In this study, the effects of CBRT monetary policy decisions on the consumer, automobile, housing and commercial loans of the banks during the period from the early of 2004 to the middle of 2011 are examined. In order to perform this study, it is benefited from weekly weighted average loan interest rate data of the banks, which is the data having the highest frequency that could be obtained from the electronic data distribution system of CBRT. Monetary policy instruments of Central Bank may change in the course of time or monetary policy could be executed by more than one instrument. Therefore, as the political interest rate would be insufficient in the calculation of the effect of monetary policy on loan interest rates of the banks, Government Dept Securities&rsquo / premiums are used instead of the political interest rates in this study to make it reflect the policies of central bank more clearly as a whole. Among the Government Dept Securities that have different maturity structure, benchmark bonds that are adapted to the expected political interest rate changes and that react to the unexpected interest rate changes at the high rate (reaction coefficient 0.983) are used. In order to weight the cointegration relation between interest rates, unrestricted error correction model is established and it is determined by Bound Test that there is a long-term relation between each interest rate and interest rate of benchmark bond. After a cointegration relation is determined among the serials, autoregressive distributed lag model is used to determine the level of transitivity and it is determined that monetary policy decisions affect the banking interest rate at 77% level and by 13 weeks delay on average.

Η εργασία που ακολουθεί εκπονήθηκε στα πλαίσια του μεταπτυχιακού προγράμματος σπουδών “Διοίκηση Επιχειρήσεων Τροφίμων ”, του τμήματος Διοίκησης Αγροτικών Προϊόντων και Τροφίμων του Πανεπιστημίου Πατρών, κατά την ακαδημαϊκή περίοδο σπουδών 2011-2013. Ο μηχανισμός μετακύλισης των τιμών μεταξύ παραγωγών και καταναλωτών έχει αποτελέσει αντικείμενο εκτεταμένης έρευνας κυρίως στον τομέα των τροφίμων. Η παρούσα εργασία αναλύει τα βασικά προϊόντα του νομού Αιτωλοακαρνανίας κυρίως του πρωτογενή τομέα ενώ ειδικότερα εξετάζει την ύπαρξη ασυμμετρίας στον μηχανισμό μεταβίβασης τιμών μεταξύ των παραγωγών και των καταναλωτών στον τομέα του κρέατος στην Ελλάδα. Πιο συγκεκριμένα, η έρευνα επικεντρώνεται στα τέσσερα είδη κρέατος όπως μοσχαριού, αρνιού, χοιρινού και κοτόπουλου. Οι τιμές των τεσσάρων ειδών κρέατος έχουν χορηγηθεί από την Ελληνική Στατιστική Αρχή. Όλες οι μεταβλητές μετασχηματίζονται σε λογαρίθμους και οι τιμές αποπληθωρίζονται με βάση τον δείκτη τιμών καταναλωτή (2009=100). Για την μελέτη της ασυμμετρίας γίνεται χρήση των τεχνικών συνολοκλήρωσης (Threshold Cointegration Analysis) καθώς εκτιμάται και ένα δυναμικό υπόδειγμα διόρθωσης λαθών (Error Correction Model, ECM). Διερευνάται η ύπαρξη μακροχρόνιας σχέσης μεταξύ παραγωγών και καταναλωτών στην υπό εξέταση αγορά, ενώ η κατεύθυνση της αιτιότητας κατά Granger έδειξε πως η τιμή του καταναλωτή επηρεάζει την τιμή του παραγωγού. / The following assignment carried out through of the postgraduate program "Food Business Management" at the department of Management Food and Agricultural products of University of Patras, the academic study period 2011-2013. The price transmission mechanism between producers and consumers has been the subject of extensive research mainly in the food sector. This paper analyzes the commodities the county of Aitoloakarnania mainly in the primary sector while particularly considers the existence of asymmetry in the transmission mechanism of prices between producers and consumers in the meat sector in Greece. More specifically, the research focused on four types of meat such as beef, lamb, pork and chicken. The values of the four types of meat have been granted by the Greek Statistical Authority. All variables transformed into logarithms and prices are deflated by the consumer price index (2009 = 100). For studying the asymmetry are used techniques of cointegration (Threshold Cointegration Analysis) and estimated as a dynamic error correction model (Error Correction Model, ECM). Investigated the existence of long-term relationships between producers and consumers in the relevant market, while the direction of Granger causality test showed that the price of the consumer affects the value of the producer.

