The main aim of this thesis is in analyzing and empirically testing the various valuation models and hedging schemes of foreign exchange barrier options and their robustness with respect to changing of market conditions. The purpose of the main empirical section is to get a detailed understanding of the static and dynamic performance of the analyzed models for the barrier options payoff mainly in the extreme market conditions, where we performed a benchmarking of the various hedging schemes. As a by-product, we analyzed the accomplishment of some of the model assumptions in real world setting, and the model dependency of the barrier options.

Le maintien ou l'amélioration de la biodiversité est un des objectifs importants de la gestion forestière durable. La flore du sous-bois, qui représente la partie la plus diversifiée de la flore dans les forêts tempérées, joue des rôles écologiques importants. Pourtant, elle pourrait être impactée par l'augmentation de la mécanisation de la gestion forestière. A l'échelle de la parcelle, nous avons étudié en forêt de Montargis les effets simples et combinés de caractéristiques du peuplement et de la surface en cloisonnement sur la diversité floristique du sous-bois (richesse et abondance). Les caractéristiques du peuplement (type de peuplement ou surface terrière des essences à étaient les meilleurs indicateurs de la diversité du sous-bois. La surface des cloisonnements avait un effet négligeable. A plus petite échelle – à l’intérieur du cloisonnement – nous avons étudié la réponse statistique de la diversité du sous-bois à la position dans ou hors du cloisonnement, à des facteurs micro-environnementaux (humidité du sol, compaction du sol, lumière) et aux caractéristiques du peuplement. A cette échelle, les meilleurs modèles incluaient pour les groupes écologiques la position par rapport au cloisonnement, l’humidité du sol et/ou la compaction du sol, selon le groupe écologique considéré. Au niveau espèce, la position par rapport au cloisonnement était le facteur dominant. Globalement, les cloisonnements avaient soit pas d’effet soit un impact positif sur la diversité floristique de sous-bois. Ces résultats ont dépendants du contexte écologique et historique de la forêt de Montargis. L’utilisation d’engins plus lourds ou des passages répétés sur une plus longue période pourraient changer ces conclusions. / Maintaining or improving biodiversity is an important goal of sustainable forest management.Ground flora, which is responsible for most floristic diversity in temperate forests, plays multiple important roles in biodiversity but may be impacted by the increasing mechanisation of forest practices. At stand scale, we investigated in Montargis forest the individual and combined effects of tree stand attributes and skid trail area on ground flora diversity. Tree stand attributes (stand type or basal area) were the best indicators of ground flora diversity, depending on the successional traits or light preference of the species group. The effects of skid trail area were negligible. At finer scale, we studied plant response to skid trail disturbance (represented by subplot on and off skid trails), micro-environmental factors (soil moisture, soil compaction, light) and stand attribute (stand type, basal area). The best models for ecological groups included subplot location, soil moisture or soil compaction, depending on which ecological groups (classified by life form, seed bank persistence, light and moisture requirements) the species belonged to. Stand type as a covariate played a significantly important role in fine-scale diversity pattern. Subplot location was the dominant factor at species level. In conclusion, skid trails had either no impact or a positive impact on ground flora diversity. These results are dependent on the context of Montargis forest (ecological and historical), especially that mechanized harvesting is relatively recent. The employment of heavier machines and increased number of passages is likely to happen. This might induce greater soil compaction and negative effects on plant.

In the study of perceptual decision making, it has been widely assumed that random fluctuations of motion stimuli are irrelevant for a participant’s choice. Recently, evidence was presented that these random fluctuations have a measurable effect on the relationship between neuronal and behavioral variability, the so-called choice probability. Here, we test, in a behavioral experiment, whether stochastic motion stimuli influence the choices of human participants. Our results show that for specific stochastic motion stimuli, participants indeed make biased choices, where the bias is consistent over participants. Using a computational model, we show that this consistent choice bias is caused by subtle motion information contained in the motion noise. We discuss the implications of this finding for future studies of perceptual decision making. Specifically, we suggest that future experiments should be complemented with a stimulus-informed modeling approach to control for the effects of apparent decision evidence in random stimuli.

