Over the last years, the growing availability of large datasets and the improvements in the computational speed of computers have further fostered the research in the fields of both macroeconomic modeling and forecasting analysis. A primary focus of these research areas is to improve the models performance by exploiting the informational content of several time series. Increasing the dimension of macro models is indeed crucial for a detailed structural understanding of the economic environment, as well as for an accurate forecasting analysis. As consequence, a new generation of large-scale macro models, based on the micro-foundations of a fully specified dynamic stochastic general equilibrium set-up, has became one of the most flourishing research areas of interest both in central banks and academia. At the same time, there has been a revival of forecasting methods dealing with many predictors, such as the factor models. The central idea of factor models is to exploit co-movements among variables through a parsimonious econometric structure. Few underlying common shocks or factors explain most of the co-variations among variables. The unexplained component of series movements is on the other hand due to pure idiosyncratic dynamics. The generality of their framework allows factor models to be suitable for describing a broad variety of models in a macroeconomic and a financial context. The revival of factor models, over the recent years, comes from important developments achieved by Stock and Watson (2002) and Forni, Hallin, Lippi and Reichlin (2000). These authors find the conditions under which some data averages become collinear to the space spanned by the factors when, the cross section dimension, becomes large. Moreover, their factor specifications allow the idiosyncratic dynamics to be mildly cross-correlated (an effect referred to as the 'approximate factor structure' by Chamberlain and Rothschild, 1983), a situation empirically verified in many applications. These findings have relevant implications. The most important being that the use of a large number of series is no longer representative of a dimensional constraint. On the other hand, it does help to identify the factor space. This new generation of factor models has been applied in several areas of macroeconomics and finance as well as for policy evaluation. It is consequently very likely to become a milestone in the literature of forecasting methods using many predictors. This thesis contributes to the empirical literature on factor models by proposing four original applications. In the first chapter of this thesis, the generalized dynamic factor model of Forni et. al (2002) is employed to explore the predictive content of the asset returns in forecasting Consumer Price Index (CPI) inflation and the growth rate of Industrial Production (IP). The connection between stock markets and economic growth is well known. In the fundamental valuation of equity, the stock price is equal to the discounted future streams of expected dividends. Since the future dividends are related to future growth, a revision of prices, and hence returns, should signal movements in the future growth path. Though other important transmission channels, such as the Tobin's q theory (Tobin, 1969), the wealth effect as well as capital market imperfections, have been widely studied in this literature. I show that an aggregate index, such as the S&P500, could be misleading if used as a proxy for the informative content of the stock market as a whole. Despite the widespread wisdom of considering such index as a leading variable, only part of the assets included in the composition of the index has a leading behaviour with respect to the variables of interest. Its forecasting performance might be poor, leading to sceptical conclusions about the effectiveness of asset prices in forecasting macroeconomic variables. The main idea of the first essay is therefore to analyze the lead-lag structure of the assets composing the S&P500. The classification in leading, lagging and coincident variables is achieved by means of the cross correlation function cleaned of idiosyncratic noise and short run fluctuations. I assume that asset returns follow a factor structure. That is, they are the sum of two parts: a common part driven by few shocks common to all the assets and an idiosyncratic part, which is rather asset specific. The correlation function, computed on the common part of the series, is not affected by the assets' specific dynamics and should provide information only on the series driven by the same common factors. Once the leading series are identified, they are grouped within the economic sector they belong to. The predictive content that such aggregates have in forecasting IP growth and CPI inflation is then explored and compared with the forecasting power of the S&P500 composite index. The forecasting exercise is addressed in the following way: first, in an autoregressive (AR) model I choose the truncation lag that minimizes the Mean Square Forecast Error (MSFE) in 11 years out of sample simulations for 1, 6 and 12 steps ahead, both for the IP growth rate and the CPI inflation. Second, the S&P500 is added as an explanatory variable to the previous AR specification. I repeat the simulation exercise and find that there are very small improvements of the MSFE statistics. Third, averages of stock return leading series, in the respective sector, are added as additional explanatory variables in the benchmark regression. Remarkable improvements are achieved with respect to the benchmark specification especially for one year horizon