This master’s thesis is focusing on compensation of reactive power, especially on creating demonstrative model of static var compensation unit (SVC). Main topic of thesis is to apply this device for fast balancing dynamic conversions of recieved reactive power. In theoretical part is described suitable method for determination of instantaneous power. Next, there is basic description of all means used for compensation of reactive power and detailed description of the SVC compensator. Practical part includes design of compensation unit and control program in LabVIEW. The pq theory is implemented for detection instantaneous power. The results of validating functionality of compensator are presented in the last part of thesis.

This thesis applies time-varying parameter vector autoregression (TVP-VAR) model with stochastic volatility to assess the effectiveness of quantitative easing in time for the Bank of Japan, the European Central Bank, the Bank of England and the Federal Reserve System between the global financial crisis and COVID-19 pandemic. We find pronounced and statistically significant response of GDP and level of implied stock market volatility to a QE shock whereas the response of CPI is feeble and statistically insignificant. We argue that this does not necessarily imply that there is no effect of QE on CPI but rather that our model was not able to detect it. We believe that this may be due to inflation expectations channel which our model did not account for. This can be reassessed with a TVP-FAVAR model which is more suitable for such an analysis as it can encompass a larger set of variables. Moreover, apart from the US, we report increasing effectiveness of QE in time. This is opposed by the researchers who believe that QE has rather decreasing effectiveness in time because it is more efficient during economic distress and then its efficiency tends to decrease during normal times. We explain this deviation by citing other unconventional monetary tools such as credit easing, forward guidance or negative...

[pt] O objetivo deste estudo consiste em analisar o efeito das condições financeiras sobre a atividade econômica e entender o papel destas na transmissão da política monetária. Nossas evidências sugerem que, no caso dos Estados Unidos, um aperto nas condições financeiras possui efeito bastante similar a um choque contracionista de juros. Apesar disso, ao controlarmos a taxa de juros para o FCI, os resultados indicam que inovações de política monetária não atuam através das condições financeiras. Tal conclusão, no entanto, não pode ser generalizada para outros países. Encontramos que, no caso do Canadá, as condições financeiras possuem relevância na transmissão da política monetária, uma vez que o efeito de um choque de juro sobre a produção industrial e a inflação após o controle para as condições financeiras, é praticamente nulo. / [en] The aim of this study is to analyze the effect of financial conditions on economic activity and to understand their role in the transmission of monetary policy. Our evidence suggests that, in the case of the United States, a tightening of financial conditions has an effect very similar to a contractionary interest rate shock. Despite this, when controlling the interest rate for the FCI, the results indicate that monetary policy innovations does not operate through financial conditions. Such a conclusion, however, can t be generalized to other countries. We find that, in the case of Canada, financial conditions are relevant to the transmission of monetary policy, since the effect of an interest rate shock on industrial production and inflation after controlling for financial conditions, is practically nil.