With the constant development of more advanced speech recognition models, the need to determine which models are better in specific areas and for specific purposes becomes increasingly crucial. Even more so for low-resource languages such as Swedish, dependent on the progress of models for the large international languages. Lagerlöf (2022) conducted a comparative analysis between Google’s speech-to-text model and NLoS’s VoxRex B, concluding that VoxRex was the best for Swedish audio. Since then, OpenAI released their Automatic Speech Recognition model Whisper, prompting a reassessment of the preferred choice for transcribing Swedish. In this comparative analysis using data from Swedish radio news segments, Whisper performs better than VoxRex in tests on the raw output, highly affected by more proficient sentence constructions. It is not possible to conclude which model is better regarding pure word prediction. However, the results favor VoxRex, displaying a lower variability, meaning that even though Whisper can predict full text better, the decision of what model to use should be determined by the user’s needs.

The impact of climate change is increasingly important to the design of urban water infrastructure like stormwater systems, sewage systems and drinking water systems. Growing evidence indicates that the water sector will not only be affected by climate change, but it will reflect and deliver many of its impacts through floods, droughts, or extreme rainfall events. Water resources will change in both quantity and quality, and the infrastructure of stormwater and wastewater facilities may face greater risk of damage caused by storms, floods and droughts. The effect of the climate change will put more difficulties on operations to disrupted services and increased cost of the water and wastewater services. Governments, urban planners, and water managers should therefore re-examine development processes for municipal water and wastewater services and are adapt strategies to incorporate climate change into infrastructure design, capital investment projects, service provision planning, and operation and maintenance. According to the Intergovernmental Panel on Climate Change, the global mean temperature has increased by 0,7 °C during the last 100 years and, as a consequence, the hydrological cycle has intensified with, for example, more acute rainfall events. As urban drainage systems have been developed over a long period of time and design criteria are based upon climatic characteristics, these changes will affect the systems and the city accordingly. The overall objective of this thesis is to increase the knowledge about the climate change impacts on the stormwater system in Al Hillah city/Iraq. In more detail, the objective is to investigate how climate change could affect urban drainage systems specifically stormwater infrastructure, and also to suggest an adaptation plan for these changes using adaptation plans examples from international case studies. Three stochastic weather generators have been investigated in order to understand the climate and climate change in Al Hillah. The stochastic weather generators have been used in different kind of researches and studies; for example in hydrology, floods management, urban water design and analysis, and environmental protection. To make such studies efficient, it is important to have long data records (typically daily data) so the weather generator can generate synthetic daily weather data based on a sound statistical background. Some weather generators can produce the climate change scenarios for different kind of global climate models. They can be used also to produce synthetic data for a site that does not have enough data by using interpolation methods. To ensure that the weather generator is fitting the climate of the region properly, it should be tested against observed data, whether the synthetic data are sufficiently similar. At the same time, the accuracy of the weather generator is different from region to region and depends on the respective climate properties. Testing three weather generators GEM6, ClimGen and LARS-WG at eight climate stations in the region of Babylon governorate/Iraq, where Al Hillah is located, is one of the purposes of the first part of this study. LARS-WG uses a semi-parametric distribution (developed distribution), whereas GEM6 and ClimGen use a parametric distribution (less complicated distribution). Different statistical tests have been selected to compare observed and synthetic weather data for the same kind, for instance, the precipitation and temperature distribution (wet and dry season). The result shows that LARS-WG represents the observed data for Babylon region in a better way than ClimGen, whereas GEM6 seems to misfit the observed data. The synthetic data will be used for a first simulation of urban run-off during the wet season and the consequences of climate change for the design and re-design of the urban drainage system in Al Hillah. The