The purpose of the thesis is to introduce novel measure of real marginal cost in the New Keynesian Phillips Curve (NKPC) and compares its performance with conventional mea- sures such as output gap and labour share of income. Real marginal cost is derived from a flexible function whereas labour share is based on restrictive assumption of Cobb-Douglas technology. Dynamic correlations and results of NKPC indicate that real marginal cost is better than ad hoc measure of output gap and labour share. Given the heterogeneity in price setting behaviour across sectors, cost functions and NKPC are estimated for the agriculture, manufacturing and other sectors of Pakistan's economy. Real marginal cost is derived from static and dynamic cost functions. In the presence of adjustment costs, dynamic cost functions that are consistent and integrated with their static systems are required. Such dynamic translog cost functions are estimated after testing the theoretical properties and existence of long term relationships in the static functions. Cost attributes, marginal cost, total factor productivity, technological progress, demand and substitution elasticities are derived from static and dynamic functions. Three specifications of forward looking and hybrid form of the Phillips curves are estimated with real marginal cost, output gap and labour share. Results indicate that hybrid specifications perform better than the forward looking models in terms of goodness of fit and statistical significance. Further, comparison of Phillips curves estimated with real marginal cost, output gap and labour share indicate that real marginal cost performs better in explaining inflation dynamics in Pakistan. The results indicate that forward looking behaviour dominates and high level of nominal rigidities persists in Pakistan. Finally, hybrid form of the NKPC is estimated for a panel of sixteen Asian economies. With the consideration of heterogeneity and aggregation bias, the mean group, random coefficient and weighted average coefficients are derived from individual estimates. The unobserved time variant common factors cause cross correlation in the errors that may lead towards inconsistent estimates. Therefore, cross section averages of the explanatory and the dependent variables are augmented in hybrid specification to capture the effect of latent variables. Findings suggest that the discount factor is almost 0.94, the nominal rigidities are 33% and the weights of expected and past inflation are 66% and 33% respectively. Nominal rigidities of the Asian economies are lower than the estimates for US and Euro areas. The weights of expected and past inflation of the Asian economies are consistent with the US but lower than the estimates from the Euro areas.

This thesis investigates systematic liquidity risk and short-term stock price reaction to large one-day price changes. We study 642 constituents of the FTSALL share index over the period from 1st July 1992 to 29th June 2007. We show that the US evidence of a priced systematic liquidity risk of Pastor and Stambaugh (2003) and Liu (2006) is not country-specific. Particularly, systematic liquidity risk is priced in the London Stock Exchange when Amihud's (2002) illiquidity ratio is used as a liquidity proxy. Given the importance of systematic liquidity risk in the asset pricing literature, we are interested in testing whether the different levels of systematic liquidity risk across stocks can explain the anomaly following large one-day price changes. Specifically, we expect that the stocks with high sensitivity to the fluctuations in aggregate market liquidity to be more affected by price shocks. We find that most liquid stocks react efficiently to price shocks, while the reactions of the least liquid stocks support the uncertain information hypothesis. However, we show that time-varying risk is more important than systematic liquidity risk in explaining the price reaction of stocks in different liquidity portfolios. Indeed, the time varying risk explains nearly all of the documented overreaction and underreaction following large one-day price changes. Our evidence suggests that the observed anomalies following large one-day price shocks are caused by the pricing errors arising from the use of static asset pricing models. In particular, the conditional asset pricing model of Harris et al. (2007), which allow both risk and return to vary systematically over time, explain most of the observed anomalies. This evidence supports the Brown et al. (1988) findings that both risk and return increase in a systematic fashion following price shocks.