forecast. Significant improvements are also achieved for the shorter forecast horizons, when the leading series of the technology and energy sectors are used. The second chapter of this thesis disentangles the sources of aggregate risk and measures the extent of co-movements in five European stock markets. Based on the static factor model of Stock and Watson (2002), it proposes a new method for measuring the impact of international, national and industry-specific shocks. The process of European economic and monetary integration with the advent of the EMU has been a central issue for investors and policy makers. During these years, the number of studies on the integration and linkages among European stock markets has increased enormously. Given their forward looking nature, stock prices are considered a key variable to use for establishing the developments in the economic and financial markets. Therefore, measuring the extent of co-movements between European stock markets has became, especially over the last years, one of the main concerns both for policy makers, who want to best shape their policy responses, and for investors who need to adapt their hedging strategies to the new political and economic environment. An optimal portfolio allocation strategy is based on a timely identification of the factors affecting asset returns. So far, literature dating back to Solnik (1974) identifies national factors as the main contributors to the co-variations among stock returns, with the industry factors playing a marginal role. The increasing financial and economic integration over the past years, fostered by the decline of trade barriers and a greater policy coordination, should have strongly reduced the importance of national factors and increased the importance of global determinants, such as industry determinants. However, somehow puzzling, recent studies demonstrated that countries sources are still very important and generally more important of the industry ones. This paper tries to cast some light on these conflicting results. The chapter proposes an econometric estimation strategy more flexible and suitable to disentangle and measure the impact of global and country factors. Results point to a declining influence of national determinants and to an increasing influence of the industries ones. The international influences remains the most important driving forces of excess returns. These findings overturn the results in the literature and have important implications for strategic portfolio allocation policies; they need to be revisited and adapted to the changed financial and economic scenario. The third chapter presents a new stylized fact which can be helpful for discriminating among alternative explanations of the U.S. macroeconomic stability. The main finding is that the fall in time series volatility is associated with a sizable decline, of the order of 30% on average, in the predictive accuracy of several widely used forecasting models, included the factor models proposed by Stock and Watson (2002). This pattern is not limited to the measures of inflation but also extends to several indicators of real economic activity and interest rates. The generalized fall in predictive ability after the mid-1980s is particularly pronounced for forecast horizons beyond one quarter. Furthermore, this empirical regularity is not simply specific to a single method, rather it is a common feature of all models including those used by public and private institutions. In particular, the forecasts for output and inflation of the Fed's Green book and the Survey of Professional Forecasters (SPF) are significantly more accurate than a random walk only before 1985. After this date, in contrast, the hypothesis of equal predictive ability between naive random walk forecasts and the predictions of those institutions is not rejected for all horizons, the only exception being the current quarter. The results of this chapter may also be of interest for the empirical literature on asymmetric information. Romer and Romer (2000), for instance, consider a sample ending in the early 1990s and find that the Fed produced more accurate forecasts of inflation and output compared to several commercial providers. The results imply that the informational advantage of the Fed and those private forecasters is in fact limited to the 1970s and the beginning of the 1980s. In contrast, during the last two decades no forecasting model is better than "tossing a coin" beyond the first quarter horizon, thereby implying that on average uninformed economic agents can effectively anticipate future macroeconomics developments. On the other hand, econometric models and economists' judgement are quite helpful for the forecasts over the very short horizon, that is relevant for conjunctural analysis. Moreover, the literature on forecasting methods, recently surveyed by Stock and Watson (2005), has devoted a great deal of attention towards identifying the best model for predicting inflation and output. The majority of studies however are based on full-sample periods. The main findings in the chapter reveal that most of the full sample predictability of U.S. macroeconomic series arises from the years before 1985. Long time series appear to attach a far larger weight on the earlier sub-sample, which is characterized by a larger volatility of inflation and output. Results also suggest that some caution should be used in evaluating the performance of alternative forecasting models on the basis of a pool of different sub-periods as full sample analysis are likely to miss parameter instability. The fourth