Η παρούσα μελέτη εξετάζει τη δυναμική σχέση μεταξύ των τιμών των ζωοτροφών και παραγωγού, καταναλωτή για τέσσερα είδη κρέατος: μοσχάρι, χοιρινό, αρνί και κοτόπουλο. Η σχετική βιβλιογραφία δείχνει ότι πολλοί παράγοντες επιδρούν στις τιμές των αγροτικών προϊόντων αλλά οι τιμές των ζωοτροφών είναι ο κυριότερος. Αυτό συμβαίνει γιατί οι ζωοτροφές αποτελούν πρώτη ύλη για την παραγωγή κρέατος και κατ΄επέκταση θα επηρέασουν τις τιμές παραγωγού και καταναλωτή. Τα δεδομένα αποτελούνται από 279 μηνιαίες τιμές που εκτείνονται από τον Ιανουάριο 1990 έως τον Ιανουάριο 2013. Χρησιμοποιώντας Johansen cointegration tests, Granger causality tests και impulse response functions τα εμπειρικά αποτελέσματα επιβεβαιώνουν πως οι τιμές των ζωοτροφών, οι τιμές παραγωγού και οι τιμές καταναλωτή δεν είναι ανεξάρτητες μεταξύ τους. / The present paper studies the relationship among feed prices, producer prices and consumer prices of meat: beef, pork, poultry and lamb. The literature indicates that there are many factors which affect agricultural commodity prices but the feed prices are the main. This is why feed has a principal role in the production of meat and will affect producer and consumer prices. The data consists of 279 monthly observations extending from January 1990 to January 2013. Using Johansen cointegration tests, Granger causality tests and impulse response functions, the empirical findings confirm that feed prices, consumer prices and producer prices are interdependent.

The supervising institutions do not give to commercial banks indications what models have to be used for stress testing. This research was done in order to find out which mathematical/statistical models are and can be used in credit risk stress testing. Credit risk is one of the biggest financial risks that every bank faces. Stress testing is a tool of credit risk assessment that helps to estimate the consequences of the events that have really small probability to happen but if they occur, banks can have significant losses. This study determined that the most plausible event is adverse macroeconomic conditions. For this reason, models that include macroeconomic impact were presented. Vector autoregression and vector error correction model were tested using the empirical data received from Swedish central bank, Swedish statistics and Eurostat. For financial stability it is worth using vector autoregression or vector error correction model as they describe the macroeconomic environment in the most suitable way and they are appropriate for shock analysis by showing how the impact of any factor can change the whole system. Structure: introduction, main part (credit risk, methods and empirical analysis), publication, conclusions, references. Thesis consists of: 50 p. text without appendices, 13 pictures, 11 tables, 26 bibliographical entries. Appendices included. / Kredito įstaigų priežiūros institucijos nepateikia komerciniams bankams kokius metodus jie turėtų naudoti testavime nepalankiomis sąlygomis. Tiriamasis darbas buvo atliktas tuo tikslu, kad būtų išsiaiškinta kokie matematiniai ir statistiniai metodai yra ir gali būti naudojami kredito rizikos vertinime testuojant nepalankiomis sąlygomis. Kredito rizika yra viena iš didžiausių finansinių rizikų su kuria bankai susiduria. Testavimas nepalankiomis sąlygomis yra