This paper explores the relationship between household income inequality and macroeconomic uncertainty in the United States. Using a novel large-scale macroeconometric model, we shed light on regional disparities of inequality responses to a national uncertainty shock. The results suggest that income inequality decreases in most states, with a pronounced degree of heterogeneity in terms of the dynamic responses. By contrast, some few states, mostly located in the Midwest, display increasing levels of income inequality over time. Forecast error variance and historical decompositions highlight the importance of uncertainty shocks in explaining income inequality in most regions considered. Finally, we explain differences in the responses of income inequality by means of a simple regression analysis. These regressions reveal that the income composition as well as labor market fundamentals determine the directional pattern of the dynamic responses. / Series: Working Papers in Regional Science

This dissertation is structured around three original studies that offer unique insights into the challenges of change in business-to-business markets. All three studies share as an important starting point that firms rely on other firms to achieve strategic flexibility in volatile business environments. This means that firms source critical resources from business relationships in order to reduce long-term investments in times of change. From this perspective, firms' competitive advantages cross the boundaries of the firm and are embedded in their business partner networks. Thus, firms' business relationships and networks have become an important locus of organizational change in order to respond to turbulence in firms' business environments. Study one of this dissertation recognizes the importance of supplier relationships as a mechanism to react to changing business environments. The article focuses on the dynamic capabilities that enable firms to structurally reconfigure their supplier portfolios or supply networks in order to access necessary resources. The framework of relationship management capabilities introduced, is structured around three important sub-dimensions: relationship initiation, development, and ending capabilities, which collectively enable a firm to manage the reconfiguration of resource portfolios accessed via supplier relationships. The key implication for management relates to thinking beyond firms' established supply chains in times of change. While to a certain degree change can be absorbed within firms' existing supply chains, there might be a need to be 'agile', i.e. search for other suppliers who are better suited to more efficiently and effectively address such changes affecting firm competitiveness in the long run. While study one highlights the importance of firms' agility in adapting their supply chains in response to changes in their business environment, study two of this dissertation, although with a focus on the demand side of the business model, addresses the managerial challenges associated with such an agile adaptation process. Study two conceptualizes a framework for business model change and provides managers guidance to approach business model redesign. In particular, study two focuses on service business models and introduces the concepts of service infusion and defusion as important processes of business model redesign. The service infusion and defusion framework provides a pragmatic and systematic approach to understanding the nature of the business model change that companies have to manage, as well as linking these changes with knowledge creation and transfer processes. These are shown to be key for successfully managing such a business model redesign. While studies one and two assume strategy and its implementation to be key to a successful response to changes in firms' business environment, study three draws attention to the difficulties of arriving at such an appropriate or fitting response strategy in the first place, given the available information. In particular, this study examines the link between sensing changes in firms' business environments and managerial decision making in the form of strategy choice. Thereby, the study shows that strategy change causes disruptions, which eventually affect firm performance. This effect is compounded with increasing sensitivity to change as well as increasing number of factors that trigger change, and thus impairs the long term benefits of such strategy change. Thus, the effectiveness of strategy or business model changes and their implementation is inevitably contingent on distinguishing key signals from noise that disturb or misguide firms' strategic decisions.

A key prediction of the Capital Asset Pricing Model (CAPM) is that idiosyncratic risk is not priced by investors because in the absence of frictions it can be fully diversified away. In the presence of constraints on diversification, refinements of the CAPM conclude that the part of idiosyncratic risk that is not diversified should be priced. Recent empirical studies yielded mixed evidence with some studies finding positive correlation between idiosyncratic risk and stock returns, while other studies reported none or even negative correlation. In this thesis we revisit the problem whether idiosyncratic risk is priced by the stock market and what the probable causes for the mixed evidence produced by other studies, using monthly data for the US market covering the period from 1980 until 2013. We find that one-period volatility forecasts are not significantly correlated with stock returns. On the other hand, the mean-reverting unconditional volatility is a robust predictor of returns. Consistent with economic theory, the size of the premium depends on the degree of 'knowledge' of the security among market participants. In particular, the premium for Nasdaq-traded stocks is higher than that for NYSE and Amex stocks. We also find stronger correlation between idiosyncratic risk and returns during recessions, which may suggest interaction of risk premium with decreased risk tolerance or other investment considerations like flight to safety or liquidity requirements. The difference between the correlations between the idiosyncratic volatility estimators used by other studies and the true risk metric - the mean-reverting volatility - is the likely cause for the mixed evidence produced by other studies. Our results are robust with respect to liquidity, momentum, return reversals, unadjusted price, liquidity, credit quality, omitted factors, and hold at daily frequency.