chapter performs a detailed forecast comparison between the static factor model of Stock and Watson (2002) (SW) and the dynamic factor model of Forni et. al. (2005) (FHLR). It is not the first work in performing such an evaluation. Boivin and Ng (2005) focus on a very similar problem, while Stock and Watson (2005) compare the performances of a larger class of predictors. The SW and FHLR methods essentially differ in the computation of the forecast of the common component. In particular, they differ in the estimation of the factor space and in the way projections onto this space are performed. In SW, the factors are estimated by static Principal Components (PC) of the sample covariance matrix and the forecast of the common component is simply the projection of the predicted variable on the factors. FHLR propose efficiency improvements in two directions. First, they estimate the common factors based on Generalized Principal Components (GPC) in which observations are weighted according to their signal to noise ratio. Second, they impose the constraints implied by the dynamic factors structure when the variables of interest are projected on the common factors. Specifically, they take into account the leading and lagging relations across series by means of principal components in the frequency domain. This allows for an efficient aggregation of variables that may be out of phase. Whether these efficiency improvements are helpful to forecast in a finite sample is however an empirical question. Literature has not yet reached a consensus. On the one hand, Stock and Watson (2005) show that both methods perform similarly (although they focus on the weighting of the idiosyncratic and not on the dynamic restrictions), while Boivin and Ng (2005) show that SW's method largely outperforms the FHLR's and, in particular, conjecture that the dynamic restrictions implied by the method are harmful for the forecast accuracy of the model. This chapter tries to shed some new light on these conflicting results. It focuses on the Industrial Production index (IP) and the Consumer Price Index (CPI) and bases the evaluation on a simulated out-of sample forecasting exercise. The data set, borrowed from Stock and Watson (2002), consists of 146 monthly observations for the US economy. The data spans from 1959 to 1999. In order to isolate and evaluate specific characteristics of the methods, a procedure, where the two non-parametric approaches are nested in a common framework, is designed. In addition, for both versions of the factor model forecasts, the chapter studies the contribution of the idiosyncratic component to the forecast. Other non-core aspects of the model are also investigated: robustness with respect to the choice of the number of factors and variable transformations. Finally, the chapter performs a sub-sample performances of the factor based forecasts. The purpose of this exercise is to design an experiment for assessing the contribution of the core characteristics of different models to the forecasting performance and discussing auxiliary issues. Hopefully this may also serve as a guide for practitioners in the field. As in Stock and Watson (2005), results show that efficiency improvements due to the weighting of the idiosyncratic components do not lead to significant more accurate forecasts, but, in contrast to Boivin and Ng (2005), it is shown that the dynamic restrictions imposed by the procedure of Forni et al. (2005) are not harmful for predictability. The main conclusion is that the two methods have a similar performance and produce highly collinear forecasts.

Taiwan business indicators are announced by CEPD(Council For Economic Planning And Development) , and divided into three categories ¡V business monitoring indications, business expectation indicators and industrial business expectation survey. Business expectation indicators are further divided into the Composite Index of Leading Indicators and the Composite Index of Coincident Indicators. Leading indicators, which are expected to forecast business cycles, are widely used to monitor or even predict the fluctuations of economic activities. They are also used to provide early signals of economic trend and, therefore, considered as a tool to adjust the government¡¦s economic policy. CEPD use the compilation of the USA National Bureau of Economic Research as a reference for a long time, and has announced Taiwan¡¦s ex-business indicators since 1977 without making any revision in the past years, so they announced new business indicators in 2007. As we know, it is difficult to find the leading indicator to make the stable variable of predicting the business cycle, which raises doubts of whether the current leading indicators can done the work concisely. CEPD make the indicators a little bit subjective because of considering government¡¦s policy and the meaning of containing widely economic fields and the convenience of static, so this research try to examine the effect of current seven leading indicators. This thesis focuses on leading indicators to investigate how the seven components are related to the general economy. Composite Index of Leading Indicators is made up of seven indicators in order to predict the business cycle. The seven indicators include Index of export orders, Monetary aggregates, M1B, Stock prices index, Index of producer's inventory, Average monthly overtime in industry & services, Building permits, SEMI book ¡Vto¡Vbill ratio. In the purpose of getting more sample data, we take Industrial production index, one of coincident indicators announced by CEPD as the variable of current economy.