kredito rizikos vertinimo įrankis, padedantis nustatyti įvykių, kurių realizavimosi tikimybės yra mažos, tačiau jiems įvykus, bankai patirtų reikšmingus nuostolius, pasekmes. Šis tyrimas nustatė, jog labiausiai tikėtinas įvykis gali būti ypatingai nepalankios ekonominės sąlygos. Dėl šios priežasties darbe yra pristatyti metodai, kurie įvertina makroekonominių veiksnių įtaką. Vektorinė autoregresija ir vektorinis paklaidų korekcijos modelis buvo patikrinti naudojant Švedijos centrinio banko, Švedijos statistikos departamento ir Eurostat empirinius duomenis. Finansinio stabilumo įvertinimui vertėtų naudoti vektorinį autoregresijos ar vektorinį paklaidų korekcijos modelius, nes šie modeliai geriausiai aprašo ekonominę aplinką bei yra labai tinkami šokų analizei, kadangi įvertina bet kurio veiksnio įtaką visai sistemai. Struktūra: įvadas, pagrindinė dalis (kredito rizika, metodai ir empirinė analizė), publikacija, išvados, literatūros sąrašas. Tiriamasis darbas sudarytas iš: 50 psl. teksto be priedų, 13 paveikslų, 11... [toliau žr. visą tekstą]

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Baigiamajame magistro darbe yra nagrinėjamas Baltijos šalių – Lietuvos, Latvijos ir Estijos BVP, jo kitimo tendencijos bei pagrindiniai makroekonomikos rodikliai – galutinio vartojimo išlaidos, prekių ir paslaugų importas bei eksportas, investicijos, užimtųjų darbo rinkoje skaičius bei BVP defliatorius. Išnagrinėti šių rodiklių apibrėžimai bei ekonominė prasmė, taip pat apžvelgta sukaupta mokslininkų patirtis modeliuojant juos. Siekiant išsiaiškinti ryšius tarp šių rodiklių atlikta koreliacijų bei Grangerio priežastingumo analizė. Modeliuojant, visų pirma, rasti ilgalaikiai sąryšiai tarp kintamųjų, o po to kiekvienai šaliai specifikuotas paklaidų korekcijos modelis bei įvertintas šio modelio tikslumas. Išnagrinėjus praktinius ir teorinius aspektus, pateikiamos baigiamojo darbo išvados ir pasiūlymai. Darbą sudaro 5 dalys: įvadas, analitinė - metodinė dalis, tiriamoji dalis, išvados ir pasiūlymai, literatūros sąrašas. Darbo apimtis – 112 p. teksto be priedų, 60 iliustr., 35 lent., 28 bibliografiniai šaltiniai. Atskirai pridedami darbo priedai. / The gross domestic product of the Baltic States – Lithuania, Latvia and Estonia, has been examined in the master thesis. Trends of the GDP and the main macroeconomic indicators – gross final consumption expenditure, imports and exports of goods and services, investment, labor force and GDP deflator have been analyzed. The definitions of these indicators and an economic sense, as well as researchers' experience in modeling, have been examined. In order to establish the relationship between these variables, analysis of the correlation and Granger causality test has been performed. In particular, modeling started with the finding of the long – term relationship between selected indicators. After that the error correction model has been specified and the accuracy of this model has been estimated. After the examination of the practical and theoretical aspects, the conclusions and recommendations have been presented. Structure: introduction, analytical – methodical part, research part, conclusions and suggestions, references. Thesis consists of: 112 p. text without appendixes, 60 pictures, 35 tables, 28 bibliographical entries. Appendixes included.

