An effective risk management program requires reliable risk measurement. Failure to assess inherited risks in mortgage-backed securities in the U.S. market contributed to the financial crisis of 2007–2008, which has prompted government regulators to pay greater attention to controlling risk in banks, investment funds, credit unions, and other financial institutions to prevent bankruptcy and financial crisis in the future. In order to calculate risk in a reliable manner, this thesis has focused on the statistical modeling of expected return and volatility. The primary aim of this study is to propose a framework, based on the probabilistic reduction approach, to reliably quantify market risk using statistical models and historical data. Particular emphasis is placed on the importance of the validity of the probabilistic assumptions in risk measurement by demonstrating how a statistically misspecified model will lead the evaluation of risk astray. The concept of market risk is explained by discussing the narrow definition of risk in a financial context and its evaluation and implications for financial management. After highlighting empirical evidence and discussing the limitations of the ARCH-GARCH-type volatility models using exchange rate and stock market data, we proposed Student's t Autoregressive models to estimate expected return and volatility to measure risk, using Value at Risk (VaR) and Expected Shortfall (ES). The misspecification testing analysis shows that our proposed models can adequately capture the chance regularities in exchange rates and stock indexes data and give a reliable estimation of regression and skedastic functions used in risk measurement. According to empirical findings, the COVID-19 pandemic in the first quarter of 2020 posed an enormous risk to global financial markets. The risk in financial markets returned to levels prior to the COVID-19 pandemic in 2021, after COVID-19 vaccine distribution started in developed countries. / Doctor of Philosophy / Reliable risk measurement is necessary for any effective risk management program. Hence, the primary purpose of this dissertation was to propose a framework to quantify market risk using statistical models and historical data, with a particular emphasis placed on checking the validity of probabilistic assumptions underlying models. After discussing the concept of market risk and its evaluation methods in financial management, we explored the empirical evidence in financial data and highlighted some limitations of other well-known modeling approaches. In order to ameliorate limitations, this study proposed Student's t Autoregressive models to estimate the conditional mean and the conditional variance of the financial variables and use them to measure risk via two popular methods: Value at Risk (VaR) and Expected Shortfall (ES). Further investigation shows that our proposed models can adequately model exchange rates and stock indexes data and give reliable estimations to use in risk measurement. We used our model to quantify risk in global financial markets in recent years. The results show that the COVID-19 pandemic posed an enormous risk to global financial markets in the first quarter of 2020. In 2021, the level of risk in financial markets returned to levels before the COVID-19 pandemic, after COVID-19 vaccine distribution started in developed countries.

Os avanços nas técnicas de neuroimagem, principalmente com o de- senvolvimento da ressonância magnética funcional (RMf), vem possibilitando um melhor compreendimento dos processos e mecanismos cerebrais. Este trabalho tem como objetivo o desenvolvimento de um modelo de conectividade dinâmico entre diversas áreas cerebrais útilzando dados de RMf. A modelagem dinâmica do fluxo de informação é realizada com a estimação dos parâmetros de um modelo autoregressivo multivariado com coeficientes variandos no tempo, baseado na projeçã o de funções em bases de ondaletas. Dessa forma, um método para estimação e a derivação de suas propriedades assintóticas são apresentados. Diversos conjuntos de simulações computacionais são realizados visando a avaliação do desempenho do método proposto. Por fim, são apresentadas aplicações do modelo de conectividade variante no tempo em dados de ressonância magnética funcional. / Advances in neuroimage technologies, mainly with the development of functional magnetic resonance imaging (fMRI), improve the comprehension of brain processes and mechanisms. The main goal of this work is the development of a time-varying connectivity model between many brain areas using fMRI datasets. The dynamic modelling of the information flow is related to the parameters estimation of a time-varying multivariate autoregressive process, based on functions projection in wavelet basis. We propose an estimation procedure and present its asymptotic properties. Computational simulations were performed focusing the evaluation of the proposed approach. Further, applications of these methodologies to real functional magnetic resonance datasets are presented.

