<p>The purpose of this paper is to investigate the predictive power of the variables advanced notice of dismissal (layoffs) and vacancies for the unemployment rate. Based on the Box Jenkins Methodology, the paper makes use of Granger causality and out-of-sample tests to compare the forecast performance of a naïve reference model and the two models extended to include either lagged values of layoffs or vacancies. It is shown that layoffs make up a significant leading variable, exhibiting particularly strong predictive power at forecast horizons of 2-6 months. It is also shown that the predictive power of vacancies is more ambiguous. Vacancies constitute a valuable explanatory variable for the unemployment rate, but does not possess the same leading, predictive qualities as layoffs.</p>

The purpose of this paper is to investigate the predictive power of the variables advanced notice of dismissal (layoffs) and vacancies for the unemployment rate. Based on the Box Jenkins Methodology, the paper makes use of Granger causality and out-of-sample tests to compare the forecast performance of a naïve reference model and the two models extended to include either lagged values of layoffs or vacancies. It is shown that layoffs make up a significant leading variable, exhibiting particularly strong predictive power at forecast horizons of 2-6 months. It is also shown that the predictive power of vacancies is more ambiguous. Vacancies constitute a valuable explanatory variable for the unemployment rate, but does not possess the same leading, predictive qualities as layoffs.

This thesis contributes via the concept of efficiency in four distinct fields of the fi nancial economics and banking literature: technological heterogeneity, liquidity creation, profitability, and stability of banks. In Chapter 1 we motivate the analysis by presenting the main developments that have been taking place in the banking sector as far as these four elds are concerned and highlight their importance to the appropriate functioning of the nancial system and of the economy overall. In Chapter 2 we address the issue that conventional surveys on bank efficiency draw conclusions based on the assumption that all banks in a sample use the same production technology. However, efficiency estimates can be severely distorted if the existence of unobserved differences in technological regimes is not taken into consideration. We estimate the unobserved heterogeneity in banking technologies using a latent class stochastic frontier model. In order to arrive at a policy implication that is valid across time and markets, we present two applications of the model using separately data from the UK and Greek banking sector over the periods 1987-2011 and 1993-2011 respectively. To increase the precision of our inferences, we adopt two distinct empirical methodologies: a panel data method and a pooled cross-section modelling strategy. Our results reveal that bank-heterogeneity in both banking sectors can be controlled for two technological regimes. We find a trade-o¤ between the level of sophistication within a fi nancial system and its level of aggregate efficiency. Consistency among the results is established under both methodologies. Further, we propose a methodology with regard to M&As activity of UK and Greek banks within a latent class context. We examine numerous potential M&A scenarios among banks that belong to different technological regimes, and we test whether there is a transition of the new banks to a more efficient technological class resulting from this M&A activity. We find strong evidence that new financial institutions can be better equipped to withstand potential adverse economic conditions. Finally, we cast doubt on what the true motivation for M&A activity is and we extract important policy inferences in terms of social welfare. In Chapter 3 we introduce the "Cost Efficiency - Liquidity Creation Hypothesis" (CELCH) according to which a rise in a bank s cost efficiency level increases its level of liquidity creation. By employing a novel stress test scenario under a PVAR methodology, we test the CELCH and the direction of causality among liquidity creation and cost efficiency variables in the UK and Greek banking sector. Moreover using new measures of liquidity creation (Berger and Bouwman, 2009) we address the question of whether potential M&As can enhance liquidity creation and create additional credit channels in the economy. We evaluate and compare the robustness of potential consolidation scenarios by employing half - life measures (Chortareas and Kapetanios 2013). We show a positive impact of cost efficiency on liquidity creation in line with CELCH. The empirical evidence further suggests that potential consolidation activity can enhance the ow of credit in the economy. Bank shocks seem to be the most persistent on both liquidity creation and cost efficiency and the UK banking system is found to withstand more effectively adverse economic conditions. Finally, we cast doubts on the strategy followed by policy authorities regarding the recent wave of M&As in the Greek banking sector. In Chapter 4, we attempt to shed light on the trade-o¤ between fi nancial stability and efficiency. We highlight that current tests of banking efficiency do not take into account whether banks managers are taking too much or too little risk relative to the value maximising amount. We assume that moving from an intermediary bank type balance sheet to an investment bank type not only changes the risk-return combination of the balance sheet but also increases the banks degree of instability, that is the probability of insolvency when adverse effects occur. To this extent, we propose a new efficiency measure which incorporates all the aforementioned ambiguous points. An empirical investigation of US commercial banks between 2003-2012 suggests that our proposed risk-adjusted index has superior explanatory power with respect to banks profi tability and gives better predictions compared to conventional banking efficiency measures. This holds after various robustness checks. Chapter 5 summarizes the main findings of all three distinct studies and concludes by highlighting the importance and the contributing points of the thesis in the banking and financial economics literature.