This study is aimed at exploring new process safety metrics for measuring the process safety performance in processing industries. Following a series of catastrophic incidents such as the Bhopal chemical tragedy (1984) and Phillips 66 explosion (1989), process safety became a more important subject than ever. These incidents triggered the development and promulgation of the Process Safety Management (PSM) standard in 1992. While PSM enables management to optimize their process safety programs and organizational risks, there is an emerging need to evaluate the process safety implementation across an organization through measurements. Thus, the process safety metric is applied as a powerful tool that measures safety activities, status, and performance within PSM. In this study, process safety lagging metrics were introduced to describe the contribution of process related parameters in determining the safety performance of an organization. Lagging metrics take process safety incidents as the numerator and divide it by different process-related denominators. Currently a process lagging metric (uses work hours as denominator) introduced by the Center for Chemical Process Safety (CCPS) has been used to evaluate the safety performance in processing industries. However, this lagging metric doesn't include enough process safety information. Therefore, modified denominators are proposed in this study and compared with the existing time-based denominator to validate the effectiveness and applicability of the new metrics. Each proposed metric was validated using available industry data. Statistical unitization method has converted incident rates of different ranges for the convenience of comparison. Trend line analysis was the key indication for determining the appropriateness of new metrics. Results showed that some proposed process-related metrics have the potential as alternatives, along with the time-based metric, to evaluate process safety performance within organizations.

Real estate is believed to have oscillational patterns and lags from business fluctuation. Leading indicators and certain lags of each cycle enable some degree of forecasting possibility but in the same time increase risk of irrational expectation among real estate companies. Not only the risks from fluctuation and expectation, but also risks from business operation and services should be considered in their risk management. Real estate management company may have different types and degree of risk compared to individual investors. The management of risk of this business operation is also different between sizes of firms, scope of services, geographical location, etc. We hereby examined the risk management of small real estate management companies, operating and servicing in residential property market within Zurich, Switzerland. These specific investment and geographical areas are distinctive in terms of risk exposure and solutions as they have continuously strong demand, various attractive features and distinctive behaviors. Unlike a real estate investor, the real estate development company emerging within these compelling economic attributes is believed to have very low risk. After the semi-structured interview with some executive representatives of these small firms, the results have revealed high level of risk awareness and actively participation to mitigate all possible risks, notwithstanding low level. Even without a person who is specifically responsible for risk management, risk assessment and evaluation have been done exclusively by their executives facilitated by personal contacts and associated institutions.

Bakalauro baigiamajame darbe tiriama, ar akcijų rinkos signalizavo apie ekonomikos pokyčius 2000-2012 m. laikotarpiu Baltijos šalyse. Pirmojoje darbo dalyje pateikiami teoriniai ekonominio ciklo ir jo indikatorių aspektai, analizuojamos priežastys, kodėl akcijų rinkos gali būti vadinamos ekonomikos indikatoriais bei išanalizuojami orientuojančio ryšio tarp akcijų rinkų ir ekonominio ciklo moksliniai tyrimai. Antrojoje, tiriamojoje dalyje, siekiama išsiaiškinti ar akcijų rinkos Lietuvoje, Latvijoje ir Estijoje gali būti vadinami orientuojantys ekonomikos ciklo sekos indikatoriais. Tam kad būtų ištirtas šis ryšys, buvo pasirinkti keturi makroekonominiai rodikliai (BVP, neto darbo užmokestis, mažmeninė prekyba, pramoninės produkcijos apimtis) ir grafinės analizės, Grangerio priežastingumo testo bei VAR modelio pagalba šis ryšys buvo tiriamas. Rezultatai parodė, jog tik iš dalies akcijų rinkos yra orientuojantis ekonomikos ciklo sekos indikatorius Baltijos valstybėse, nes reikšmingas ir patikimas ryšys buvo rastas tik su BVP ir pramoninės produkcijos apimtimis. / The thesis investigates if the stock markets signaled about the economic changes for the period of 2000 – 2012 in the Baltic States. In the first part of this work the theoretical overview on economic cycle and its indicator aspects are introduced. What is more, the circumstances why the stock markets can be described as economic indicators are also presented. Moreover, the scientific researches about the orientation links between the stock markets and the economic cycle are analyzed in this final work. The practical part of the work seeks to find out if the stock markets in Lithuania, Latvia and Estonia can be called leading indicators of economic cycle sequence. In order to analyze these relations, four macroeconomic indicators were chosen (GDP, net earnings, retail and volume of industrial production). With the help of graphic analysis, Granger Causality Test and VAR model this relation was analyzed. The results show that the stock markets are just partially leading indicators of economic cycle sequence in the Baltic States because the significant and reliable relation was found only with the volumes GDP and industrial production.

