Since its’ invention at Morgan Stanley in 1987 pairs trading has grown to be one of the most common and most researched strategies for market neutral returns. The strategy identifies stocks, or other financial securities, that historically has co-moved and forms a trading pair. If the price relation is broken a short position is entered in the overperforming stock and a long in the underperforming. The positions are closed when the spread returns to the long-term relation. A pairs trading portfolio is formed by combining a number of pairs. To detect adequate pairs different types of data analysis has been used. The most common way has been to study historical price data with different statistical models such as the distance method. Gatev et al (2006) used this method and provided the most extensive research on the subject and this study will follow the standards set by that article and add new interesting factors. This is done through an investigation on how the analysis can be improved by using the stocks fundamental data, e.g. P/E, P/B, leverage, industry classification. This data is used to set up restrictions and Lasso models (type of regression) to optimize the trading portfolio and achieve higher returns. All models have been back-tested using S&P 500 stocks between 2001-04-01 and 2015-04-01 with portfolios changed every six months. The most important finding of the study is that restricting stocks to have close P/E-ratios combined with traditional price series analysis increases returns. The most conservative measure gives annual returns of 3.99% to 4.98% depending on the trading rules for this portfolio. The returns are significantly (5%-level) higher than those obtained by the traditional distance method. Considerable variations in return levels is shown to be created when capital commitments are changed and trading rules, transaction costs and restrictions on unique portfolio stocks are implemented. Further research regarding how analysis of P/E-ratios can improve pairs trading is suggested. The thesis has been written independently without an external client and studied an area that the author found interesting.

Abstract The main objective of our paper is to investigate whether expansion in exports can lead to improve economic growth of Sub-Saharan African countries for the period 1970-2006. Four macro economic indicators (real GDP, Trade balance, Government expenditure and Investment) are used in our model to carry out our analysis concerning Sub Saharan African countries. Time series techniques such as unit root test (Augmented Dickey Fuller test) and co integration test (Johansen’s procedure) are used to find out whether there is a long run relationship between economic growth and trade balance. The results of the unit root test indicate that all series are stationary after first difference, with I (1). Johansen’s co integration test showed that co integration (long run relationship) exists between GDP and Trade balance, as we got significant eigenvalues and found co integration between all of the four variables which shows that they are co integrated with each other and indicates a long run relationship. Our results indicate that for the time period of 1970 to 2006, Sub Saharan African countries experienced a simultaneous increase in economic growth and trade balance as well as in investment and Govt expenditure.   Key words: exports, economic growth, unit root, co integration, Sub-Saharan Africa

The efficiency of commodity futures markets is a widely debated topic in academia. The cotton futures market is no exception. The existence of trends in the futures market is characterized as a price bias, which is a testable trait. When analyzed, it allows a better understanding of market behavior and allows implementation of more effective income enhancing and/or risk reducing strategies. Three different approaches will be used to test the efficiency of the U.S. cotton futures market: pricing patterns, cointegration, and asset-pricing. In the first approach, pricing patterns, statistical methodology was applied to a dataset of daily futures prices. Returns did not show a consistent trend, supporting arguments of efficiency. Further research into seasonally-differentiated contracts has yielded strong evidence of declining prices. This result differs from previously published work in the most comprehensive study of futures prices, while updating and extending information on pricing patterns in the cotton futures market. Co-integration, the second approach, is a popular method for testing the efficiency of various commodity future and cash markets. Evidence indicates that the cotton futures and cash markets are co-integrated over the last ten years. Results lead to the conclusion that price is discovered in the cotton futures market, reinforcing the notion of an efficient cotton futures market that serves as an indicator for future cotton cash prices. The cotton futures market was also analyzed to explain price movements with an equilibrium asset-pricing framework, in the third approach. In particular, the cotton futures market was analyzed to determine if behavior displayed by the market could be explained by risks specific to the cotton futures contract. Cotton futures do not show significant risk premiums over other financial assets, again supporting the efficient market hypothesis. The three approaches implemented in this thesis are generally supportive of longrun efficiency in the U.S. cotton futures market. An updated analysis of the cotton futures market will allow market participants the most recent information on pricing patterns and the overall long-run behavior of the market. More effective trading and operating strategies can be implemented that will best meet needs of market participants.