[pt] Este estudo buscou analisar a relação dinâmica entre três variáveis macroeconômicas no Brasil: Taxa de juros, Taxa de câmbio e Índice Ibovespa. A primeira variável analisada foi a taxa de juros, utilizando-se os preços de contratos futuros de taxas CDI negociados na BMeF Bovespa com vencimento em 360 dias e levantados através da plataforma Bloomberg. A segunda variável analisada foi a taxa de câmbio Real/Dolar real histórica fornecida pelo Banco Central do Brasil. A terceira variável analisada foi o Índice Ibovespa levantado pelo programa Economática. Para este estudo, foram coletados dados diários para as três variáveis do período de 04 de janeiro de 1999 a 04 de setembro de 2015 com o objetivo de se estudar a relação dinâmica entre as variáveis e a existência da memória longa e análise dos choques de volatilidade através da aplicação de modelos econométricos. O modelo VAR foi aplicado com 13 lags para a análise da relação dinâmica com o objetivo de estudar o poder explanatório entre as três variáveis. O modelo FIPARCH foi aplicado para testar a existência da memória longa e analisar os impactos dos choques de volatilidade nas variáveis. Os resultados foram significativos e mostraram um maior poder explanatório da taxa de câmbio sobre as demais variáveis, a existência da memória longa e que a volatilidade condicional é mais afetada por choques positivos para o índice Ibovespa e mais afetada por choques negativos para a taxa de câmbio e para a taxa de juros. Este estudo é importante para que profissionais de empresas e do governo planejem suas ações de curto e longo prazo para controle e planejamento da economia e para contribuir com os demais estudos sobre este tema. / [en] This study investigates the dynamic relationship between three macroeconomic variables in Brazil: Interest rate, exchange rate and Ibovespa index. The first variable analyzed was the interest rate, using the prices of futures contracts CDI traded on the BMeF Bovespa maturing in 360 days and raised through the Bloomberg platform. The second variable analyzed was the exchange rate real/dolar real historic provided by the Central Bank of Brazil. The third variable analyzed was the Ibovespa index raised by Economática program. For this study were collected daily data for the three variables between January 4,1999 to September 4, 2015 in order to study the dynamic relationship between the variables and the existence of long-term memory and analysis of shocks volatility by applying econometric models. The VAR model was applied with 13 lags for the analysis of the dynamics related to study the explanatory power between the three variables. The FIPARCH model was applied to test the existence of long memory and analyze the impacts of volatility shocks in the variables. The results were significant and showed a greater explanatory power of the exchange rate on the remaining variables, the existence of long memory and that the conditional volatility is more affected by positive shocks to the Ibovespa index and more affected by negative shocks to the exchange rate and the interest rate. This study is important for professionals in business and government to plan their short and long term actions to control and planning of the economy and to contribute for others studies of this topic.

Submitted by Sara Ribeiro (sara.ribeiro@ucb.br) on 2018-06-18T18:53:22Z No. of bitstreams: 1 RafaeldeMoraisVargasDissertacao2018.pdf: 2179808 bytes, checksum: e2993cd35f13b4bd6411d626aefa0043 (MD5) / Approved for entry into archive by Sara Ribeiro (sara.ribeiro@ucb.br) on 2018-06-18T18:54:14Z (GMT) No. of bitstreams: 1 RafaeldeMoraisVargasDissertacao2018.pdf: 2179808 bytes, checksum: e2993cd35f13b4bd6411d626aefa0043 (MD5) / Made available in DSpace on 2018-06-18T18:54:14Z (GMT). No. of bitstreams: 1 RafaeldeMoraisVargasDissertacao2018.pdf: 2179808 bytes, checksum: e2993cd35f13b4bd6411d626aefa0043 (MD5) Previous issue date: 2018-02-27 / Given the importance of market risk measures, such as value at risk (VaR), in this paper, we compare traditionally accepted volatility forecast models, in particular, the GARCH family models, with more recent models such as HAR-RV and GAS in terms of the accuracy of their VaR forecasts. For this purpose, we use intraday prices, at the 5-minute frequency, of the S&P 500 index and the General Electric stocks, for the period from January 4, 2010 to December 30, 2013. Based on the tick loss function and the Diebold-Mariano test, we did not find difference in the predictive performance of the HAR-RV and GAS models in comparison with the Exponential GARCH (EGARCH) model, considering daily VaR forecasts at the 1% and 5% significance levels for the return series of the S&P 500 index. Regarding the return series of General Electric, the 1% VaR forecasts obtained from the HAR-RV models, assuming a t-Student distribution for the daily returns, are more accurate than the forecasts of the EGARCH model. In the case of the 5% VaR forecasts, all variations of the HAR-RV model perform better than the EGARCH. Our empirical study provides evidence of the good performance of HAR-RV models in forecasting value at risk. / Dada a importância de medidas de risco de mercado, como o valor em risco (VaR), nesse trabalho, comparamos modelos de previsão de volatilidade tradicionalmente mais aceitos, em particular, os modelos da família GARCH, com modelos mais recentes, como o HAR-RV e o GAS, em termos da acurácia de suas previsões de VaR. Para isso, usamos preços intradiários, na frequência de 5 minutos, do índice S&P 500 e das ações da General Electric, para o período de 4 de janeiro de 2010 a 30 de dezembro de 2013. Com base na função perda tick e no teste de Diebold-Mariano, não encontramos diferença no desempenho preditivo dos modelos HAR-RV e GAS em relação ao modelo Exponential GARCH (EGARCH), considerando as previsões de VaR diário a 1% e 5% de significância para a série de retornos do índice S&P 500. Já com relação à série de retornos da General Electric, as previsões de VaR a 1% obtidas a partir dos modelos HAR-RV, assumindo uma distribuição t-Student para os retornos diários, mostram-se mais acuradas do que as previsões do modelo EGARCH. No caso das previsões de VaR a 5%, todas as variações do modelo HAR-RV apresentam desempenho superior ao EGARCH. Nosso estudo empírico traz evidências do bom desempenho dos modelos HAR-RV na previsão de valor em risco.