The strong relationship between cognitive processing and coherent behaviour and neurocognitive networks justifies the current huge interest in cortical functional connectivity modeling. This has fostered the development of connectivity estimators from the classical bivariate coherence concept to the notion of multivariate partial directed coherence (PDC) which provides information about temporal dependencies exposing cause and effect relationships. This work examines PDC performance for scalp EEG data whose research value has been subject to much debate in the light of the presence of volume conduction (VC) effects that often obscure the actual nature of cortical source dynamics. Through analytical considerations and simulations we show that even though (VC) can hinder accurate connectivity estimation, one can mitigate its effects by a judicious choice of scalp electrode configuration/ground reference. This observation allows settling the connectivity estimation adequacy debate in the presence of PDC. / A forte relação que processamento cognitivo e comportamento coerente tem com redes neurocognitivas justifica o enorme interesse atual em modelamento de conectividade cortical. Este fato tem justificado o desenvolvimento de estimadores de conectividade desde a clássica coerência bivariada até a noção multivariada de coerência parcial direcionada (PDC) que exibe informação a cerca de dependências temporais que permitem expor relações de causa e efeito. O presente trabalho examina o desempenho da PDC no contexto de EEG de escalpo cujo valor em pesquisa sob os efeitos de condução de volume (VC) tem sido objeto de uma quantidade substancial de questionamentos na medida em esta obscurece a observação da dinâmica das fontes corticais. Por meio de considerações analíticas e simulações, mostramos que é possível mitigar os erros de estimação devidos à VC através da escolha judiciosa da configuração de eletrodos e da referência de terra. Esta observação permite resolver o conflito acerca da adequabilidade da inferência cortical baseada em EEG de escalpo.

This thesis contains three main parts. In the first part, we adapt a model developed for the US economy to the unique Chinese economic and institutional context. The uniqueness is mainly from two perspectives: the dual-channel housing financing system in China and the existence of the shadow banking sector (which differs from the shadow banking in developed economies) in China’s housing market. It would be difficult to obtain a clear picture of the Chinese housing market and macroeconomy without a thorough understanding of these two characteristics. This is due to the crucial role played by shadow banking and other informal finance institutions within the context of China in both the development and purchase of housing, in supporting productive economic activities in general, and that the housing market is in turn intricately connected to the health of the Chinese economy, being a key ‘barometer’. The second part of the research is the quantification of the determinants of the scale of shadow banking in China. The quantification is crucial since policy makers need to be aware of how sensitive shadow banking is to various factors. We develop a theoretical framework to explain the evolution of the scale of shadow banking in China. As part of this research, we investigate whether the real interest rate of household saving deposits, the required reserve ratio and bank loans to business and household are the main factors in explaining the evolution of China’s shadow banking. In the third part of research, we employ a credit risk and macroeconomic stress test to investigate the vulnerability of the commercial banks in China. Our originality here is the integration of both the role of shadow banking and housing market related loans in the commercial banks’ stress test scenarios at the macro level. Since a systematic analysis regarding the effect of changes in the macroeconomy and housing market on the credit risk of commercial banks in China is scarce, we use bank stress tests to analyse the credit risk in terms of the non-performing loans ratio of commercial banks in China; this is in response to changes in the macroeconomic factors and housing market. We address the role of the variation of the scale of shadow banking in China in terms of its contribution to the credit risk because of its uncertainty and close link with the commercial banks. Stress tests often focus on a single bank or financial institution yet we apply the same principles to examine the financial system as a whole in China, which would allow us to quantify the systemic risk in the entire Chinese financial system; and which variables, especially shadow banking contribute to the risks and by how much. This thesis contributes to the understanding of how China’s dual-channel housing finance system and shadow banking affect the evolution of house prices; and also, the main driving factors of the scale of China’s shadow banking and whether the housing market related loans and shadow banking pose risks to commercial banks. Possible research questions raised by the main findings of this thesis will enrich the debate on China’s housing market, shadow banking and regular banks, especially at a time when China is reforming its economic structure.