No description available.

Organizations responsible for managing commercial fisheries conduct annual stock assessments to monitor stock and, in principle, reduce the risk of overexploitation. These are fundamental to setting the total allowable catch for the upcoming fishing year. While there have been many attempts to estimate uncertainty associated with certain components and estimates of stock assessments, to date there has been no systematic assessment of their forecasted predicted value. Using data from annual stock assessments from 65 commercial fisheries around the world, estimates were obtained of both predicted (from the previous year) and observed (in the current year) catch-at-age. When comparing observed (actual) and predicted catch-at-age for a given stock, estimates were obtained of the predictive power of next-season forecasts. Using other attributes of the fishery and the stock (biological (e.g. life history) and management (e.g. assessment model employed)), empirical models were constructed that attempt to determine variability in predictive power among stocks. It was observed that, on average, within-year predictive powers (age-series within time samples) were higher than year over year predictive powers (time-series within age samples). While focusing on time-series within age, it was observed that change over the period of record (in natural mortality rate, assessment model employed, etc.) reduced predictive power; while for age-series within time, it was shown that cumulative landings reduced predictive power. This study represents one of the first attempts to quantify systematically the predictive power of fisheries stock assessment models.

This study examines whether the implied volatility index can provide further information in forecasting volatility than historical volatility using GARCHfamily models. For this purpose, this researchhas been conducted to forecast volatility in two main markets the United States of America through its wildly used Standard and Poor’s 500 index and its correspondingvolatility index VIX and in Europe through its Euro Stoxx 50 and its correspondingvolatility index VSTOXX. To evaluate the in-sample content of information, the conditional variance equations of GARCH(1,1) and EGARCH (1,1) are supplemented by integrating implied volatility as an explanatory variable. The realized volatility has been generated from daily squared returns and was employed as a proxy for true volatility. To examine the out-of-sample forecast performance, one-day-ahead rolling forecasts have been generated, and Mincer–Zarnowitz regression and encompassing regression has been utilized. The predictive power of implied volatility has been assessed based on Mean Square Error (MSE). Findings suggest that the integration of implied volatility as an exogenous variable in the conditional variance of GARCHmodels enhancesthe fitness of modelsand decreasesvolatility persistency. Furthermore, the significance of the implied volatility coefficient suggests that implied volatility includes pertinent information in illuminating the variation of the conditional variance. Implied volatility is found to be a biased forecast of realized volatility. Empirical findings of encompassingregression testsimply that the implied volatility index does not surpass historical volatility in terms of forecasting future realized volatility.