The volatility of house prices can raise systemic risks in the housing market due to the vulnerability of the banking and mortgage sectors to such fluctuations. Moreover, the extreme increases in housing markets have been considered a key feature of the last economic crisis and the run-up to it. Such increases, however, came to a sudden halt immediately before the crisis or directly it began. Despite the recent growth of scholarly work on the role of house price behaviour in economic stability, fundamental questions have yet to be answered: for instance: (i) how far do the nonlinear models outperform the linear models? And how does such nonlinearity explain the asymmetry in the cycle; (ii) what are the main characteristics of house price cycles, and how do they differ over time; and (iii) what kind of policy intervention would stop a real estate boom? This thesis, made up of three empirical essays, aims to take a step forward in answering these questions. The first essay examines whether house prices in large metropolitan areas such as London, New York and Hong Kong follow linear or nonlinear models. The Smooth Transition Autoregressive model was used on a sample of monthly data over the period 1996:1 to 2015:12. The results indicate that linear models are unsuitable for modelling the housing market for the chosen cities. Moreover, strong evidence indicates that real estate prices are largely nonlinear and can well be modelled using a logistic smooth transition model (LSTAR). Estimation results also show different degrees of asymmetry. In particular, the speed of transition between the expansion and contraction of house prices is greater in London than it is in Hong Kong while the speed of transition between boom and bust in New York house prices is the slowest. Further, the forecast results suggest that the LSTAR outdoes the linear model in out-of-sample performance. The second essay investigates the main features of house price cycles in the same major metropolitan areas by providing a reasonable level of discrimination between the cyclical decomposition techniques available for capturing suitable measurements for house price cycles. Through a sample of large cities in several countries, it is shown that the model-based filter is suitable for capturing the main features of house price cycles and the results confirm that these cycles are centred at low frequency. Moreover, there is evidence of substantial variation in the duration and amplitude of these cycles both across cities and over time. The third essay provides evidence that real house prices are significantly affected by financial stability policies. Considering the Hong Kong experience, the results show strong evidence of duration dependences in both the upswing and downswing phases of the cycle. Moreover, the time taken to reach the turning point increases dramatically as the cycle proceeds. The findings also suggest that there is feedback between house price volatility and the policies that affect the housing market. Accordingly, house prices respond with more volatility to any change in the loan to value and lending policy indicators (ignoring the sign of this shock). Finally, the evidence of asymmetry suggests that unanticipated house price increases are more destabilising than unanticipated falls in house prices.

O estudo da volatilidade dos retornos dos ativos ocupa um lugar de destaque dentro da moderna teoria de finanças. Tradicionalmente, os modelos empregados para a modelagem da volatilidade são estimados a partir de dados diários. No entanto, a recente disponibilidade de dados intradiários tem permitido a modelagem e a previsão da volatilidade dos ativos por meio da chamada variância realizada. Dessa forma, o objetivo principal da presente dissertação foi analisar como os modelos que incorporam dados intradiários se comportam, em termos de acurácia de previsão de volatilidade diária, em relação àqueles que utilizam apenas dados diários. Foram observados os comportamentos dos índices Ibovespa e S&P 500 durante o período de janeiro de 2006 a junho de 2009. Os resultados revelaram que o desempenho de previsão dos modelos estimados a partir de dados diários foi superior ao dos modelos de variância realizada para os dois índices. Buscou-se ainda comparar o comportamento dos modelos durante o período da crise de 2008. Novamente os resultados apontaram para uma melhor acurácia de previsão dos modelos que utilizaram apenas dados diários. / The study of volatility in asset returns is relevant within the modern theory of finance. Modeling volatility has been frequently based on daily data. Recent availability of intraday data has allowed volatility modeling and forecasting through the so called realized variance. The main objective of this master’s thesis was, therefore, to compare the accuracy of daily volatility forecasting between models that use either daily or intraday data. Returns during the period January 2006 to June 2009 on two indexes, the Ibovespa and the S&P 500, were used. Results showed that, for both indexes, forecasting based on daily data was superior to forecasting that used intraday returns. Comparison between models was also tested during the 2008 crisis. Similarly, results showed a better forecasting performance of daily data models.