The financial crisis of 2007-2008 highlighted the societal impacts of bank risk-taking. A strong focus on maximizing profits for shareholders combined with a disregard for, and  underestimation of risks led to the downfall of large banks such as Lehman Brothers and multiple other banks getting bailed out by several governments and other banks. The financial crisis spread and impacted all major financial markets across the globe, which highlights the importance of investigating the banking sector from a global perspective. In addition, the influences of corporate social responsibility (CSR) on financial performance and risk have been a growing topic in research as well as in practice. Most banks today invest large amounts of money in CSR activities. The question of how bank spending in CSR activities impacts market risk is important. There are two contradicting views on CSR activities and market risk. The risk mitigation view suggests that banks that focus on stakeholder satisfaction have lower risk due to increased moral capital with stakeholders. The overinvestment view suggests that managers waste scarce resources by overinvesting in CSR activities to further selfish goals and therefore increase risk. This study examines the relationship between sustainability performance (ESG Combined score) and market risk (VaR/CVaR) using a deductive approach. The authors sample 159 banks from 39 countries and all 7 economic regions from 2011-2022. Data is used for testing hypotheses. Results find high ESG Combined Scores are associated with lower VaR/CVaR and results are robust to modifications in VaR/CVaR calculation assumptions. Disaggregation of ESG pillars shows that social pillar scores decrease VaR/CVaR in banks while environmental and governance pillars are insignificant. Results primarily lend support to the risk mitigation view and stakeholder theory stating that firms should focus more on stakeholder satisfaction than maximizing shareholder value. Complementing theories such as legitimacy theory and resources-based view are also considered important theories for explaining the results.

貨幣政策能否影響實質的經濟活動一直以來都是總體經濟學家討論的主要課題，而欲了解此一課題，便須深入了解貨幣政策影響總體經濟的傳導過程。其中最被人廣為探討的為利率管道與信用管道，而一般而言信用管道可分成兩種基本途徑，分別為銀行借貸管道與資產負債表管道。本文將重點放於銀行借貸管道的探討。 在討論金融因素與經濟體系的互動關係時，既有文獻大多是根據金融加速理論 (financial accelerator theory) 加以探討，該理論認為當實質經濟體系產生干擾時會經由金融體系而使該干擾對經濟的衝擊效果加以放大與加速。因此本文在研究信用管道在我國經濟體系中所扮演角色時，我們採用結構向量自我迴歸模型(Structural Vector Autoregression Model) ，將信用因素作為影響實質經濟體系的干擾來源之一，並分析在1997年亞洲金融危機爆發前後，信用管道與實質經濟之關聯性是否有所不同。另外由於我國過去幾年信用受限制的對象大多是較不具競爭力的傳統產業，或財務出現警訊的企業體，信用管道對經濟體系的影響可能因個人或家計單位，以及民營企業而有所差異。因此本文在討論以本國一般銀行放款所代表的信用變動對經濟體系之影響時，我們將之區分為本國一般銀行對個人與家計單位放款，以及本國一般銀行對民營企業放款兩種不同性質的放款。希望藉由以上的分析，能提供貨幣主管單位一個參考以及喚起讀者對貨幣傳導機制的重視與興趣。

