This volume is a collection of three essays relating to the pricing of securities in financial markets, such as stock markets, where a large number of individual securities are traded. Lead-Lag Effects in a Competitive REE MarketThis essay introduces a model of cross-security information aggregation. The model is essentially an extension of Chan (Journal of Finance, 1993) to the case of simultaneous auction markets where revealed information is correlated across securities.The model provides clear predictions of lead-lag effects between securities returns. Several of the model's predictions are confirmed empirically using data from the Paris Bourse. Other models of price formation, including the basic Chan model and nonsynchronous trading, are rejected as they cannot account for observed return patterns. Learning the True Index LevelThis essay extends the model of cross-security information aggregation by deriving implications for autocorrelation in index returns. Both time series and cross-sectional predictions are confirmed by empirical evidence from the Paris Bourse. In addition, the time series predictions are consistent with earlier, partly unexplained, empirical evidence from the US market. An Empirical Study of Index Return AutocorrelationThis essay studies return autocorrelation on the Stockholm Stock Exchange focusing on the relation between index returns and indvidual stock returns. It is demonstrated that the two return types have similar time series properties, and it is concluded that the causes of autocorrelation are the same in both cases. / <p>Diss. Stockholm : Handelshögskolan, 1997</p>

Der Klimawandel hat weitreichende Folgen auf die Gesundheit der Menschen. Insbesondere Übertragungskrankheiten wie Dengue bekommen global gesehen eine zunehmende Bedeutung. Über die raumzeitliche Verteilung und das Umwelt-Dengue Risiko ist bisher wenig bekannt. Das Hauptziel dieser Dissertation war es daher, die Ätiologie von Dengue in einem hoch endemischen Gebiet besser zu verstehen. Es wurden räumliche Muster des Krankheitsauftretens untersucht, die anschließend in einer Umwelt-Risiko Analyse mit örtlichen Wetterdaten und Landnutzungsinformationen in Zusammenhang gebracht wurden. Zunächst wurde ein raumzeitlicher Ansatz durchgeführt, um herauszufinden, in wie weit Analysen, die verschiedene Aggregationsebenen miteinander kombinieren, zu neuen Erkenntnissen von raumzeitlichen Mustern von Dengue beitragen können. Anschließend wurde ein auf nicht-lineare zeitliche Einflüsse kontrolliertes, Poisson-generalisiertes additives Regressionsmodell genutzt, um herauszufinden, welchen Einfluss Wetterparameter auf die Verbreitung von Dengue haben. Schließlich wurden Boosted regression trees verwendet, um auf nicht-lineare Zusammenhänge und Interaktionen zwischen einzelnen Landnutzungsfaktoren und Dengue zu kontrollieren und um eine Risikokarte zu erstellen. Die Ergebnisse deuten darauf hin, dass mehr als eine geographische Ebene notwendig ist, um Krankheitscluster zu bestätigen. Minimaltemperatur, Regenmenge, und Windgeschwindigkeit waren mit der Verbreitung von Dengue im Untersuchungsgebiet assoziiert. Räumliche Dengue-Muster konnten durch Siedlungen, Wasser, gemischte Landwirtschaftsflächen, offene Flächen und stillgelegte Grünflächen erklärt werden. Dengue-Risiko ist auf der Ergebniskarte des Studiengebietes ersichtlich. Diese Dissertation liefert sowohl wertvolle Informationen für die Gesundheitspolitik in Malaysia als auch wichtige Herangehensweisen für die Entwicklung von Dengue-Kontrollmechanismen in und über die Untersuchungsregion hinaus. / Global changes to our earth system have impacts on human health; specifically vector-borne diseases such as dengue are of epidemiological importance. Dengue is a global disease burden. Little is known about the spatio-temporal distribution and environmental risk association of dengue disease. The main goal of this dissertation was to improve understanding of the etiology of dengue disease in a highly endemic region by focusing on, initially, vulnerability mapping of the disease occurrences and, next, environmental risk assessment between disease clusters and both weather and land use. First, a Spatio-temporal scan statistics approach was used to assess to what extent analyses that combine sub-district and address level data contribute to new insights into spatio-temporal dengue disease patterns to better inform health interventions. Second, a Poisson generalized additive model was used to assess the weather effects on dengue disease accounting for non-linear temporal effects. Third, a Boosted regression trees approach was used to account for nonlinearities and interactions between the land use factors and dengue disease and to generate a risk map. Results suggested that more than one geographical level was needed to confirm the disease clusters. Minimum temperature, rainfall and wind speed, were associated with the dengue cases in the study area. Spatial patterns of dengue cases could be explained by land use types, including human settlements, water bodies, mixed horticulture land, open land and neglected grassland. The predicted risk map depicted dengue risk in the study area. This dissertation provided compelling approaches that are highly valuable for dengue vector control policy advice; applicability is not confined to Malaysia but is transferable to other studies in similar settings.