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。

Tourism is currently the third largest industry within South Africa. Many African countries, including South Africa, have the potential to achieve increased economic growth and development with the aid of the tourism sector. As tourism is a great earner of foreign exchange and also creates employment opportunities, especially low–skilled employment, it is identified as a sector that can aid developing countries to increase economic growth and development. Accurate forecasting of tourism demand is important due to the perishable nature of tourism products and services. Little research on forecasting tourism demand in South Africa can be found. The aim of this study is to forecast tourism demand (international tourist arrivals) to South Africa by making use of different causal models and to compare the forecasting accuracy of the causal models used. Accurate forecasts of tourism demand may assist policy–makers and business concerns with decisions regarding future investment and employment. An overview of South African tourism trends indicates that although domestic arrivals surpass foreign arrivals in terms of volume, foreign arrivals spend more in South Africa than domestic tourists. It was also established that tourist arrivals from Africa (including the Middle East), form the largest market of international tourist arrivals to South Africa. Africa is, however, not included in the empirical analysis mainly due to data limitations. All the other markets namely Asia, Australasia, Europe, North America, South America and the United Kingdom are included as origin markets for the empirical analysis and this study therefore focuses on intercontinental tourism demand for South Africa. A review of the literature identified several determinants of tourist arrivals, including income, relative prices, transport cost, climate, supply–side factors, health risks, political stability as well as terrorism and crime. Most researchers used tourist arrivals/departures or tourist spending/receipts as dependent variables in empirical tourism demand studies. The first approach used to forecast tourism demand is a single equation approach, more specifically an Autoregressive Distributed Lag Model. This relationship between the explanatory variables and the dependent variable was then used to ex post forecast tourism demand for South Africa from the six markets identified earlier. Secondly, a system of equation approach, more specifically a Vector Autoregressive Model and Vector Error Correction Model were estimated for each of the identified six markets. An impulse response analysis was undertaken to determine the effect of shocks in the explanatory variables on tourism demand using the Vector Error Correction Model. It was established that it takes on average three years for the effect on tourism demand to disappear. A variance decomposition analysis was also done using the Vector Error Correction Model to determine how each variable affects the percentage forecast variance of a certain variable. It was found that income plays an important role in explaining the percentage forecast variance of almost every variable. The Vector Autoregressive Model was used to estimate the short–run relationship between the variables and to ex post forecast tourism demand to South Africa from the six identified markets. The results showed that enhanced marketing can be done in origin markets with a growing GDP in order to attract more arrivals from those areas due to the high elasticity of the real GDP per capita in the long run and its positive impact on tourist arrivals. It is mainly up to the origin countries to increase their income per capita. Focussing on infrastructure development and maintenance could contribute to an increase in future tourist arrivals. It is evident that arrivals from Europe might have a negative relationship with the number of hotel rooms available since tourists from this region might prefer accommodation with a safari atmosphere such as bush lodges. Investment in such accommodation facilities and the marketing of such facilities to Europeans may contribute to an increase in arrivals from Europe. The real exchange rate also plays a role in the price competitiveness of the destination country. Therefore, in order for South Africa to be more price competitive, inflation rate control can be a way to increase price competitiveness rather than to have a fixed exchange rate. Forecasting accuracy was tested by estimating the Mean Absolute Percentage Error, Root Mean Square Error and Theil’s U of each model. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was estimated for each origin market as a benchmark model to determine forecasting accuracy against this univariate time series approach. The results showed that the Seasonal Autoregressive Integrated Moving Average model achieved more accurate predictions whereas the Vector Autoregressive model forecasts were more accurate than the Autoregressive Distributed Lag Model forecasts. Policy–makers can use both the SARIMA and VAR model, which may generate more accurate forecast results in order to provide better policy recommendations. / Thesis (M.Com. (Economics))--North-West University, Potchefstroom Campus, 2011.