Submitted by Mariana Dornelles Vargas (marianadv) on 2015-03-31T19:04:12Z No. of bitstreams: 1 indicadores_antecedentes.pdf: 1667232 bytes, checksum: e9ef01f6125796d79eae31ad1c8a72ca (MD5) / Made available in DSpace on 2015-03-31T19:04:12Z (GMT). No. of bitstreams: 1 indicadores_antecedentes.pdf: 1667232 bytes, checksum: e9ef01f6125796d79eae31ad1c8a72ca (MD5) Previous issue date: 2010-09-16 / Nenhuma / Este estudo tem por objetivo construir um sistema de indicadores antecedentes compostos com freqüência mensal para a atividade econômica do estado do Rio Grande do Sul. Utilizou-se o conceito do ciclo de crescimento, baseado metodologia proposta pela OECD. A variável proxy para o nível de atividade utilizada foi a produção industrial do estado. Para a extração dos componentes cíclicos foram utilizados tanto o filtro de Hodrick-Prescott (HP) quanto filtro de Christiano-Fitzgerald (CF). Partindo de um universo de 456 séries, dez foram selecionadas para comporem os indicadores através de testes de correlação cruzada, causalidade de Granger e do algoritmo de Bry-Boschan (1971). Foram construídos indicadores de curto-prazo, indicadores de longo-prazo e um modelo misto. Os indicadores de longo-prazo se mostraram demasiadamente instáveis, tal característica indesejável foi transmitida para os indicadores mistos. Já os indicares de curto-prazo apresentaram desempenho satisfatório. / This study aims to build a monthly system of composite leading indicators for the economic activity in the state of Rio Grande do Sul. We used the concept of the growth cycle, based on the methodology proposed by the OECD. The proxy variable for the level of activity used was the industrial production of the state. For extracting cyclical components were used both the Hodrick-Prescott (HP) filter and Christiano-Fitzgerald (CF). Starting from a universe of 456 series, by testing cross-correlation, Granger causality and the using the Bry Boschan(1971) algorithm, ten series were selected to compose the indicators. We constructed short and long-term indicators and a mixed model. The long-term indicators showed to be too unstable, this undesirable trait was transmitted to the mixed indicators. The short-term indicators showed satisfactory performance.

Background: Forecasting is an instrument that the managers rely upon for their anticipations of the future. Subcontractors control their operations according to the forecast volumes provided by the telecom mobile network equipment suppliers. The information in the forecasts is however not sufficient. Purpose: The purpose of this thesis is to identify and test relevant and available market indicators for prediction of future demand fluctuations on the telecom mobile network equipment market. Realisation: During a number of interviews, factors that are driving the network equipment market were clarified. The aim of this part was to identify possible market indicators. Hypotheses were set up to test the chosen indicators. In the second part, the indicators were tested statistically. Finally, the theoretical and logical support of the results was discussed. Result: To predict future movements in network equipment demand, the market indicators should focus on the telecom mobile operators, and their ability, need, and willingness to make new investments. The market indicators proven to be of most importance after the regression analyses were long-term market interest rates and telecom corporate bond indices.