[pt] Esta tese é composta por dois capítulos. O primeiro capítulo mostra que a presença de efeitos lead-lag no mercado de ações dos EUA é um fenômeno mais amplo do que previamente reportado pela literatura e está associado à existência de momentum de fatores de um dia. Os efeitos lead-lag estão presentes na frequência diária, sempre que as ações são expostas ao mesmo fator de risco, difundidas por quase 100 fatores. Este fenômeno não é explicado pelo efeito por indústria, reportado previamente pela literatura, efeitos de firmas de baixo valor de mercado reagindo a firmas com maior valor de mercado, assim como outros efeitos de lead-lag. O momentum de fatores de um dia está diretamente relacionado à existência de autocovariância cruzada entre ações expostas aos mesmos fatores de risco e está presente tanto na seção transversal quanto na série temporal. O momentum do fator de um dia é rentável mesmo após os custos de negociação e não apresenta quedas bruscas como outras estratégias de momentum. O momentum do fator de um mês é absorvido pelo momentum do fator de um dia, apresentando alfa negativo. A relevância do efeito do primeiro dia é confirmada com técnicas de machine learning. As reversões de curto prazo em ações também se tornam mais fortes depois de controlarmos para esse efeito de autocovariância cruzada que vem pelo componente de fatores. O segundo capítulo mostra como o momentum de fatores impacta o desempenho das estratégias de reversão de curto prazo em ações nos Estados Unidos. Benefícios significativos no desempenho podem ser alcançados se os efeitos do momento do fator forem considerados na construção de estratégias de reversão. As estratégias tradicionais de reversão de curto prazo em ações padrão têm uma exposição negativa ao momentum de fatores, uma vez que vendem as ações vencedoras de curto prazo que, em média, estão mais expostas aos fatores vencedores de curto prazo e compram ações perdedoras de curto prazo que, em média, estão mais expostas aos fatores perdedores de curto prazo. A melhor maneira de neutralizar esse efeito que prejudica a rentabilidade da reversão de curto prazo é proteger simultaneamente as exposições das ações a um conjunto elevado de fatores de risco. Por exemplo, o hedge feito apenas para os 3 fatores Fama-French não elimina completamente a exposição ao momentum de fatores. Classificar ações pelo usando o resíduo dos retornos não é tão eficiente quanto classificar nos retornos totais, pois tal estratégia não neutraliza completamente a exposição negativa ao momentum do fator. Propomos uma estratégia de reversão totalmente hedgeada que, diferentemente das estratégias convencionais de reversão de curto prazo, é lucrativa após os custos de transação, que não apresenta quedas bruscas como outras estratégias de momentum tradicional, que tem índice de Sharpe 2,5 vezes maior do que as estratégias de reversão convencionais e que é lucrativa mesmo se for restrita a apenas a ações com alto valor de mercado. / [en] This thesis is composed by two chapters. The first chapter shows that the presence of lead-lag effects in the US equity market is a broader phenomenon than previously found in the literature and is associated with the existence of a strong one-day factor momentum. Lead-lag effects are present whenever stocks are exposed to the same common risk factor, holding for almost 100 factors on a daily frequency. This phenomenon is not explained by the previously reported industry, large-cap to small-cap and other lead-lag effects. One-day factor momentum is directly related to the existence of factor-based stock cross-autocovariance and is present both in the cross-section and the time series. One-day factor momentum is profitable after trading costs and does not present crashes. One-month factor momentum is subsumed by one-day factor momentum with negative alpha in spanning tests. The relevance of the one-day effect is confirmed with machine learning techniques. Short-term reversals in stocks also become stronger after we control for this factor-based cross-autocovariance pattern. The second chapter shows how factor momentum impacts the performance of standard short-term single-equity reversal strategies in the US equity market. Significant benefits in performance can be achieved if the effects of factor momentum is considered in the construction of reversal strategies. Standard short-term reversal strategies have a negative exposure to factor momentum since they sell winner stocks that on average are more exposed to the winner factors and buy loser stocks that on average are more exposed to loser factors. The best way to neutralize this effect that drags down short-term reversal performance is to hedge stocks exposures simultaneously to a very large set of factors. For instance, hedging only with the 3 Fama-French factors does not eliminate the exposure to factor momentum. Sorting stocks using residual returns is not as efficient as sorting on total returns as it does not completely neutralize the negative exposure to factor momentum. We propose a fully-hedged reversal strategy that, differently from conventional short-term reversal strategies, is profitable after trading costs, that do not present crashes, that has Sharpe ratio 2.5 times higher than the conventional reversal strategies and that is profitable even if we restrict our sample to only large-cap stocks.

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