Os avanços nas técnicas de neuroimagem, principalmente com o de- senvolvimento da ressonância magnética funcional (RMf), vem possibilitando um melhor compreendimento dos processos e mecanismos cerebrais. Este trabalho tem como objetivo o desenvolvimento de um modelo de conectividade dinâmico entre diversas áreas cerebrais útilzando dados de RMf. A modelagem dinâmica do fluxo de informação é realizada com a estimação dos parâmetros de um modelo autoregressivo multivariado com coeficientes variandos no tempo, baseado na projeçã o de funções em bases de ondaletas. Dessa forma, um método para estimação e a derivação de suas propriedades assintóticas são apresentados. Diversos conjuntos de simulações computacionais são realizados visando a avaliação do desempenho do método proposto. Por fim, são apresentadas aplicações do modelo de conectividade variante no tempo em dados de ressonância magnética funcional. / Advances in neuroimage technologies, mainly with the development of functional magnetic resonance imaging (fMRI), improve the comprehension of brain processes and mechanisms. The main goal of this work is the development of a time-varying connectivity model between many brain areas using fMRI datasets. The dynamic modelling of the information flow is related to the parameters estimation of a time-varying multivariate autoregressive process, based on functions projection in wavelet basis. We propose an estimation procedure and present its asymptotic properties. Computational simulations were performed focusing the evaluation of the proposed approach. Further, applications of these methodologies to real functional magnetic resonance datasets are presented.

This thesis aims to produce short-term forecasts for the economic activity in Turkey. As a proxy for the economic activity, industrial production index is used. Univariate autoregressive distributed lag (ADL) models, vector autoregressive (VAR) models and combination forecasts method are utilized in a pseudo out-of-sample forecasting framework to obtain one-month ahead forecasts. To evaluate the models&rsquo / forecasting performances, the relative root mean square forecast error (RRMSFE) is calculated. Overall, results indicate that combining the VAR models with four endogenous variables yields the most substantial improvement in forecasting performance, relative to benchmark autoregressive (AR) model.

This study investigates the long-term and short-term dynamic relationships among the variables of stock returns and institutional investors' buy-sell difference in Taiwan's stock market for the sample periods from Jan., 2000 through May, 2009. Some econometrical methodologies are used in this study, such as unit test, vector autoregressive model, cointegration test, vector error correction model, impulse response function. The major empirical results are shown as follows: 1. Cointegration test For the sample periods, one long-term equilibrium relationship is found from the Johansen's cointegration test, significantly with 5% confidence level between stock year returns and the buy-sell difference for the foreign investment institutions, the domestic investment institutions, and the dealers. The long-term equilibrium relationship is Ry=1.65*QFII+4.28*FUND+35.22*DLR-1142.6. 2. VECM estimation (1)With the vector error correction model (VECM) being applied to the sample periods, the findings indicate that the changes of stock returns are not influenced among the short-term dynamic relationships by the changes of institutional investors' buy-sell difference, but only affected by one-period-lag of itself. (2) Among the short-term dynamic relationships, the changes of foreign investment institutions' buy-sell difference are affected by one-period-lag of institutional investors that positively affected by one-period-lag of the dealers, and inversely affected by one-period-lag of itself and one-period-lag of the domestic investment institutions. However, it is positively affected by one-period-lag of long-term equilibrium, which indicates foreign investment institutions follow positive feedback trading strategies. (3)The changes of the domestic investment institutions' buy-sell difference are only affected by one-period-lag of itself among the short-term dynamic relationships. (4)The changes of the dealers' buy-sell difference are positively affected among the short-term dynamic relationships by one-period-lag of the foreign investment institutions. As for the long-term relationships, it is affected by one-period-lag of long-term equilibrium, which also indicates the dealers follow positive feedback trading strategies. (5)The foreign investment institutions and the dealers have the mutual feedback relationship.