Real estate is an attractive asset class in the mixed-asset portfolio due to favorable risk return characteristics and low correlations with other asset classes like stock and bonds. Unlike financial assets, real estate is a physical asset where large lot sizes/indivisibility, heterogeneity, low liquidity and high transaction costs make applying financial models like modern portfolio theory (MPT) challenging. Optimal allocations to real estate found in literature are generally lower than actual allocations by investors and portfolio managers indicating there are aspects of the application of MPT to real estate that are not fully understood. Since management of real estate is costly and requires expert skills, the question on whether to outsource property management functions is of paramount interest for the real estate industry. The aim of the thesis is to contribute to the literature on strategic decisions for real estate investors on allocation and management, Apart from reviewing literature relevant for strategic decisions at different levels and using a top-down approach to illustrate how selected allocation and management decisions are connected, four separate empirical studies are made to investigate the nature of selected strategic decisions for real estate investors. / <p>QC 20170515</p>

In this thesis, we examine the exchange rate pass-through phenomenon in the Czech Re- public over 1998:1-2014:1 period. As our vector autoregression results indicate, short-term pass-through effect slowed down and prolongated its duration substantially. Consequently, the accumulated value to be transmitted increased compared to previous findings. In the case of exchange rate pass-through effect to CPI, the accumulated response after 18 months accounts for about 40-60 per cent. In this regard, our time-varying results using unique Chebyshev Time Polynomials points to period 2008-2014 to be the leading cause. It seems that during macroeconomically less stable periods the exchange rate pass-through in the Czech Republic tends to increase. Even though the consensus on the pass-through lev- els and its development over time is rather scarce, we find support for our conclusions. More interestingly, having in mind November's currency interventions of the Czech Na- tional Bank to weaken koruna (and thus avoiding deflation), our results reveal that this measure has become much more effective in the latest years (as consequence of the crisis) than previous literature suggested. Following up on that, it seems that exchange rate regained some of its rather historical importance while conducting monetary policy...

The dissertation consists of two essays. The first essay investigates the ability of prior returns, relative to some aggregate market returns, to predict future returns on industry style portfolios. By pooling time series of returns across industries for the period between July 1969 and June 2012, we find that prior returns differential predicts one month ahead returns negatively, even in the presence of a set of popular state variables. The predictability remains significant and negative for up to 5 month ahead returns. The predictability is shown to be robust to alternative specifications, estimation methodology and industry classifications. A possible explanation of this finding is based on time–varying (dynamic) loss aversion among investors. More specifically, when combined with house money effects, prior performance has inverse relationship with degree of loss aversion leading to predictability in the next period returns. The second essay examines the nature of time variation in the risk exposure of country mutual funds to the US market movement and to the benchmark foreign market movement. It uses weekly data on 15 closed end funds and 19 exchange traded funds for the sample period between January, 2001 and December, 2012. Conditional factor models are employed to uncover the time variation in the estimated betas through short horizon regressions. The findings of the paper indicate considerable time variation in risk exposure of country mutual funds to the US market and foreign market risk factors. Additional investigation reveals the following observations. First, the US market betas suffer greater variation over the sample period than the target foreign market betas. Second, the overall fluctuation in betas for the closed end funds is found to be higher than that for the exchange traded funds. Third, emerging market funds experience more oscillation in the risk exposure than their developed market counterparts. It is found that a combination of the US macroeconomic state variables and investors’ sentiment can predict future betas significantly. The findings of the paper have important implication for US investors seeking diversification benefits from country mutual funds.

[pt] Os modelos GAS (generalized autoregressive score) são modelos de séries temporais com parâmetros variantes no tempo, os quais possuem sua evolução ditada pelo vetor score ponderado da função de verossimilhança. A avaliação da verossimilhança nestes modelos é bastante simples, bem como incorporação de efeitos como assimetria, memória longa e outras dinâmicas. Por serem baseados no score da verossimilhança, exporta-se a estrutura completa da distribuição preditiva para o mecanismo de atualização dos parâmetros, e não apenas a média ou momentos de ordem superior. Estas características, somadas á capacidade de lidar com processos multivariados e não estacionários, tornam a classe em estudo uma nova alternativa para a construção de modelos com parâmetros variantes, particularmente para séries temporais não gaussianas. Nesta dissertação, foram desenvolvidos modelos GAS univariados para a análise das séries mensais de vazão do Rio Paraibuna (MG) e de fator de capacidade de uma usina é olica não divulgada do Nordeste, utilizando as distribuições gama e beta, respectivamente. Além disso, foi derivado um novo modelo GAS bivariado com marginais gama e beta para a modelagem conjunta dos processos de vazão e vento, de modo a explorar a complementaridade sazonal entre as séries. / [en] The GAS models (generalized autoregressive score) are time series models with time-varying parameters, which have their update mechanism drived by the scaled score of the likelihood function. The likelihood evaluation in these models is quite simple, as well as the incorporation of effects like asymmetry, long memory and other dynamics. Because they are based in the scaled score of the likelihood, it exploits the full structure of the predictive distribution to the update mechanism of the parameters, and not just mean or higher order moments. These characteristics, coupled with the ability to handle with multivariate and non-stationary processes, make the studied class a new alternative to the construction of models with time-varying parameters, particularly for non-Gaussian time series. In this dissertation, univariate GAS models were developed to analyze monthly series of streamflow of Paraibuna river (MG) and of capacity factor of a wind farm undisclosed in Northeast, using the gamma and beta distributions, respectively. In addition, a new bivariate GAS model with gamma and beta marginals was derived for the joint modeling of the streamflow and wind processes, in order to explore the seasonal complementarity between the series.

