, we also compared our results to historical average return model using several benchmarks, such as root mean squared error, mean absolute error or direction of change statistic. We found out that bayesian model averaging can not generally outperform random walk or historical average return, but in specific setting it can produce forecasts with low error and with high percentage of correctly predicted signs of change.

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.

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.

This dissertation includes three chapters. The first chapter studies the question of whether countries with different fiscal capacity should optimally have different ex-ante minimum bank capital requirements. In an environment with endogenously incomplete markets and overinvestment because of moral hazard and pecuniary externalities, I show that countries with larger fiscal capacity should have lower minimum ex-ante bank capital requirements. I also show that, in addition to the minimum capital requirement, regulators in countries with a concentrated financial sector and large fiscal capacity (which are also countries with strong moral hazard) should impose a limit on the amount of liquidity pledged by financial institutions in a crisis state (for example, restrict the amount of put options/CDS contracts sold by financial institutions). The second chapter studies the welfare implications of a concentrated, imperfectly competitive banking sector, which faces a bank net worth constraint in a small open economy (SOE) environment. There are two standard sources of inefficiency --- pecuniary externalities, which lead to overinvestment, and a standard monopolistic underinvestment force. I show that the optimal policy instruments include subsidies on firm borrowing costs in certain periods and capital account controls in others, which is a good proxy for the behavior of emerging markets. For every country, there exists a financial sector with a particular banking sector concentration, for which the inefficiencies offset each other and no government intervention is required in some periods. Furthermore, this paper documents a novel theoretical result --- the interaction between future binding bank net worth constraints and dynamic (future) underinvestment could lead to ex-ante overinvestment even in economies with a single monopolistic bank where there are no pecuniary externalities. The last third chapter, which is coauthored with Kenneth Rogoff, evaluates a new class of exchange rate forecasting studies, which claim that structural models are getting closer to being able to forecast exchange rates at short horizons. We argue that misinterpretation of some new out-of-sample tests for nested models, over-reliance on asymptotic test statistics, and failure to sufficiently check robustness to alternative time windows have led many studies to overstate even the relatively thin positive results that have been found. / Economics

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.

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

本研究以新台幣兌美元之匯率日資料作為主要研究標的，同時加入台灣加權股價指數及金融業隔夜拆借利率之日資料作為股價與利率之代理變數，利用Beveridge-Nelson分解趨勢的方法將變數資料拆解成趨勢項與循環項之時間序列資料，藉此捕捉匯率資料具有景氣循環的特性。在循環項的序列資料，以向量自我迴歸模型來分析並予以估計，趨勢項的部分，利用共整合檢定來探討趨勢項變數間長期的均衡關係，再以向量誤差修正模型予以估計，得到未來30天期之匯率走勢。接著，再以RMSE與MAE指標來衡量不同模型之匯率預測績效，以期能找出最適之匯率預測模型。 實證研究結果發現，將匯率資料先透過Beveridge-Nelson分解趨勢的方法予以拆解後，再利用時間序列模型進行分析及預測，時間序列模型的預測能力都比原始匯率利用時間序列模型進行預測或透過ARIMA模型進行預測還要來的好。因此，根據實證研究的結果，若企業與政府在進行匯率預測的分析時，能夠考慮先將匯率資料透過Beveridge-Nelson分解方法予以處理，便能更有效提升模型的預測能力，除了企業能夠降低避險成本來提高公司整體績效，對於國家而言，有效的掌握匯率的趨勢便能夠迅速且正確的制定政策，提升國家的經濟發展。

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.

在全球經貿體系自由化下，國際資金流通快速，匯率變動也非常頻繁，廠商的產銷決策與營運，面對匯率風險更加難以掌控。如何掌握匯率的變動，並採取有效的避險措施，是廠商從事貿易必須面臨之重要課題。本研究採用自我迴歸整合移動平均模式、倒傳遞類神經網路及混合式自我迴歸整合移動平均模式及倒傳遞類神經網路模型進行未來即期匯率報酬率之預測。試圖找出合適的新台幣兌美元即期匯率之預測模型，並將其應用於外匯避險操作。 研究結果顯示，關於預測誤差的績效表現，整體來說，以自我迴歸整合移動平均及倒傳遞類神經網路混合式模型表現最佳，顯示傳統時間序列模型捕捉匯率報酬率走勢之能力，藉由倒傳遞類神經網路捕捉其線性預測誤差中非線性的部分，可更符合資料的特性，加強匯率報酬率預測的準確性。考慮預測方向的正確性，在兩個不同的準則下(SR、PT)，皆以自我迴歸整合移動平均模型表現最差，代表其在進行匯率報酬率之預測時正確率較為不足。而在PT檢定當中，倒傳遞類神經網路模型及混合式模型皆達到顯著。因此利用人工智慧模型對報酬率之方向進行預測是有效的，又以自我迴歸整合移動平均及倒傳遞類神經網路混合式模型表現最好。總結來說，利用倒傳遞類神經網路模型針對自我迴歸整合移動平均模型做非線性的調整，同時涵蓋未來匯率報酬率線性與非線性的部分，使得自我迴歸整合移動平均模型之預測誤差、方向準確性皆得到改善，藉由倒傳遞類神經網路捕捉其線性預測誤差中非線性的部分，可更符合資料的特性，加強匯率報酬率預測的準確性。

本研究提出UIP、PPP、 MF、TR及TRa五種匯率預測模型，以新台幣兌美元即期匯率、遠期匯率進行避險準確率及避險成效的實證分析。資料期間為1996年12月到2012年10月的新台幣兌美元即期匯率月資料，資料來源為資料庫Datastream。 實證結果發現UIP、PPP、 MF、TR及TRa五種匯率預測模型比較分析中，若以相對購買力平價模型(PPP)進行選擇性避險，再搭配適當避險比率，其報酬率可能由負報酬轉為正報酬；避險績效衡量方面，以相對購買力平價模型搭配完全避險的績效最好。若以不對稱泰勒模型(TRa)進行選擇性避險，再搭配適當避險比率，報酬率明顯由負轉為正；衡量避險績效衡量方面，完全避險在風險降幅及下方動差避險績效衡量下，以不對稱泰勒模型搭配完全避險的績效最好。 / This study provides five exchange rate models to predict future exchange rate (UIP,PPP,MF,Taylor rule and asymmetric Taylor rule). We illustrate these methods by assessing the forecasting performance of five exchange rate models using monthly returns on TWD/US dollar exchange rate. The data are monthly exchange rates ranging from December 1996 to October 2012, using spot and one-month forward exchange rates form Datastream. We find that empirical models based on purchase power parity (PPP) and the asymmetric Taylor rule(TRa) outperform the other models in out-of-sample forecasting using the appropriate hedging ratio. Comparing the hedging performance between PPP and models, we find that the hedging performance by the PPP will get the higher return. However, the hedging performance by the will get the lower volatility.