Department of Statistics / MSc (Statistics) / The ripple e ect of a stock market crash due to extremal dependence is a global issue with key attention and it is at the core of all modelling e orts in risk management. Two methods of extreme value theory (EVT) were used in this study to model equity risk and extremal dependence in the tails of stock market indices from four African emerging markets: South Africa, Nigeria, Kenya and Egypt. The rst is the \bivariate-threshold-excess model" and the second is the \point process approach". With regards to the univariate analysis, the rst nding in the study shows in descending hierarchy that volatility with persistence is highest in the South African market, followed by Egyptian market, then Nigerian market and lastly, the Kenyan equity market. In terms of risk hierarchy, the Egyptian EGX 30 market is the most risk-prone, followed by the South African JSE-ALSI market, then the Nigerian NIGALSH market and the least risky is the Kenyan NSE 20 market. It is therefore concluded that risk is not a brainchild of volatility in these markets. For the bivariate modelling, the extremal dependence ndings indicate that the African continent regional equity markets present a huge investment platform for investors and traders, and o er tremendous opportunity for portfolio diversi cation and investment synergies between markets. These synergistic opportunities are due to the markets being asymptotic (extremal) independent or (very) weak asymptotic dependent and negatively dependent. This outcome is consistent with the ndings of Alagidede (2008) who analysed these same markets using co-integration analysis. The bivariate-threshold-excess and point process models are appropriate for modelling the markets' risks. For modelling the extremal dependence however, given the same marginal threshold quantile, the point process has more access to the extreme observations due to its wider sphere of coverage than the bivariate-threshold-excess model. / NRF

Au cours des dernières années, les communautés virtuelles de patients se sont énormément développées sur l'Internet. Ces communautés permettent des échanges fréquents entre les patients, qui peuvent partager des informations liées à la santé dans un environnement interactif. Alors que beaucoup s'accordent sur l'opportunité représentée par ces communautés pour ses utilisateurs, les connaissances sur ce qui détermine l'action collective en ligne des patients ainsi que sur les fondamentaux de l'action collective en ligne dans ces espaces virtuels sont relativement peu développées. En conséquence, ce travail doctoral examine les raisons pour lesquelles les patients interagissent entre eux et comment ils procèdent. En nous appuyant sur le modèle du comportement orienté vers un but, la théorie de la valeur de l'attente, la théorie des forces du champ, les concepts de dons et les interviews menées, nous avons développé un modèle qui examine les interactions en ligne des patients dans un contexte d'action collective en ligne. Une approche multi-méthode, qualitative et quantitative, permet d'explorer les interactions des patients et de mesurer les déterminants de l'action collective en ligne sur ces espaces virtuels. L'analyse qualitative de 54 entretiens menés avec des patients, des proches de patients, des professionnels de la santé 2.0, des médecins et des soignants permet d'affiner le modèle de recherche, qui a ensuite été testé au travers d'une enquête quantitative auprès de 269 patients. Cette recherche contribue à la recherche en systèmes d'information en augmentant nos connaissances sur la dynamique individuelle et les interactions qui entourent les communautés de patients en ligne. / Over the last few years, virtual patients’communities have been developing tremendously over the Internet. These Web 2.0 communities allow frequent interactions among patients, who can share health-related information within an interactive environment. While many agree on the opportunity represented by those communities for its users, we know very little about what determines patients’ online collective action, specifically on virtual communities as well as the fundamentals of online collective action in these virtual spaces. Accordingly, this doctoral work examines why patients interact with others and how they interact on topics related to their disease through these virtual communities. Drawing on the goal-directed behavior (MGB), the expectancy-value (EVT) theories, the field force theory, gift concepts and field interviews, we have developed a model for examining patients’ online interactions and identified gift-giving behaviors in the context of online collective action. A multi-method, qualitative and quantitative approaches, enables us to explore patients’ interactions and measures the determinants of online collective action on these virtual spaces. The qualitative analysis of 54 interviews conducted with patients, patient’s relatives, Health 2.0 professionals, doctors and caregivers allows refining the research model, which has then been tested through a survey handled with 269 patients, members of patient’s communities. This research contributes to IS research by increasing our knowledge regarding the individual dynamics and interactions that surround online patients’ communities.