Este trabalho apresenta um estudo inicial da aplicação de medidas de previsibilidade em sinais de voz. O objetivo é desenvolver métodos que sejam capazes de diferenciar sinais saudáveis e patológicos, inclusive separar patologias. Para isso, tenta-se medir a variação da incerteza e previsibilidade entre os sinais de voz dos grupos analisados. Algumas patologias de laringe, como nódulo e edema de Reinke, usadas neste estudo, causam modificações nos sinais de voz devido a mudanças na estrutura e funcionalidade do trato e pregas vocais. Nos casos patológicos, tem-se, principalmente, aumento de perturbações de freqüência e amplitude, adição de ruído e supressão de componentes harmônicos de alta freqüência da voz. Por causa disso, observa-se perda da estrutura quase-periódica dos sinais, aumentando-se a incerteza do sistema vocal e, portanto, diminuição de sua previsibilidade. Para avaliar essas mudanças, usam-se medidas de entropia de Shannon e entropia relativa entre os sinais saudáveis e patológicos. Além dessas, tem-se a potência de previsão (PP), a qual é uma medida baseada na entropia relativa entre o sinal de voz e seu erro de previsão obtido por um modelo. Inicialmente, optou-se pelo modelo autorregressivo (AR), consagrado em análise de voz, porém, devido a resultados não satisfatórios, apresentou-se um modelo baseado em decomposições por wavelets. Outra ferramenta utilizada foi a chamada análise de componentes previsíveis (PrCA), a qual realiza uma decomposição dos sinais em componentes ordenados por sua previsibilidade, sendo possível reconstruí-los usando somente os componentes mais previsíveis. Também, com essa técnica, analisaram-se representações tridimensionais dos sinais de voz em um espaço cujas coordenadas são dadas por versões atrasadas dos próprios sinais. Os algoritmos desenvolvidos foram testados com o auxílio de sinais de voz simulados, os quais possuíam variações de nível de ruído e perturbações de amplitude e freqüência. Com isso, foi possível detectar erros e solucionar problemas com os métodos. Após a avaliação dos algoritmos, estimou-se os valores de entropia dos sinais de voz, a entropia relativa entre os sinais saudáveis e os sinais dos grupos analisados, além de se calcular a PP usando o modelo AR e o modelo por wavelets. Por fim, utilizou-se a PrCA para obtenção de versões mais previsíveis dos sinais, então, calculando-se a PP para esses casos usando essa versão como previsão dos sinais. Aplicou-se, também, a PrCA para as representações tridimensionais dos sinais usando um modelamento AR multidimensional para obtenção de previsões. Com os ensaios de entropia dos sinais de voz, não foi possível diferenciar os grupos, mas com os resultados de entropia relativa, conseguiu-se distinguir eficientemente os sinais patológicos dos saudáveis. Porém, essa medida não possui muita aplicação prática, isso pois é necessário um banco de vozes diagnosticadas para servir de comparação. Nos ensaios de PP usando modelo AR, também não foi possível diferenciar os grupos, no entanto, com o modelo wavelet, os sinais saudáveis apresentaram significativamente maior previsibilidade do que os patológicos, mas, mesmo assim, não se conseguiu diferenciar as patologias. Com a PrCA, utilizando-se ambos os modelos, foi possível diferenciar os grupos patológicos do saudável, porém, frente ao modelo AR, os sinais saudáveis apresentaram menor previsibilidade. Isso demonstra que a previsibilidade depende do modelo usado para a análise, assim, as patologias da laringe podem diminuir ou aumentar a capacidade de previsão dos sinais de voz conforme o modelo usado. Com a avaliação dos resultados de PrCA das representações tridimensionais, tem-se comportamento semelhante ao obtido pela análise direta nos sinais de voz com o modelo AR, entretanto, essa forma e representação dos dados mostra se promissora em estudos futuros. Com esses ensaios, concluiu-se que este estudo foi muito útil para um maior conhecimento da dinâmica da produção vocal e que as medidas de previsibilidade são interessantes para avaliação de patologias da laringe, em especial, a presença de nódulo nas pregas vocais e edema de Reinke, pelo menos nestes estudos iniciais e usando os sinais de voz disponíveis. Mais estudos ainda são necessários, entretanto essa forma de análise já apresenta bons resultados, os quais podem ser aplicados para auxiliar o diagnóstico de disfonias por profissionais da saúde. / This thesis presents initial studies of the application of predictability measures to voice signal analysis. Its aim is to develop methods that are capable of differentiating healthy and pathological signals, also amongst pathologies. In order to do that, we perform an attempt to measure the uncertainty and predictability variations of the signals from the analyzed groups. Some larynx pathologies, such as nodule and Reinkes edema, that are used in this study, cause changes to the voice signals due to structure and functionality modifications of the vocal tract and folds. The main modifications are higher amplitude and frequency perturbations, noise addition, and supression of high frequency harmonic components. Because of that, the signals lose some of their almost periodic structure, the vocal system\'s uncertainty increases and, therefore, the predictability decreases. We use several measures to evaluate these changes, such as Shannons entropy and relative entropy between healthy and pathological signals. In addition, we use the predictive power (PP), that is based on the relative entropy between the voice signal and its prediction error given by a model. Firstly, we used the autoregressive model (AR), common for voice analysis, however, due to unsatisfactory results, we presented a model based on wavelet decomposition. We also took advantage of another tool, called predictable component analysis (PrCA), it performs a signal decomposition in components that are ordered by their predictability. Then it is possible to reconstruct the signals using only their most predictable components. Using this technique, we analyzed a kind of tridimensional representation of the voice signals in a space with coordinates given by delayed versions of the signals. We tested the developed algorithms with the aid of simulated voice signals, which had variations of noise level and amplitude and frequency perturbations. By means of that, it was possible to detect errors and solve method problems. After the algorithms evaluation, we estimated the entropy of the voice signals and the relative entropy between the healthy signals and all the signals. In addition, we estimated the PP using the AR and wavelet based models. After that, we used the PrCA in order to obtain more predictable versions of the signals and then, estimated the PP using this version as the signals prediction. Also, we applied the PrCA to the signals tridimensional representations using a multidimensional AR model as a predictor. Using the voice entropy results, we could not distinguish between the analyzed groups, but with the relative entropy values, the healthy and pathological signals were differentiated efficiently. In spite of that, this measure has no practical application, because a diagnosed voice database is necessary as a basis of comparison. For the PP with AR modeling, no distinction between the groups is observed, but with the wavelet modeling, the healthy signals showed significantly higher predictability than the pathological ones, however the pathologies were differentiated. Using the PrCA with both models, the pathological and healthy groups were distinguished, but for the AR model, the healthy signals presented smaller predictability. This shows that the predictability depends on the analysis model, thus the larynx pathologies can decrease or increase the prediction capacity of the voice signals according to the used model. The results of PrCA of the tridimensional representations show similar behavior of the ones from direct PrCA signal analisys with the AR model. Despite of these results, this form of data representation seems to be promising for future studies. Considering these results, we concluded that this study was very useful to acquire a better understanding of the dynamics of voice production and that the predictability measures are interesting for the evaluation of larynx pathologies, especially presence of nodule in the vocal folds and Reinke\'s edema, at least for this initial study using the available signals. More studies are still necessary, but this analysis method already presents good results, which can be applied to aid pathology diagnosis by health professionals.