Tourism is currently the third largest industry within South Africa. Many African countries, including South Africa, have the potential to achieve increased economic growth and development with the aid of the tourism sector. As tourism is a great earner of foreign exchange and also creates employment opportunities, especially low–skilled employment, it is identified as a sector that can aid developing countries to increase economic growth and development. Accurate forecasting of tourism demand is important due to the perishable nature of tourism products and services. Little research on forecasting tourism demand in South Africa can be found. The aim of this study is to forecast tourism demand (international tourist arrivals) to South Africa by making use of different causal models and to compare the forecasting accuracy of the causal models used. Accurate forecasts of tourism demand may assist policy–makers and business concerns with decisions regarding future investment and employment. An overview of South African tourism trends indicates that although domestic arrivals surpass foreign arrivals in terms of volume, foreign arrivals spend more in South Africa than domestic tourists. It was also established that tourist arrivals from Africa (including the Middle East), form the largest market of international tourist arrivals to South Africa. Africa is, however, not included in the empirical analysis mainly due to data limitations. All the other markets namely Asia, Australasia, Europe, North America, South America and the United Kingdom are included as origin markets for the empirical analysis and this study therefore focuses on intercontinental tourism demand for South Africa. A review of the literature identified several determinants of tourist arrivals, including income, relative prices, transport cost, climate, supply–side factors, health risks, political stability as well as terrorism and crime. Most researchers used tourist arrivals/departures or tourist spending/receipts as dependent variables in empirical tourism demand studies. The first approach used to forecast tourism demand is a single equation approach, more specifically an Autoregressive Distributed Lag Model. This relationship between the explanatory variables and the dependent variable was then used to ex post forecast tourism demand for South Africa from the six markets identified earlier. Secondly, a system of equation approach, more specifically a Vector Autoregressive Model and Vector Error Correction Model were estimated for each of the identified six markets. An impulse response analysis was undertaken to determine the effect of shocks in the explanatory variables on tourism demand using the Vector Error Correction Model. It was established that it takes on average three years for the effect on tourism demand to disappear. A variance decomposition analysis was also done using the Vector Error Correction Model to determine how each variable affects the percentage forecast variance of a certain variable. It was found that income plays an important role in explaining the percentage forecast variance of almost every variable. The Vector Autoregressive Model was used to estimate the short–run relationship between the variables and to ex post forecast tourism demand to South Africa from the six identified markets. The results showed that enhanced marketing can be done in origin markets with a growing GDP in order to attract more arrivals from those areas due to the high elasticity of the real GDP per capita in the long run and its positive impact on tourist arrivals. It is mainly up to the origin countries to increase their income per capita. Focussing on infrastructure development and maintenance could contribute to an increase in future tourist arrivals. It is evident that arrivals from Europe might have a negative relationship with the number of hotel rooms available since tourists from this region might prefer accommodation with a safari atmosphere such as bush lodges. Investment in such accommodation facilities and the marketing of such facilities to Europeans may contribute to an increase in arrivals from Europe. The real exchange rate also plays a role in the price competitiveness of the destination country. Therefore, in order for South Africa to be more price competitive, inflation rate control can be a way to increase price competitiveness rather than to have a fixed exchange rate. Forecasting accuracy was tested by estimating the Mean Absolute Percentage Error, Root Mean Square Error and Theil’s U of each model. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was estimated for each origin market as a benchmark model to determine forecasting accuracy against this univariate time series approach. The results showed that the Seasonal Autoregressive Integrated Moving Average model achieved more accurate predictions whereas the Vector Autoregressive model forecasts were more accurate than the Autoregressive Distributed Lag Model forecasts. Policy–makers can use both the SARIMA and VAR model, which may generate more accurate forecast results in order to provide better policy recommendations. / Thesis (M.Com. (Economics))--North-West University, Potchefstroom Campus, 2011.

Nesta tese são desenvolvidos três estudos sobre as reservas internacionais brasileiras, utilizando diferentes técnicas econométricas, com o objetivo de determinar a influência de medidas absolutas e relativas de reservas sobre o rating soberano de crédito e o spread soberano, bem como o nível adequado para garantir a liquidez externa. As análises foram feitas com dados mensais do período jan/2000-jun/2008. No primeiro estudo, mostrou-se que diferentes medidas de reservas internacionais apresentam efeito significativo na explicação do rating soberano de crédito, através de modelos ordered logit para a média dos ratings emitidos pelas três principais agências (Moody's, Standard & Poors e Fitch). Entretanto, o indicador de maior poder explicativo não foi o nível absoluto de reservas, mas a razão entre dívida pública externa líquida e PIB. Outras variáveis de destacada importância na maioria dos modelos foram o percentual da dívida interna de curto prazo, investimento estrangeiro direto/PIB e inflação. Variáveis tradicionalmente utilizadas como indicadores de liquidez, como