Bakgrund: I november 2014 beslutade Riksbanken att ta steget mot en nollränta och i februari 2015 gick Riksbanken ut med ytterligare en sänkning till -0,10 procent. På så vis fick Sverige för första gången en negativ reporänta. Enligt makroekonomisk teori ska en sänkning av reporäntan stimulera konsumtion och investeringar i ekonomin. Huruvida reporäntan och dess räntesänkningar skapar förutsättningar för företag att investera är ett aktuellt och viktigt forskningsområde. Forskningen i ämnet är tunn på den svenska marknaden och således är forskningsbidraget från denna studie av betydelse.Syfte: Syftet med studien är att undersöka och analysera hur förändringar i reporänta, makro-ekonomiska faktorer samt finansiella indikatorer påverkar investeringar hos svenska företag.Genomförande: Studien bygger på en kvantitativ metod. En Vector Autoregressive model har skapats för att redogöra hur reporäntan, de makroekonomiska faktorerna och de finansiella indikatorerna påverkar företagens investeringar. För att möjliggöra en analys av dessa effekter har impulse response functions skattats i modellen. På så vis undersöks det hur en isolerad enhetsökning i de valda variablerna påverkar företagens investeringar över flera tidsperioder. För att genomföra en mer omfattande analys skattas tre modeller där den första tar hänsyn till både makroekonomiska faktorer och finansiella indikatorer. Den andra modellen exkluderar de finansiella indikatorerna och den tredje modellen speglar reporäntans utveckling i två olika tidsperioder.Resultat: Företagens investeringar påverkas av flertalet faktorer. En enhetsökning av utlåningsräntan, växelkursen och företagens inflationsförväntningar uppvisar ett signifikant negativt samband. En enhetsökning av BNP-tillväxten visar däremot ett signifikant positivt samband. Reporäntan visar ingen direkt effekt på investeringar i de första två modellerna. Däremot uppvisar reporäntan skillnader i den tredje modellen, där ett negativt samband förekommer i den första av de två observerade tidsperioderna. / Background: The central bank of Sweden decided in November 2014 to set the repo rate close to zero. Further they decided to lower the repo rate to -0,10 percent in February 2015. In regard to this, Sweden had a negative repo rate for the first time. According to macroeconomic theory a decrease in the repo rate is performed to stimulate an economy’s investments and consumptions. Whether or not a decrease in interest rates gives greater incentives for firms to invest is a topical subject and an important field of research. In addition to this, the existing research on the Swedish market is insufficient within this field, which gives us further motives to conduct this study.Aim: The purpose of this study is to examine and analyse how changes in the repo rate, macroeconomic factors and financial indicators affects investments of Swedish firms.Completion: The study is conducted with a quantitative approach. A Vector Autoregressive model is created in order to examine the impact of changes in the repo rate, the macroeconomic factors and the financial indicators on firms’ investments. Impulse response functions are estimated to allow a further analysis of these effects. Hence, it is conceivable to examine how one isolated unit-increase in a specific variable affects firms’ investment through several time periods. Furthermore, we estimate three models, one which includes both macroeconomic variables and financial indicators and another which excludes the financial indicators. The last model reflects the repo rate’s impact on investments in two separate time periods.Result: Investments of firms are affected by numerous of factors. One unit-increase of the lending rate, the exchange rate and firms’ expectations of inflation exhibit a negative relation to investments. Furthermore, one unit-increase in GDP-growth tends to increase investments. However, the repo rate has no impact on investments in the first two models. In spite of this, evidence from the third model indicates that the repo rate has a negative impact on investments during the first period.