Este trabalho apresenta um estudo inicial da aplicação de medidas de previsibilidade em sinais de voz. O objetivo é desenvolver métodos que sejam capazes de diferenciar sinais saudáveis e patológicos, inclusive separar patologias. Para isso, tenta-se medir a variação da incerteza e previsibilidade entre os sinais de voz dos grupos analisados. Algumas patologias de laringe, como nódulo e edema de Reinke, usadas neste estudo, causam modificações nos sinais de voz devido a mudanças na estrutura e funcionalidade do trato e pregas vocais. Nos casos patológicos, tem-se, principalmente, aumento de perturbações de freqüência e amplitude, adição de ruído e supressão de componentes harmônicos de alta freqüência da voz. Por causa disso, observa-se perda da estrutura quase-periódica dos sinais, aumentando-se a incerteza do sistema vocal e, portanto, diminuição de sua previsibilidade. Para avaliar essas mudanças, usam-se medidas de entropia de Shannon e entropia relativa entre os sinais saudáveis e patológicos. Além dessas, tem-se a potência de previsão (PP), a qual é uma medida baseada na entropia relativa entre o sinal de voz e seu erro de previsão obtido por um modelo. Inicialmente, optou-se pelo modelo autorregressivo (AR), consagrado em análise de voz, porém, devido a resultados não satisfatórios, apresentou-se um modelo baseado em decomposições por wavelets. Outra ferramenta utilizada foi a chamada análise de componentes previsíveis (PrCA), a qual realiza uma decomposição dos sinais em componentes ordenados por sua previsibilidade, sendo possível reconstruí-los usando somente os componentes mais previsíveis. Também, com essa técnica, analisaram-se representações tridimensionais dos sinais de voz em um espaço cujas coordenadas são dadas por versões atrasadas dos próprios sinais. Os algoritmos desenvolvidos foram testados com o auxílio de sinais de voz simulados, os quais possuíam variações de nível de ruído e perturbações de amplitude e freqüência. Com isso, foi possível detectar erros e solucionar problemas com os métodos. Após a avaliação dos algoritmos, estimou-se os valores de entropia dos sinais de voz, a entropia relativa entre os sinais saudáveis e os sinais dos grupos analisados, além de se calcular a PP usando o modelo AR e o modelo por wavelets. Por fim, utilizou-se a PrCA para obtenção de versões mais previsíveis dos sinais, então, calculando-se a PP para esses casos usando essa versão como previsão dos sinais. Aplicou-se, também, a PrCA para as representações tridimensionais dos sinais usando um modelamento AR multidimensional para obtenção de previsões. Com os ensaios de entropia dos sinais de voz, não foi possível diferenciar os grupos, mas com os resultados de entropia relativa, conseguiu-se distinguir eficientemente os sinais patológicos dos saudáveis. Porém, essa medida não possui muita aplicação prática, isso pois é necessário um banco de vozes diagnosticadas para servir de comparação. Nos ensaios de PP usando modelo AR, também não foi possível diferenciar os grupos, no entanto, com o modelo wavelet, os sinais saudáveis apresentaram significativamente maior previsibilidade do que os patológicos, mas, mesmo assim, não se conseguiu diferenciar as patologias. Com a PrCA, utilizando-se ambos os modelos, foi possível diferenciar os grupos patológicos do saudável, porém, frente ao modelo AR, os sinais saudáveis apresentaram menor previsibilidade. Isso demonstra que a previsibilidade depende do modelo usado para a análise, assim, as patologias da laringe podem diminuir ou aumentar a capacidade de previsão dos sinais de voz conforme o modelo usado. Com a avaliação dos resultados de PrCA das representações tridimensionais, tem-se comportamento semelhante ao obtido pela análise direta nos sinais de voz com o modelo AR, entretanto, essa forma e representação dos dados mostra se promissora em estudos futuros. Com esses ensaios, concluiu-se que este estudo foi muito útil para um maior conhecimento da dinâmica da produção vocal e que as medidas de previsibilidade são interessantes para avaliação de patologias da laringe, em especial, a presença de nódulo nas pregas vocais e edema de Reinke, pelo menos nestes estudos iniciais e usando os sinais de voz disponíveis. Mais estudos ainda são necessários, entretanto essa forma de análise já apresenta bons resultados, os quais podem ser aplicados para auxiliar o diagnóstico de disfonias por profissionais da saúde. / This thesis presents initial studies of the application of predictability measures to voice signal analysis. Its aim is to develop methods that are capable of differentiating healthy and pathological signals, also