This master's thesis studies portfolio optimization using linear programming algorithms. The contribution of this thesis is an extension of the convex framework for portfolio optimization with Conditional Value-at-Risk, introduced by Rockafeller and Uryasev. The extended framework considers risk measures in this thesis belonging to the intersecting classes of coherent risk measures and distortion risk measures, which are known as coherent distortion risk measures. The considered risk measures belonging to this class are the Conditional Value-at-Risk, the Wang Transform, the Block Maxima and the Dual Block Maxima measures. The extended portfolio optimization framework is applied to a reference portfolio consisting of stocks, options and a bond index. All assets are from the Swedish market. The returns of the assets in the reference portfolio are modelled with elliptical distribution and normal copulas with asymmetric marginal return distributions. The portfolio optimization framework is a simulation-based framework that measures the risk using the simulated scenarios from the assumed portfolio distribution model. To model the return data with asymmetric distributions, the tails of the marginal distributions are fitted with generalized Pareto distributions, and the dependence structure between the assets are captured using a normal copula. The result obtained from the optimizations is compared to different distributional return assumptions of the portfolio and the four risk measures. A Markowitz solution to the problem is computed using the mean average deviation as the risk measure. The solution is the benchmark solution which optimal solutions using the coherent distortion risk measures are compared to. The coherent distortion risk measures have the tractable property of being able to assign user-defined weights to different parts of the loss distribution and hence value increasing loss severities as greater risks. The user-defined loss weighting property and the asymmetric return distribution models are used to find optimal portfolios that account for extreme losses. An important finding of this project is that optimal solutions for asset returns simulated from asymmetric distributions are associated with greater risks, which is a consequence of more accurate modelling of distribution tails. Furthermore, weighting larger losses with increasingly larger weights show that the portfolio risk is greater, and a safer position is taken. / Denna masteruppsats behandlar portföljoptimering med linjära programmeringsalgoritmer. Bidraget av uppsatsen är en utvidgning av det konvexa ramverket för portföljoptimering med Conditional Value-at-Risk, som introducerades av Rockafeller och Uryasev. Det utvidgade ramverket behandlar riskmått som tillhör en sammansättning av den koherenta riskmåttklassen och distortions riksmåttklassen. Denna klass benämns som koherenta distortionsriskmått. De riskmått som tillhör denna klass och behandlas i uppsatsen och är Conditional Value-at-Risk, Wang Transformen, Block Maxima och Dual Block Maxima måtten. Det utvidgade portföljoptimeringsramverket appliceras på en referensportfölj bestående av aktier, optioner och ett obligationsindex från den Svenska aktiemarknaden. Tillgångarnas avkastningar, i referens portföljen, modelleras med både elliptiska fördelningar och normal-copula med asymmetriska marginalfördelningar. Portföljoptimeringsramverket är ett simuleringsbaserat ramverk som mäter risk baserat på scenarion simulerade från fördelningsmodellen som antagits för portföljen. För att modellera tillgångarnas avkastningar med asymmetriska fördelningar modelleras marginalfördelningarnas svansar med generaliserade Paretofördelningar och en normal-copula modellerar det ömsesidiga beroendet mellan tillgångarna. Resultatet av portföljoptimeringarna jämförs sinsemellan för de olika portföljernas avkastningsantaganden och de fyra riskmåtten. Problemet löses även med Markowitz optimering där "mean average deviation" används som riskmått. Denna lösning kommer vara den "benchmarklösning" som kommer jämföras mot de optimala lösningarna vilka beräknas i optimeringen med de koherenta distortionsriskmåtten. Den speciella egenskapen hos de koherenta distortionsriskmåtten som gör det möjligt att ange användarspecificerade vikter vid olika delar av förlustfördelningen och kan därför värdera mer extrema förluster som större risker. Den användardefinerade viktningsegenskapen hos riskmåtten studeras i kombination med den asymmetriska fördelningsmodellen för att utforska portföljer som tar extrema förluster i beaktande. En viktig upptäckt är att optimala lösningar till avkastningar som är modellerade med asymmetriska fördelningar är associerade med ökad risk, vilket är en konsekvens av mer exakt modellering av tillgångarnas fördelningssvansar. En annan upptäckt är, om större vikter läggs på högre förluster så ökar portföljrisken och en säkrare portföljstrategi antas.