Abstract With the processing of globalization and the large increases in international trade and openness, it is important to capture the business cycle correlation with the intimate countries for government to make better policies and keep the economy steady. This study investigated the changes in relationships between the G7 business cycle after the European integration. Choosing 1993 the Europe Union (EU) commencement as the segment, we separated the sample period into 1965:1-1992:4 and 1993:1-2006:4.We adopted kinds of unit root tests to exam if these variables were stationary and the Johansen co-integration analysis to test whether the stationary long-run equilibrium exist or not. With the consideration of long run information, the Vector Error Correction Model (VECM) was applied to study the relationship between the business cycles of United State, EU and the other G7 countries. By Johansen co-integration analysis, we found that the stationary long-run relationship did exist between their industrial productions¡]IP¡^. In addition, the VECM evidence supported the emergence of two cyclically coherent groups -- the Euro-zone and the English-speaking countries -- after the EU commencement in 1993. In conclusion, with the greater correlation of business cycles, the party in office should take account of the business cycle movement of the closed countries more deliberately in this regionalization era.

<p><strong>Abstract</strong></p><p>The main objective of our paper is to investigate whether expansion in exports can lead to improve economic growth of Sub-Saharan African countries for the period 1970-2006. Four macro economic indicators (real GDP, Trade balance, Government expenditure and</p><p>Investment) are used in our model to carry out our analysis concerning Sub Saharan African countries.</p><p>Time series techniques such as unit root test (Augmented Dickey Fuller test) and co integration test (Johansen’s procedure) are used to find out whether there is a long run relationship between economic growth and trade balance.</p><p>The results of the unit root test indicate that all series are stationary after first difference, with I (1). Johansen’s co integration test showed that co integration (long run relationship) exists between GDP and Trade balance, as we got significant eigenvalues and found co integration between all of the four variables which shows that they are co integrated with each other and indicates a long run relationship.</p><p>Our results indicate that for the time period of 1970 to 2006, Sub Saharan African countries experienced a simultaneous increase in economic growth and trade balance as well as in investment and Govt expenditure.</p><p> </p><p><strong>Key words</strong>: exports, economic growth, unit root, co integration, Sub-Saharan Africa</p>

Using both single and vector processes, we fitted the Box-Jenkin’s ARIMA model and the Vector Autoregressive model following the Johansen approach, to forecast soy bean and green bean prices on the Chinese futures markets. The results are encouraging and provide empirical evidence that the vector processes perform better than the single series. The co-integration test indicated that the null hypothesis of no co-integration among the relevant variables could be rejected. This is one of the most important findings in this paper. The purposes for analyzing and modeling the series jointly are to understand the dynamic relationships over time among the series and improve the accuracy of forecasts for individuals series by utilizing the additional information available from the related series in the forecasts for each series.

114f. / Submitted by Suelen Reis (suziy.ellen@gmail.com) on 2013-03-13T14:02:58Z No. of bitstreams: 1 Sandra%20Oliveira%201.pdf: 2033579 bytes, checksum: 3e405e708e055309e91c3cf3e2964f6e (MD5) / Approved for entry into archive by Vania Magalhaes(magal@ufba.br) on 2013-03-14T13:20:45Z (GMT) No. of bitstreams: 1 Sandra%20Oliveira%201.pdf: 2033579 bytes, checksum: 3e405e708e055309e91c3cf3e2964f6e (MD5) / Made available in DSpace on 2013-03-14T13:20:45Z (GMT). No. of bitstreams: 1 Sandra%20Oliveira%201.pdf: 2033579 bytes, checksum: 3e405e708e055309e91c3cf3e2964f6e (MD5) Previous issue date: 2007 / Esta dissertação tem como principal objetivo investigar alguns dos principais determinantes da competitividade dos setores exportadores brasileiros apontados pela literatura teórica e empírica. No conjunto das teorias do comércio internacional, estes são diversos, mas muitas dessas teorias sugerem que deve haver uma relação entre produtividade e exportações, embora isso não valide uma teoria particular. Ademais, pode-se perguntar qual o papel cumprido pela produtividade do trabalho no desempenho do setor exportador brasileiro recente e qual sua importância relativa frente aos outros determinantes comumente identificados com a demanda e/ou a oferta de exportações, notadamente a taxa de câmbio real. Dar respostas a essas perguntas também é objetivo fundamental. Para atingir o que se propõe, exportações e outras variáveis candidatas a estabelecerem uma relação de co-integração são submetidas a testes cujo objetivo é verificar se essa relação é compatível com os dados disponíveis em séries de tempo. A fim de permitir a inclusão da produtividade do trabalho como variável ?explicativa? das exportações nesses dois níveis de agregação, foram usadas duas bases diferentes de dados. Câmbio real é outra variável também inclusa em ambos os níveis. Utilização da capacidade produtiva, que tradicionalmente é compreendida como uma proxy para o nível de atividade doméstica, e importações mundiais, proxy da renda externa, são adicionalmente inclusas no modelo das exportações totais. Com o conjunto de dados disponíveis, essas especificações são as que melhor permitem a inclusão da produtividade do trabalho, que, conquanto seja identificada como importante determinante das exportações, raramente é considerada nos trabalhos empíricos aplicados ao Brasil, salvo naqueles mais recentes que tomam a firma como unidade de análise. Após testes de raiz unitária (tanto convencionais como aqueles que consideram a possibilidade de uma única quebra), se processos integrados de primeira ordem forem compatíveis com os dados, são empreendidas as metodologias de Engle-Granger e de Johansen, o teste de Gregory-Hansen e, quando necessário, um procedimento para estimação do modelo vetorial de correção de erros aplicando-se um ?estimador simples de dois passos? (simple two step ? S2S). / Salvador