Este trabalho propõe uma metodologia de identificação para sistemas lineares sujeitos a saltos markovianos. Dada uma sequência de observações ruidosas da variável de estados, busca-se estimá-la juntamente com os parâmetros (desconhecidos) que descrevem o sistema dinâmico no espaço de estados. Como é bem conhecido, a ltragem ótima nesta classe de sistemas tem requisitos computacionais exponencialmente crescentes em função do tamanho da amostra, e torna-se inviável na prática. Recorre-se, portanto, a um algoritmo sub-ótimo de ltragem, cujos resultados são utilizados na identificação por máxima verossimilhança segundo a metodologia apresentada. Simulações realizadas mostram boa convergência. / This paper proposes a methodology for the identification of Markov-jump linear systems. Given a sequence of noisy observations of the state variable, our objective is to estimate it along with the (unknown) parameters that drive the system in the state-space. As it is well known, the optimal ltering in this class of systems requires exponentially increasing computing power, in proportion to the sample size, and is not feasible in practice. We resort, therefore, to a sub-optimal algorithm, whose results are used for a maximum likelihood identification according to the methodology presented here. Simulations show a good convergence.

Les travaux présentés dans ce mémoire traitent de l'identification des systèmes dynamiques représentés sous la forme de modèles linéaires continus à paramètres variant lentement au cours du temps. La complexité du problème d'identification provient d'une part du caractère inconnu de la loi de variation des paramètres et d'autre part de la présence de bruits de nature inconnue sur les signaux mesurés. Les solutions proposées s'appuient sur une combinaison judicieuse du filtre de Kalman en supposant que les variations des paramètres peuvent être représentées sous la forme d'une marche aléatoire et de la méthode de la variable instrumentale qui présente l'avantage d'être robuste vis à vis de la nature des bruits de mesure. Les algorithmes de type récursif sont développés dans un contexte d'identification en boucle ouverte et en boucle fermée. Les différentes variantes se distinguent par la manière dont est construit la variable instrumentale. Inspirée de la solution développée pour les systèmes linéaires à temps invariant, une construction adaptative de la variable instrumentale est suggérée pour pouvoir suivre au mieux l'évolution des paramètres. Les performances des méthodes développées sont évaluées à l'aide de simulations de Monte Carlo et montrent la suprématie des solutions proposées s'appuyant sur la variable instrumentale par rapport celles plus classiques des moindres carrés récursifs. Les aspects pratiques et d'implantation numérique sont d'une importance capitale pour obtenir de bonnes performances lorsque ces estimateurs sont embarqués. Ces aspects sont étudiés en détails et plusieurs solutions sont proposées non seulement pour robustifier les estimateurs vis à vis du choix des hyper-paramètres mais également vis à vis de leur implantation numérique. Les algorithmes développés sont venus enrichir les fonctions de la boîte à outils CONTSID pour Matlab. Enfin, les estimateurs développés sont exploités pour faire le suivi de paramètres de deux systèmes physiques : un benchmark disponible dans la littérature constitué d'un filtre électronique passe-bande et une vanne papillon équipant les moteurs de voiture. Les deux applications montrent le potentiel des approches proposées pour faire le suivi de paramètres physiques variant lentement dans le temps / The work presented in this thesis deals with the identification of dynamic systems represented through continuous-time linear models with slowly time-varying parameters. The complexity of the identification problem comes on the one hand from the unknown character of the parameter variations and on the other hand from the presence of noises of unknown nature on the measured signals. The proposed solutions rely on a judicious combination of the Kalman filter assuming that the variations of the parameters can be represented in the form of a random walk, and the method of the instrumental variable which has the advantage of being robust with respect to the nature of the measurement noises. The recursive algorithms are developed in an open-loop and closed-loop identification setting. The different variants are distinguished by the way in which the instrumental variable is built. Inspired by the solution developed for time-invariant linear systems, an adaptive construction of the instrumental variable is suggested in order to be able to follow the evolution of the parameters as well as possible. The performance of the developed methods are evaluated using Monte Carlo simulations and show the supremacy of the proposed solutions based on the instrumental variable compared with the more classical least squares based approaches. The practical aspects and implementation issues are of paramount importance to obtain a good performance when these estimators are used. These aspects are studied in detail and several solutions are proposed not only to robustify the estimators with respect to the choice of hyperparameters but also with respect to their numerical implementation. The algorithms developed have enhanced the functions of the CONTSID toolbox for Matlab. Finally, the developed estimators are considered in order to track parameters of two physical systems: a benchmark available in the literature consisting of a bandpass electronic filter and a throttle valve equipping the car engines. Both applications show the potential of the proposed approaches to track physical parameters that vary slowly over time