amongst pathologies. In order to do that, we perform an attempt to measure the uncertainty and predictability variations of the signals from the analyzed groups. Some larynx pathologies, such as nodule and Reinkes edema, that are used in this study, cause changes to the voice signals due to structure and functionality modifications of the vocal tract and folds. The main modifications are higher amplitude and frequency perturbations, noise addition, and supression of high frequency harmonic components. Because of that, the signals lose some of their almost periodic structure, the vocal system\'s uncertainty increases and, therefore, the predictability decreases. We use several measures to evaluate these changes, such as Shannons entropy and relative entropy between healthy and pathological signals. In addition, we use the predictive power (PP), that is based on the relative entropy between the voice signal and its prediction error given by a model. Firstly, we used the autoregressive model (AR), common for voice analysis, however, due to unsatisfactory results, we presented a model based on wavelet decomposition. We also took advantage of another tool, called predictable component analysis (PrCA), it performs a signal decomposition in components that are ordered by their predictability. Then it is possible to reconstruct the signals using only their most predictable components. Using this technique, we analyzed a kind of tridimensional representation of the voice signals in a space with coordinates given by delayed versions of the signals. We tested the developed algorithms with the aid of simulated voice signals, which had variations of noise level and amplitude and frequency perturbations. By means of that, it was possible to detect errors and solve method problems. After the algorithms evaluation, we estimated the entropy of the voice signals and the relative entropy between the healthy signals and all the signals. In addition, we estimated the PP using the AR and wavelet based models. After that, we used the PrCA in order to obtain more predictable versions of the signals and then, estimated the PP using this version as the signals prediction. Also, we applied the PrCA to the signals tridimensional representations using a multidimensional AR model as a predictor. Using the voice entropy results, we could not distinguish between the analyzed groups, but with the relative entropy values, the healthy and pathological signals were differentiated efficiently. In spite of that, this measure has no practical application, because a diagnosed voice database is necessary as a basis of comparison. For the PP with AR modeling, no distinction between the groups is observed, but with the wavelet modeling, the healthy signals showed significantly higher predictability than the pathological ones, however the pathologies were differentiated. Using the PrCA with both models, the pathological and healthy groups were distinguished, but for the AR model, the healthy signals presented smaller predictability. This shows that the predictability depends on the analysis model, thus the larynx pathologies can decrease or increase the prediction capacity of the voice signals according to the used model. The results of PrCA of the tridimensional representations show similar behavior of the ones from direct PrCA signal analisys with the AR model. Despite of these results, this form of data representation seems to be promising for future studies. Considering these results, we concluded that this study was very useful to acquire a better understanding of the dynamics of voice production and that the predictability measures are interesting for the evaluation of larynx pathologies, especially presence of nodule in the vocal folds and Reinke\'s edema, at least for this initial study using the available signals. More studies are still necessary, but this analysis method already presents good results, which can be applied to aid pathology diagnosis by health professionals.

In this thesis, we consider several statistical techniques applicable to claim frequency models of an MTPL portfolio with a focus on overdispersion. The practical part of the work is focused on the application and comparison of the models on real data represented by an MTPL portfolio. The comparison is presented by the results of goodness-of-fit measures. Furthermore, the predictive power of selected models is tested for the given dataset, using the simulation method. Hence, this thesis provides a combination of the analysis of goodness-of-fit results and the predictive power of the models.

No description available.