Die Dissertation untersucht das Potenzial von Machine-Learning-Methoden zur Verwaltung von Schwanzrisiken in nicht-stationären und hochdimensionalen Umgebungen. Dazu vergleichen wir auf robuste Weise datenabhängige Ansätze aus parametrischer oder nicht-parametrischer Statistik mit datenadaptiven Methoden. Da datengetriebene Methoden reproduzierbar sein müssen, um Vertrauen und Transparenz zu gewährleisten, schlagen wir zunächst eine neue Plattform namens Quantinar vor, die einen neuen Standard für wissenschaftliche Veröffentlichungen setzen soll. Im zweiten Kapitel werden parametrische, lokale parametrische und nicht-parametrische Methoden verglichen, um eine dynamische Handelsstrategie für den Schutz vor Schwanzrisiken in Bitcoin zu entwickeln. Das dritte Kapitel präsentiert die Portfolio-Allokationsmethode NMFRB, die durch eine Dimensionsreduktionstechnik hohe Dimensionen bewältigt. Im Vergleich zu klassischen Machine-Learning-Methoden zeigt NMFRB in zwei Universen überlegene risikobereinigte Renditen. Das letzte Kapitel kombiniert bisherige Ansätze zu einer Schwanzrisikoschutzstrategie für Portfolios. Die erweiterte NMFRB berücksichtigt Schwanzrisikomaße, behandelt nicht-lineare Beziehungen zwischen Vermögenswerten während Schwanzereignissen und entwickelt eine dynamische Schwanzrisikoschutzstrategie unter Berücksichtigung der Nicht-Stationarität der Vermögensrenditen. Die vorgestellte Strategie reduziert erfolgreich große Drawdowns und übertrifft andere moderne Schwanzrisikoschutzstrategien wie die Value-at-Risk-Spread-Strategie. Die Ergebnisse werden durch verschiedene Data-Snooping-Tests überprüft. / This dissertation shows the potential of machine learning methods for managing tail risk in a non-stationary and high-dimensional setting. For this, we compare in a robust manner data-dependent approaches from parametric or non-parametric statistics with data-adaptive methods. As these methods need to be reproducible to ensure trust and transparency, we start by proposing a new platform called Quantinar, which aims to set a new standard for academic publications. In the second chapter, we dive into the core subject of this thesis which compares various parametric, local parametric, and non-parametric methods to create a dynamic trading strategy that protects against tail risk in Bitcoin cryptocurrency. In the third chapter, we propose a new portfolio allocation method, called NMFRB, that deals with high dimensions thanks to a dimension reduction technique, convex Non-negative Matrix Factorization. This technique allows us to find latent interpretable portfolios that are diversified out-of-sample. We show in two universes that the proposed method outperforms other classical machine learning-based methods such as Hierarchical Risk Parity (HRP) concerning risk-adjusted returns. We also test the robustness of our results via Monte Carlo simulation. Finally, the last chapter combines our previous approaches to develop a tail-risk protection strategy for portfolios: we extend the NMFRB to tail-risk measures, we address the non-linear relationships between assets during tail events by developing a specific non-linear latent factor model, finally, we develop a dynamic tail risk protection strategy that deals with the non-stationarity of asset returns using classical econometrics models. We show that our strategy is successful at reducing large drawdowns and outperforms other modern tail-risk protection strategies such as the Value-at-Risk-spread strategy. We verify our findings by performing various data snooping tests.