Neste trabalho foram utilizadas cópulas bivariadas variantes no tempo para modelar a dependência entre séries de retornos financeiros. O objetivo deste trabalho é apresentar uma abordagem de estimação semi-paramétrica de cópulas variantes no tempo a partir de uma função de cópula paramétrica na qual o parâmetro varia no tempo. A função do parâmetro desconhecido será estimada pela aproximação de ondaleta Haar, polinômio de Taylor e Kernel. O desempenho dos três métodos de aproximação será comparado via estudos de simulação. Uma aplicação aos dados reais será apresentada para ilustrar a metodologia estudada. / In this work the bivariate Time-varying copula models have been used to model the dependence between payback. The aim of this work is to present an approach of semiparametric estimation of Time-varying copula models from a parametric copula function in which the parameter varies with the time. The function of the unknown parameter will be estimated by Haar wavelet approach, Taylor series and smoothing Kernel approximation. The measured performance of the three estimation method will be compared by simulation study. An application of the data will be presented to illustrate the studied methodology.

Ferramentas e técnicas de visualização promovem uma análise de dados mais efetiva pelo fato de explorar a capacidade humana na percepção de padrões, principalmente em representações gráficas. Muitos fenômenos são associados a algum tipo de referência, temporal ou geográfica, que pode oferecer informação importante quando são submetidos a processos de análise. Este trabalho aborda representações visuais de dados geradas por técnicas de projeção multidimensional, e propõe uma estratégia para o tratamento diferenciado das referências temporais ou geográficas presentes em conjuntos de dados, no processo de gerar uma projeção multidimensional. Foi proposta e implementada uma variação da técnica Least Square Projection (LSP) que evidencia a informação das referências e permite ao usuário interagir com os mapas visuais gerados, bem como diversas funcionalidades que auxiliam no processo de análise exploratória. A nova abordagem é ilustrada por meio de estudos de caso envolvendo bases de dados temporais e com referências geográficas, em que foi possível observar o comportamento global dos elementos, bem como comportamentos de elementos ou grupos de elementos de interesse. Limitações da estratégia proposta também são discutidas / Visualization tools and techniques promote more effective data analysis by exploiting the human visual perception capabilities in detecting patterns in graphical representations. Many phenomena generate data that include temporal or geographical references, which are likely to provide important information in data analysis procedures. This work addresses data visualizations generated with multidimensional projections, proposing a strategy to handle temporal and geographical references present in multidimensional data sets, when generating multidimensional projections. The Least Squares Projection (LSP) technique was extended to explicitly handle the reference information and represent it in the visual maps, and a set of supporting analysis functions have been implemented. The proposed approach is illustrated through case studies on multidimensional data sets, in which it was possible to observe the global behavior of the elements, as well as individual behavior of elements or groups of elements of interest