<p>In this paper, the forecast performance of an unrestricted Vector Autoregressive (VAR) model was compared against the forecast accuracy of a Vector error correction (VECM) model when computing out-of-sample forecasts for Swedish exports. The co-integrating relation used to estimate the error correction specification was based upon an economic theory for international trade suggesting that a long run equilibrium relation among the variables included in an export demand equation should exist. The results obtained provide evidence of a long run equilibrium relationship between the Swedish export volume and its main determinants. The models were estimated for manufactured goods using quarterly data for the period 1975-1999 and once estimated, the models were used to compute out-of-sample forecasts up to four-, eight- and twelve-quarters ahead for the Swedish export volume using both multi-step and one-step ahead forecast techniques. The main results suggest that the differences in forecasting ability between the two models are small, however according to the relevant evaluation criteria the unrestricted VAR model in general yields somewhat better forecast than the VECM model when forecasting Swedish exports over the chosen forecast horizons.</p>

The output of the National economy of the Czech Republic is conditioned by a sum of important factors. There are sectors, which increased power during the last two decades, mainly due to expansion of modern technologies and knowledge workers. One of this is Construction. Construction is specific to its position in the economy and in particular is characterized by the greatest seasonality ever. However, this is not a problem for statistical analysis, rather a benefit. Modern approaches allow us to analyze seasonal fluctuations. From selected data we are able to construct evolutionary forecasts. The work will be performed for the most important indicators in the Czech Construction. The outcome of the paper will be conditional forecasts of these indicators. It will also make analyze of the relationship between these indicators and other variables that might affected it. The work is practical application of stochastic modeling approach by Box and Jenkins, augmented by more modern approaches, such as verification of Granger causality and co-integration and testing of seasonal unit roots by Hylleberg et al.

This dissertation is composed of two parts, an integrative essay and a set of published papers. The essay and the collection of papers are placed in the context of development and application of time series econometric models in a temporal-axis from 1970s through 2005, with particular focus in the Marketing discipline. The main aim of the integrative essay is on modelling the effects of marketing actions on performance variables, such as sales and market share in competitive markets. Such research required the estimation of two kinds of time series econometric models: multivariate and multiple time series models. I use Autoregressive Integrated Moving Average (ARIMA) intervention models and the Pierce and Haugh statistical test to model the impact of a single marketing instrument, mainly price promotions, to measure own and cross-short term sales effects, and to study asymmetric marketing competition. I develop and apply Vector AutoRegressive (VAR) and Bayesian Vector AutoRegressive (BVAR) models to estimate dynamic relationships in the market and to forecast market share. Especially, BVAR models are advantageous because they contain all relevant dynamic and interactive effects. They accommodate not only classical competitive reaction effects, but also own and cross-market share brand feedback effects and internal decision rules and provided substantively useful insights into the dynamics of demand. The integrative essay is structured in four main parts. The introduction sets the basic ideas behind the published papers, with particular focus on the motivation of the essay, the types of competitive reaction effects analysed, an overview of the time series econometric models in marketing, a short discussion of the basic methodology used in the research and a brief description of the inter-relationships across the published papers and structure of the essay. The discussion is centred on how to model the effects of marketing actions at the selective demand or brand level and at the primary demand or product level. At the brand level I discuss the research contribution of my work on (i) modelling promotional short-term effects of price and non-price actions on sales and market share for consumer packaged goods, with no competition, (ii) how to measure own and cross short-term sales effects of advertising and price, in particular, cross-lead and lag effects, asymmetric sales behaviour and competition without retaliatory actions, in an automobile market, (iii) how to model the marketing-mix effectiveness at the short and long-term on market shares in a car market, (iv) what is the best method to forecast market share, and (v) the study of causal linkages at different time horizons between sales and marketing activity for a particular brand. At the product or commodity level, I propose a way to model the flows of tourists that come from different origins (countries) to the same country-destination as market segments defining the primary demand of a commodity - the product