As séries temporais financeiras são marcadas por comportamentos complexos e não-lineares. No mercado financeiro, além da trajetória das cotações, a sua variabilidade, representada pela volatilidade, consiste em importante informação para o mercado. Redes neurais são modelos não lineares flexíveis com capacidade de descrever funções de distintas classes, possuindo a propriedade de aproximadores universais. Este trabalho busca empregar redes neurais, especificamente Perceptron de múltiplas camadas com uma única camada escondida alimentada para frente (Feedforward Multilayer Perceptron), para a previsão da volatilidade. Mais ainda, é proposto um modelo híbrido que combina o modelo GARCH e redes neurais. Os modelos GARCH e redes neurais são estimados para duas séries financeiras: Índice S&P500 e cotações do petróleo tipo Brent. Os resultados indicam que a volatilidade aproximada por redes neurais é muito semelhante as estimativas dos tradicionais modelos GARCH. Suas diferenças são mais qualitativas, na forma de resposta da volatilidade estimada a choques de maior magnitude e sua suavidade, do que quantitativas, apresentando critérios de erros de previsão em relação a uma medida de volatilidade benchmark muito próximos. / The financial time series are characterized by complex and non-linear behaviors. In addition to the financial market trend in prices their variability or volatility, a risk estimate, is important information for the market players. Neural networks are flexible nonlinear models capable of describing functions of different classes, having the property of universal approximators. This paper employs neural networks, specifically one hidden layer feedforward Multilayer Perceptron, for volatility forecasting. Moreover, we propose a hybrid model that combines the GARCH model with neural networks. The GARCH and neural network models are estimated over two financial series: the S&P500 composite index and prices of Brent oil. The results indicate that the volatility approximated by neural networks is very similar to that estimated by the traditional GARCH models, while their differences are more qualitative than quantitative, with information content that differs from and complements each other for different market environments.

Neste trabalho analisaremos a utilização dos modelos de mudança de regime markoviano para a variância condicional. Estes modelos podem estimar de maneira fácil e inteligente a variância condicional não observada em função da variância anterior e do regime. Isso porque, é razoável ter coeficientes variando no tempo dependendo do regime correspondentes à persistência da variância (variância anterior) e às inovações. A noção de que uma série econômica possa ter alguma variação na sua estrutura é antiga para os economistas. Marcucci (2005) comparou diferentes modelos com e sem mudança de regime em termos de sua capacidade para descrever e predizer a volatilidade do mercado de valores dos EUA. O trabalho de Hamilton (1989) foi uns dos mais importantes para o desenvolvimento de modelos com mudança de regime. Inicialmente mostrou que a série do PIB dos EUA pode ser modelada como um processo que tem duas formas diferentes, uma na qual a economia encontra-se em crescimento e a outra durante a recessão. O câmbio de uma fase para outra da economia pode seguir uma cadeia de Markov de primeira ordem. Utilizamos as séries de índice Bovespa e S&P500 entre janeiro de 2003 e abril de 2012 e ajustamos o modelo GARCH(1,1) com mudança de regime seguindo uma cadeia de Markov de primeira ordem, considerando dois regimes. Foram consideradas as distribuições gaussiana, t de Student e generalizada do erro (GED) para modelar as inovações. A distribuição t de Student com mesmo grau de liberdade para ambos os regimes e graus distintos se mostrou superior à distribuição normal para caracterizar a distribuição dos retornos em relação ao modelo GARCH com mudança de regime. Além disso, verificou-se um ganho no percentual de cobertura dos intervalos de confiança para a distribuição normal, bem como para a distribuição t de Student com mesmo grau de liberdade para ambos os regimes e graus distintos, em relação ao modelo GARCH com mudança de regime quando comparado ao modelo GARCH usual. / In this work we analyze heterocedastic financial data using Markov regime switching models for conditional variance. These models can estimate easily the unobserved conditional variance as function of the previous variance and the regime. It is reasonable to have time-varying coefficients corresponding to the persistence of variance (previous variance) and innovations. The economic series notion may have some variation in their structure is usual for economists. Marcucci (2005) compared different models with and without regime switching in terms of their ability to describe and predict the volatility of the U.S. market. The Hamiltons (1989) work was the most important one in the regime switching models development. Initially showed that the series of U.S. GDP can be modeled as a process that has two different forms one in which the economy is growing and the other during the recession. The change from one phase to another economy can follow a Markov first order chain. We use the Bovespa series index and S&P500 between January 2003 and April 2012 and fitted the GARCH (1,1) models with regime switching following a Markov first order chain, considering two regimes. We considered Gaussian distribution, Student-t and generalized error (GED) to model innovations. The t-Student distribution with the same freedom degree for both regimes and distinct degrees showed higher than normal distribution for characterizing the distribution of returns relative to the GARCH model with regime switching. In addition, there was a gain in the percentage of coverage of the confidence intervals for the normal distribution, as well as the t-Student distribution with the same freedom degree for both regimes and distinct degrees related to GARCH model with regime switching when compared to the usual GARCH model.