This study delves into the media coverage of cannabis by analyzing articles from Aftonbladet and Expressen between 2010 and 2024. Through a combination of quantitative and qualitative methods, the study examines the tone, content, and trends in reporting on cannabis through these years. A total of 904 articles were analyzed, with a focus on identifying patterns, trends, and shifts in the portrayal of cannabis in the two newspapers. The quantitative content analysis involves coding and categorizing a large sample of articles to uncover key themes and frequencies of coverage. Additionally, a qualitative discourse analysis is conducted on a subset of texts to deepen the understanding of how cannabis is framed and represented in the media. The methodological rigor includes inter-coderreliability checks and statistical evaluations to ensure consistency and validity in the analysis process. The findings reveal a predominance of negative articles, with a particular focus on domestic issues in Sweden such as increasing gang violence. The study also highlights the influence of media discourse in shaping perceptions of cannabis, portraying it both as an illegal commodity associated with violence and as a symbol of deviance. By scrutinizing the language and narratives used in media representations, this research sheds light on the construction of cannabis in Swedish newspapers and the potential impact on public attitudes and policies.

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“The Purloined Letter,” Edgar Allan Poe's third and final tale featuring the detective Dupin, has evoked a long history of critical response. Criticism has tended to read the text for its role in the development of detective fiction and as illustrative of various theoretical positions. However, the implications of the “The Purloined Letter,” as a tale of ratiocination, has largely been left unexplored. “The Purloined Letter” explores logical processes of value and exchange, particularly economic exchange, in a manner very similar to what Charles Sanders Peirce will call pragmatism several decades later. Dupin's deductive methods and Peirce's abductive logic express the nature of objects in terms of social systems of preference and perception rather metaphysics. Peirce's classification of signs as icon, index, or symbol provides a framework of signification which can be read in conjunction with “The Purloined Letter” to flesh out the role of materiality and value in the theory of economic criticism. Reading value and exchange as part of a social system of signs, perceptions, and representations of value will serve to expose a penchant for material fetishism in economic criticism and provide a theory of currency, value, and exchange that contextualizes representational and material notions of value within the social and economic system that provides the processes and mechanisms of value determination. The way that the Prefect, the Minister D___, and Dupin each conceptualize the purloined letter as having a different representational relationship with value can be used to demonstrate Poe's abductive framework for economy.

<p dir="ltr">Accurately assessing outcomes of students’ motivation when solving complex food system problems through integrating STEM learning can describe their learning experiences and help teachers make relevant connections. This study shows high school students self-reported that they were motivated by solving food system STEM projects.</p><p dir="ltr">The purpose of the study was to predict high school students’ self-efficacy (confirmed by Exploratory Factor Analysis, EFA) based on Situated Expectancy-Value Theory (SEVT) variables (confirmed by EFA). The convenience sample for this study was students from four high schools in Indiana (<i>N</i> = 160) who had participated in food system STEM projects at their schools. Quantitative data was collected using the Food System Motivation Questionnaire containing 41 items related to two self-efficacy variables and five SEVT variables. Quantitative data were analyzed using Principal Components Analysis, descriptive statistics, simple linear correlations, and multiple regression. Qualitative data were collected using a focus group interview protocol (Appendix D) and analyzed using thematic coding (deductive) followed by pattern coding. Quantitative and qualitative findings were analyzed using triangulation.</p><p dir="ltr">There were four conclusions to this study. First, the Food System Motivation Questionnaire accurately and reliably measured five variables aligned with SEVT motivation. Second, students were motivated regarding the project’s usefulness in their local contexts and reported higher cultural project self-efficacy after completing the project.<b> </b>Third, over 70% of high school students’ cultural project self-efficacy to complete a food system STEM project can be predicted based on their local context utility value, personal importance and usefulness, intrinsic value, and cost value. Local context utility value was the highest contributor of unique variance. Last, <a href="" target="_blank">after completing the food system STEM projects, urban high school students shared they made connections to their families, local and global community contexts, and future careers and applications. </a>Implications regarding how teachers can motivate high school students to solve food system STEM projects were discussed.</p>

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