razão reservas/importações e conta corrente/PIB, não foram significativas na maioria dos modelos. Os resultados confirmam os indícios contidos no discurso das agências de rating, quanto à importância das reservas internacionais em sua avaliação, embora alertando que outras variáveis, como perfil de endividamento do governo e perspectivas de crescimento, são também fundamentais. O segundo estudo de caso encontrou relação significativa entre as reservas internacionais e o spread soberano, através de modelos de correção de erros. O efeito estimado do rating soberano foi não-significativo ou pouco explicativo, comparado aos fundamentos, provavelmente devido à volatilidade do spread soberano em resposta a variações nas condições do mercado, ao contrário do rating. O melhor modelo obtido utilizou o nível absoluto de reservas, evidenciando também efeitos significativos da aversão global ao risco, taxas de juros internacionais e crises políticas internas. Os resultados desse estudo indicam custo marginal decrescente das reservas internacionais e a necessidade de considerá-lo endógeno em modelos de minimização de custos para determinação do nível ótimo de reservas. O terceiro estudo implementou a metodologia de Liquidity-at-Risk sugerida por Greenspan (1999) para avaliar a adequação do nível de reservas internacionais para a manutenção da liquidez externa. Para a medida de liquidez reservas/dívida externa de curto prazo (razão de Guidotti), estimou-se que o nível de reservas internacionais mantidas pelo Brasil em jun/2008 (US$200 bilhões) era aproximadamente o dobro do necessário para garantir uma razão de Guidotti superior a 1, com 99% de probabilidade, durante 24, 36 ou 48 meses. Em diversos cenários alternativos de percentual das dívidas externa e interna de curto prazo, meta de superávit primário, índice de aversão ao risco e taxas de juros externas, as reservas iniciais necessárias situaram-se em US$85-105 bilhões. A análise de custos revela que o aumento das reservas diminui os juros médios da dívida, embora efeito maior pudesse ser alcançado através do aumento do superávit primário. As evidências sugerem que a motivação das autoridades brasileiras para a manutenção de reservas em torno de US$200 bilhões não é puramente precaucionária, admitindo as hipóteses de ganho de credibilidade e flexibilidade para a execução da política fiscal. / This thesis developed three case studies on the Brazilian international reserves, using various econometric techniques in order to determine the influence of absolute and relative measures of reserves over both the sovereign credit rating and the sovereign spread, as well as to assess the adequate reserves level to ensure external liquidity. Analyses were carried out on monthly data from Jan/2000 to Jun/2008. The first case study found significant effects of different reserves measures in explaining the sovereign credit rating, by fitting ordered logit models to the average of the ratings issued by the three main agencies (Moody's, Standard & Poors and Fitch) for the Brazilian long term external debt. However, the best explaining variable was not the absolute level of reserves, but the ratio "net public external debt/GDP" instead. It was noteworthy the significance of the following variables in most of the models tested: short term internal debt (%), foreign direct investment/GDP and inflation. Variables traditionally used as external liquidity measures, like reserves/imports and current account/GDP, are not statistically significant in most of the models fitted in this study. Results support the evidence found in the rating agencies' reports, as to the importance of international reserves in their credit quality assessment, although pointing to other variables, like government debt profile and growth perspectives, as equally critical. The second case study found significant relationship between the Brazilian international reserves and its sovereign spread, using error correction models. The estimated effect of sovereign rating was either non-significant, or poorly explanatory when compared to macroeconomic fundamentals, probably due to the volatility of sovereign spread in response to changes in market conditions, unlike the sovereign rating. The best model obtained included the absolute level of reserves, showing also significant effect of the global risk aversion, external interest rates and internal political crises. The results of this study point to a decreasing marginal cost of international reserves and the need of considering it as endogenous in optimal reserves models based in cost minimization. Finally, the third case study implemented the Liquidity-at-Risk methodology suggested by Greenspan (1999), in order to assess the Brazilian reserves level adequacy in maintaining external liquidity. For the liquidity measure adopted - the ratio "reserves/short term external debt" (Guidotti's ratio) - it was found that the Brazilian reserves level held in Jun/2008 (US$200 billion) was roughly twice the necessary one to ensure a Guidotti's ratio above 1, with 99% probability, within 24, 36 or 48 months. In several alternative scenarios varying the short term external debt, short term internal debt, primary surplus, global risk aversion and external interest rates, the required initial reserves was in the range US$85-105 billion. An analysis of alternative policies' costs revealed the expected effect of higher reserves in decreasing the average debt service, although a dramatically higher impact would be obtained by an increase in primary surplus. Evidence suggest that the Brazilian authorities motivation for holding international reserves as high as US$200 billion may not be purely precautionary, pointing to the hypotheses of credibility gains and fiscal flexibility issues.