This diploma thesis deals with problem of option pricing with stochastic volatility. At first, the Black-Scholes model is derived and then its biases are discussed. We explain shortly the concept of volatility. Further, we introduce three pricing models with stochastic volatility- Hull-White model, Heston model and Stein-Stein model. At the end, these models are reviewed.

Motivated by the recent evidence of investors' preference for stocks with lottery- type payoffs documented on the U.S. stock markets, I investigate preferences for stocks that appear to be like lotteries in Europe. Across 14 markets, lottery- type stocks, characterized by high idiosyncratic skewness, high idiosyncratic volatility and low price, underperform and exhibit a "lottery premium". Fur- thermore, preferences for lottery-type stocks can help to explain the puzzling negative relation between past idiosyncratic volatility and returns, which does not persist after controlling for past extreme positive returns. Examining the relation between national revenues from gambling and "lottery premium" I find that countries featuring higher gambling revenues also exhibit a higher "lottery premium". Overall, the results indicate that lottery preferences might impact investment decisions and stock prices. JEL Classification G11, G12 Keywords gambling, lottery-type stocks, idiosyncratic volatility, maximum returns Author's e-mail p.vokata@gmail.com Supervisor's e-mail novakji@fsv.cuni.cz

O objetivo desta dissertação foi avaliar a possível ocorrência de contágio de volatilidade no mercado de energia combustível, com foco em etanol, analisando commodities agrícolas e de energia. São examinados dois cenários. O Cenário I teve como objetivo identificar a presença de volatilidade spillover entre os preços futuros de petróleo e milho, cotados no mercado internacional, e o preço futuro de etanol, cotado no Brasil. Ou seja, se propôs a identificar a presença de volatilidade spillover no mercado futuro de etanol brasileiro. O Cenário II teve como objetivo identificar a presença de volatilidade spillover entre os preços futuros de petróleo e açúcar cotados no mercado internacional em relação ao preço físico de açúcar no Brasil. Neste caso o objetivo foi identificar a presença de volatilidade spillover no mercado físico de etanol brasileiro. As séries de preços trabalhadas abrangem o período de 18/05/10 a 29/12/11 e 20/05/03 a 29/12/11, para cada cenário respectivamente. Utilizou-se para análise uma modelagem GARCH multivariada, em função da robustez de seus resultados e da possibilidade de sua aplicação prática por profissionais do mercado. Concluiu-se que apenas no Cenário II foi possível identificar transmissão de volatilidade entre as estruturas analisadas. Porém, a não observação de contágio no Cenário I pode ter sido decorrente da limitação de dados disponíveis, dado ser recente a existência de contrato futuro de etanol brasileiro e pela baixa liquidez dos contratos negociados, o que incentiva análises futuras que busquem essa comprovação importante para a mitigação dos riscos inerentes a esses mercados. / The aim of this thesis was to evaluate the possible occurrence of volatility contagion in the fuel energy market, with focus on ethanol, analysing agricultural and energy commodities. Two scenarios are evaluated. Scenario I is proposed to identify the presence of volatility spillover between the futures prices of oil and corn, negotiated on the international future market and the futures prices of ethanol, negotiated in Brazil. That is, set out to identify the presence of volatility spillover in the ethanol Brazilian future market. Scenario II is proposed to identify the presence of volatility spillover between the futures prices of oil and sugar listed in the international market in relation to the spot price of sugar in Brazil. In this case the objective was to identify the presence of volatility spillover in the ethanol Brazilian spot market. The price series covered the period 05/18/10 until 12/29/11 and 12/29/11 until 05/20/03, respectively for each scenario. It was used the multivariate GARCH model to analyze this occurrence, because this model give robust results and the possibility of practical application by market professionals. It was concluded that only in Scenario II was identified volatility transmission between the analyzed structures. However, non-observation of contagion in Scenario I may have been due to the limited data available, since it is recent the existence of ethanol Brazilian futures contract and the low liquidity, which encourages future analyzes seeks to prove this evidence, important to mitigating the risks inherent in these markets.