The dissertation is mainly made up of three empirical theses on the Optimum Currency Area theory, business cycle synchronization, and intra-industry trade. The second chapter conducts an empirical test into the theory of Optimum Currency Area. I investigate the feasibility of creating a currency union in East Asia by examining the dominance and symmetry of macroeconomic shocks. Relying on a series of structural Vector Autoregressive models with long-run and block exogeneity restrictions, I identify a variety of macroeconomic disturbances in eleven East Asian economies. To examine the nature of the disturbances, I look into the forecast error variance decomposition, correlation of disturbances, size of shocks, and speed of adjustments. Based on both statistical analysis and economic comparison, it is found that two groups of economies are subject to dominant and symmetrical domestic supply shocks, and that the two groups respond quickly to moderate-sized shocks. Therefore, it is economically feasible for the two groups of economies to foster common currency zones. The third chapter investigates the different effects of intra- and inter-industry trade on business cycle synchronization, controlling for financial market linkage and monetary policy making. The chapter is the first attempt to use intra- and inter-industry trade simultaneously in Instrument Variable estimations. The evidence in my paper is supportive that intra-industry trade increases business cycle synchronization, while inter-industry trade brings about divergence of cycles. The findings imply that country pairs with higher intra-industry trade intensity are more likely to experience synchronized business cycles and are more feasible to join a monetary union. My results also show that financial integration and monetary policy coordination provide no explanation for synchronization when industry-level trade are accounted for. The fourth chapter extends the third chapter and explores how the characteristics of global trade network influence intra-industry trade. Borrowing the concept of structural equivalence, the similarity of two countries’ aggregate trade relations with other countries, from the social network analysis, this study incorporates this measure of trade network to the augmented gravity model of intra-industry trade. I build up two fixed effects models to analyze intra-industry trade in the raw material and final product sectors among 182 countries from 1962 through 2000. Structural equivalence promotes intra-industry trade flows in the final product sector, but it does not influence intra-industry trade in the crude material sector. Moreover, structural equivalence has been increasingly important in boosting intra-industry trade over time. / Graduate / 0508

Nas últimas três décadas, o Brasil produziu mais de um milhão de mortos por homicídios, alcançando assim a triste posição de 18 país com maior taxa de mortes violentas no mundo (GENEVADECLARATIONON ARMED VIOLENCE AND DEVELOPMENT, 2011). Para solucionar tal problema, diversos esforços privados e públicos foram feitos, tendo sido o Estatuto de Desarmamento um dos esforços de maior destaque. No entanto, apesar de decorridos mais de dez anos após a promulgação desta legislação, a literatura econômica sobre o crime ainda não é unânime acerca dos efeitos das armas de fogo sobre os crimes violentos. Com a intenção de analisar estes efeitos, esta dissertação investiga as diferentes abordagens da Teoria Econômica do Crime e elabora um modelo teórico capaz de respaldar a análise empírica. Esta análise, por sua vez, avalia as relações entre armas de fogo e homicídios por perfuração de arma de fogo no Brasil e no Estado do Rio Grande do Sul, por meio de Vetores Auto Regressivos em painel. Dos resultados obtidos, conclui-se que os efeitos entre armas e homicídios variam de acordo com as heterogeneidades locais, não sendo possível extrapolar os mesmos. / Over the last three decades, Brazil produced over one million deaths by homicide.This puts Brazil in a sad position of the 18thcountry with the highest rates of violent deaths in the world (GENEVA DECLARATION ON ARMED VIOLENCE AND DEVELOPMENT, 2011). To solve this problem, many private and public efforts had been made. One effort with major prominence is the Disarmament Statute. Since the promulgation of this legislation over ten years ago,the economic literature about crimeis still not unanimous about the effectsof firearms on violent crimes. To analyze these effects, this dissertationinvestigates thedifferent approachesof the Economic Theory of Crimeand elaborateson a theoretical modelable to supportthe empirical analysis. This analysisevaluatesthe relations between firearms and homicides byfirearm perforationin Brazilandin the Rio Grande do Sul state, usingpanel Vector Autoregressives. It is concluded thatthe effectsbetweenfirearms and homicides varyaccording tolocal heterogeneities, being impossible to extrapolate this results.