<p>Background: Forecasting is an instrument that the managers rely upon for their anticipations of the future. Subcontractors control their operations according to the forecast volumes provided by the telecom mobile network equipment suppliers. The information in the forecasts is however not sufficient. </p><p>Purpose: The purpose of this thesis is to identify and test relevant and available market indicators for prediction of future demand fluctuations on the telecom mobile network equipment market. </p><p>Realisation: During a number of interviews, factors that are driving the network equipment market were clarified. The aim of this part was to identify possible market indicators. Hypotheses were set up to test the chosen indicators. In the second part, the indicators were tested statistically. Finally, the theoretical and logical support of the results was discussed. </p><p>Result: To predict future movements in network equipment demand, the market indicators should focus on the telecom mobile operators, and their ability, need, and willingness to make new investments. The market indicators proven to be of most importance after the regression analyses were long-term market interest rates and telecom corporate bond indices.</p>

Made available in DSpace on 2016-12-23T14:00:38Z (GMT). No. of bitstreams: 1 FOLHA DE ROSTO A SUMARIO.pdf: 114961 bytes, checksum: 8ff6dbc0edc8b0f0942b2113ba85fdfd (MD5) Previous issue date: 2008-05-01 / Leading indicators are a method based on the examination of a cycle as an empirical phenomenon; the concept of business cycles appeared in middle of the 20th century by Wesley Mitchell and Arthur Burns. It is one technique that searchs to anticipate the behavior of one given serie (reference). Diverse methods exist for its construction. In this study it is considered a construction of an indicator for Produção Industrial Brasileira (Brazilian Industrial Production) with the objective of calculating the probabilities of the turning point. For this, the series have been filtered and standardized. The Granger criterion gave basis for the selection. A balance with cross correlograms of the chosen sequencies (series) occurs for the composition of the indicator and for the calculation of the probability of reversion the methodology of Neftçi (1982) was used. Around 290 sequencies (series) were analyzed and only 9 were selected for the construction of the indicator. Some samples periods of time have been studied from January of 1992 to December of 2006 for the calculation of the probabilities, these also have been calculated by another method (probit) to compare with the methodology of Neftçi. The results demonstrate the great utility of this tool for forecasting of the cyclical movements of economic series. It is a technique that can be used to help with public politics and private policy making. / Indicadores antecedentes é um método baseado no exame do ciclo como um fenômeno empírico; surgiu do conceito de ciclos de negócio em meados do século XX por Wesley Mitchell e Arthur Burns. É uma técnica que busca antecipar o comportamento cíclico de uma dada série (referência). Existem diversos métodos para sua construção. Neste estudo propõe-se a construção de um indicador para a Produção Industrial Brasileira com o objetivo de calcular as probabilidades de mudança de fase ou reversão dos ciclos (turning point). Para isto as séries foram filtradas e padronizadas. A seleção se deu por um critério de causação (Causalidade de Granger). Para a composição do indicador, foi realizada uma ponderação com a correlação cruzada das séries escolhidas devidamente defasadas e, por fim, para o cálculo da probabilidade de reversão foi utilizada a metodologia de Neftçi (1982). Analisou-se cerca de 290 séries e somente nove foram selecionadas para a construção deste indicador. Estudaram-se alguns períodos amostrais, entre 01/1992 a 12/2006, para o cálculo das probabilidades, estas também foram calculadas por outro método (probit) para confronto com a metodologia de Neftçi. Os resultados demonstram a grande utilidade de tal ferramenta para previsão dos movimentos cíclicos de séries econômicas. É uma técnica que pode ser utilizada como orientação para políticas públicas e decisões privadas.

Magister Commercii - MCom / The drastic decline in stock prices on the 24th October 1929 sent a frantic wave of panic across the US. Merely a century later, on the 29th September 2008 another financial crisis hit the globe - this time leaving most countries devastated. The main objective of this study is twofold: 1) to determine whether leading indicators have sufficient predictive capacity to predict global financial crises; and 2) to use the Efficient Market Theory (EMT) and/ or Behavioural Finance Theory (BFT) as a means of developing a theory explaining the potential impact bad public announcements had on the level of investor confidence before the 1929 Great Depression and the 2008 Global Financial Crisis. This study was not only designed to qualitatively conceptualise the notion of the term “investor confidence” whilst drawing special attention to its frailty using the 1929 Great Depression and the 2008 Global Financial Crisis, but also assist governments, reserve banks and key institutions to develop effective strategies of mitigating the effects of the latter financial crisis as well as provide guidance on how another financial crisis can be prevented. This study extracted bad public announcements from 40 books and 60 journal articles using 6 NBER-based leading economic indicators (LEI) and 4 systematic risk-based leading non-economic indicators (LNEI) in order to: 1) qualitatively assess the extent to which leading indicators can be used to predict global financial crises 3 – 8 months in advance; and 2) use the EMT and/ or BFT to provide an explanation concerning the potential impact that bad public announcements had on the level of investor confidence before the 1929 Great Depression and the 2008 Global Financial Crisis.