This diploma thesis is devoted to the linkages among stock, bond and foreign exchange markets in the Czech Republic, Austria, Germany and Poland during the period from the beginning of the year 2007 to the end of the year 2014. In order to complexly describe the interconnections among the markets, we utilized two kinds of spillover indices (from the generalized and structural VAR model), dynamic correlation coefficients obtained from the multivariate GARCH model and contemporaneous coefficients from the structural VAR model that was identified through heteroskedasticity in structural shocks. These methods enabled us to describe the linkages among the markets from different angles, to capture their time evolution and to obtain a notion about the transmission mechanism among these markets in Central Europe. The results, inter alia, indicate an intensifying interconnection among the markets during crisis periods, lowering impact of stock markets, increasing influence of bonds and a dominant role of German bonds and Austrian stocks. At the same time, we were able to capture the influence of the European sovereign debt crisis on the spillovers and on the intensity of linkages among the markets. We showed that the intensity of linkages among bond markets relented, probably as a result of higher emphasis on the...

Dans les études géophysiques, l'assimilation de données a pour but d'estimer l'état d'un système ou les paramètres d'un modèle physique de façon optimale. Pour ce faire, l'assimilation de données a besoin de trois types d'informations : des observations, un modèle physique/numérique et une description statistique de l'incertitude associée aux paramètres du système. Dans ma thèse, de nouvelles méthodes d'assimilation de données sont utilisées pour l'étude de la physico-chimie de l'atmosphère: (i) On y utilise de manière conjointe la méthode 4D-Var avec un modèle sous-maille statistique pour tenir compte des erreurs de représentativité. (ii) Des échelles multiples sont prises en compte dans la méthode d'estimation BLUE. (iii) Enfin, la méthode du maximum de vraisemblance est appliquée pour estimer des hyper-paramètres qui paramètrisent les erreurs à priori. Ces trois approches sont appliquées de manière spécifique à des problèmes de modélisation inverse des sources de polluant atmosphérique. Dans une première partie, la modélisation inverse est utilisée afin d'estimer les émissions de monoxyde de carbone sur un domaine représentant la France. Les stations du réseau d'observation considérées sont impactées par les erreurs de représentativité. Un modèle statistique sous-maille est introduit. Il est couplé au système 4D-Var afin de réduire les erreurs de représentativité. En particulier, les résultats de la modélisation inverse montrent que la méthode 4D-Var seule n'est pas adaptée pour gérer le problème de représentativité. Le système d'assimilation des données couplé conduit à une meilleure représentation de la variabilité de la concentration de CO avec une amélioration très significative des indicateurs statistiques. Dans une deuxième partie, on évalue le potentiel du réseau IMS (International Monitoring System) du CTBTO pour l'inversion d'une source accidentelle de radionucléides. Pour évaluer la performance du réseau, une grille multi-échelle adaptative pour l'espace de contrôle est optimisée selon un critère basé sur les degrés de liberté du signal (DFS). Les résultats montrent que plusieurs régions restent sous-observées par le réseau IMS. Dans la troisième et dernière partie, sont estimés les émissions de Composés Organiques Volatils (COVs) sur l'Europe de l'ouest. Cette étude d'inversion est faite sur la base des observations de 14 COVs extraites du réseau EMEP. L'évaluation des incertitudes des valeurs des inventaires d'émission et des erreurs d'observation sont faites selon le principe du maximum de vraisemblance. La distribution des inventaires d'émission a été supposée tantôt gaussienne et tantôt semi-normale. Ces deux hypothèses sont appliquées pour inverser le champs des inventaires d'émission. Les résultats de ces deux approches sont comparés. Bien que la correction apportée sur les inventaires est plus forte avec l'hypothèse Gaussienne que semi-normale, les indicateurs statistiques montrent que l'hypothèse de la distribution semi-normale donne de meilleurs résultats de concentrations que celle Gaussienne. / Data assimilation in geophysical sciences aims at optimally estimating the state of the system or some parameters of the system's physical model. To do so, data assimilation needs three types of information: observations and background information, a physical/numerical model, and some statistical description that prescribes uncertainties to each componenent of the system.In my dissertation, new methodologies of data assimilation are used in atmospheric chemistry and physics: the joint use of a 4D-Var with a subgrid statistical model to consistently account for representativeness errors, accounting for multiple scale in the BLUE estimation principle, and a better estimation of prior errors using objective estimation of hyperparameters. These three approaches will be specifically applied to inverse modelling problems focussing on the emission fields of tracers or pollutants. First, in order to estimate the emission inventories of carbon monoxide over France, in-situ stations which are impacted by the representativeness errors are used. A subgrid model is introduced and coupled with a 4D-Var to reduce the representativeness error. Indeed, the results of inverse modelling showed that the 4D-Var routine was not fit to handle the representativeness issues. The coupled data assimilation system led to a much better representation of theCO concentration variability, with a significant improvement of statistical indicators, and more consistent estimation of the CO emission inventory. Second, the evaluation of the potential of the IMS (International Monitoring System) radionuclide network is performed for the inversion of an accidental source. In order to assess the performance of the global network, a multiscale adaptive grid is optimised using a criterion based on degrees of freedom for the signal (DFS). The results show that several specific regions remain poorly observed by the IMS network. Finally, the inversion of the surface fluxes of Volatile Organic Compounds (VOC) are carried out over Western Europe using EMEP stations. The uncertainties of the background values of the emissions, as well as the covariance matrix of the observation errors, are estimated according to the maximum likelihood principle. The prior probability density function of the control parameters is chosen to be Gaussian or semi-normal distributed. Grid-size emission inventories are inverted under these two statistical assumptions. The two kinds of approaches are compared. With the Gaussian assumption, the departure between the posterior and the prior emission inventories is higher than when using the semi-normal assumption, but that method does not provide better scores than the semi-normal in a forecast experiment.

Depuis quelques années, la recherche financière s'inscrit dans une nouvelle dynamique. La nécessité de mieux modéliser le comportement des rendements des actifs financiers et les risques sur les marchés pousse les chercheurs à trouver des mesures de risque plus adéquates. Ce travail de recherche se situe dans cette évolution, ayant admis les caractéristiques des séries financières par des faits stylisés tels que la non normalité des rendements. A travers cette thèse nous essayons de montrer l'importance d'intégrer des mesures de risque qui tiennent compte de la non normalité dans le processus d'évaluation et d'allocation des actifs financiers sur le marché français. Cette thèse propose trois chapitres correspondant chacun à un article de recherche académique. Le premier article propose de revisiter les modèles d'évaluation en prenant en compte des moments d'ordres supérieurs dans un cadre de downside risk. Les résultats indiquent que les downside co-moments d'ordres supérieurs sont déterminants dans l'explication des variations des rendements en coupe transversale. Le second chapitre propose de mettre en relation la rentabilité financière et le risque mesuré par la VaR ou la CVaR. Nous trouvons que la VaR présente un pouvoir explicatif plus élevé que celui de la CVaR et que l'approche normale est plus intéressante que l'approche basée sur l'expansion de Cornish-Fisher (1937). Ces deux résultats contredisent les prédictions théoriques mais nous avons pu démontrer qu'ils sont inhérents au marché français. Le troisième chapitre propose une autre piste, nous revisitons le modèle moyenne-CVaR dans un cadre dynamique et en présence des coûts de transaction / This dissertation is part of an ongoing researches looking for an adequate model that apprehend the behavior of financial asset returns. Through this research, we propose to analyze the relevance of risk measures that take into account the non-normality in the asset pricing and portfolio allocation models on the French market. This dissertation is comprised of three articles. The first one proposes to revisit the asset pricing model taking into account the higher-order moments in a downside framework. The results indicate that the downside higher order co-moments are relevant in explaining the cross sectional variations of returns. The second paper examines the relation between expected returns and the VaR or CVaR. A cross sectional analysis provides evidence that VaR is superior measure of risk when compared to the CVaR. We find also that the normal estimation approach gives better results than the approach based on the expansion of Cornish-Fisher (1937). Both results contradict the theoretical predictions but we proved that they are inherent to the French market. In the third paper, we review the mean-CVaR model in a dynamic framework and we take into account the transaction costs. The results indicate that the asset allocation model that takes into account the non-normality can improve the performance of the portfolio comparing to the mean-variance model, in terms of the average return and the return-to CVaR ratio. Through these three studies, we think that it is possible to modify the risk management framework to apprehend in a better way the risk of loss associated to the non-normality problem