stochastic weather generator LARS is then used to generate ensembles of future weather data using five Global Climate Models (GCMs) that best captured the full range of uncertainty. These Global Climate Models are used to construct future climate scenarios of temperature and precipitation over the region of Babylon Governorate in Iraq. The results show an increase in monthly temperatures and a decrease in the total amount of rain, yet the extreme rain events will be more intense in a shorter time. Changes in the amount, timing, and intensity of rain events can affect the amount of stormwater runoff that needs to be controlled. The climate change calculated projections may make existing stormwater-related flooding worse. Different districts in Al Hillah city may face more frequent stormwater floods than before due to the climate change projections. All the results that have been taken from the Global Climate Models are in a daily resolution format and in order to run the Storm Water Management Model it is important to have all data in a minimum of one hour resolution. In order to fulfill this condition a disaggregation model has been used. Some hourly precipitation data were required to calibrate the temporal disaggregation model; however none of the climate stations and rain gauges in the area of interest have hourly resolution data, so the hourly data from Baghdad airport station have been used for that calibration. The changes in the flood return periods have been seen in the projected climate change results, and a return period will only remain valid over time if environmental conditions do not change. This means that return periods used for planning purposes may need to be updated more often than previously, because values calculated based on the past 30 years of data may become unrepresentative within a relatively short time span. While return periods provide useful guidance for planning the effects of flooding and related impacts, they need to be used with care, and allowances have to be made for extremes that may occur more often than may be expected. In the study area with separated stormwater systems, the Storm Water Management Model simulation shows that the number of surface floods as well as of the floods increases in the future time periods 2050s and 2080s. Future precipitation will also increase both the flooding frequency and the duration of floods; therefore the need to handle future situations in urban drainage systems and to have a well-planned strategy to cope with future conditions is evident. The overall impacts on urban drainage systems due to the increase of intensive precipitation events need to be adapted. For that reason, recommendations for climate change adaptation in the city of Al Hillah have been suggested. This has been accomplished by merging information from the review of five study cases, selected based on the amount and quality of information available. The cities reviewed are Seattle (USA), Odense (Denmark), Tehran (Iran), and Khulna (Bangladesh). / Die Auswirkungen des Klimawandels auf die Gestaltung der städtischen Wasserinfrastruktur wie Regenwasser, Kanalisation und Trinkwassersysteme werden immer wichtiger. Eine wachsende Anzahl von Belegen zeigt, dass der Wassersektor nicht nur durch den Klimawandel beeinflusst werden wird, aber er wird zu reflektieren und liefern viele seiner Auswirkungen durch Überschwemmungen, Dürren oder extreme Niederschlagsereignisse. Die Wasserressourcen werden sich in Quantität und Qualität verändern, und die Infrastruktur von Regen-und Abwasseranlagen kann einer größeren Gefahr von Schäden durch Stürme, Überschwemmungen und Dürren ausgesetzt sein. Die Auswirkungen des Klimawandels werden zu mehr Schwierigkeiten im Betrieb gestörter Dienstleistungen und zu erhöhten Kosten für Wasser-und Abwasserdienstleistungen führen. Regierungen, Stadtplaner, und Wasser-Manager sollten daher die Entwicklungsprozesse für kommunale Wasser-und Abwasserdienstleistungen erneut überprüfen und Strategien anpassen, um den Klimawandel in Infrastruktur-Design, Investitionsprojekte, Planung von Leistungserbringung, sowie Betrieb und Wartung einzuarbeiten. Nach Angaben des Intergovernmental Panel on Climate Change hat die globale Mitteltemperatur in den letzten 100 Jahren um 0,7 °C zugenommen, und in der Folge hat sich der hydrologische Zyklus intensiviert mit, zum Beispiel, stärkeren Niederschlagsereignisse. Da die städtischen Entwässerungssysteme über einen langen Zeitraum entwickelt wurden und Design-Kriterien auf klimatischen Eigenschaften beruhen, werden diese Veränderungen die Systeme und die Stadt entsprechend beeinflussen. Das übergeordnete Ziel dieser Arbeit ist es, das Wissen über die Auswirkungen des Klimawandels auf das Regenwasser-System in der Stadt Hilla / Irak zu bereichern. Im Detail ist das Ziel, zu untersuchen, wie der Klimawandel die Siedlungsentwässerung und insbesondere die Regenwasser-Infrastruktur betreffen könnte. Desweiteren soll ein Anpassungsplan für diese Änderungen auf der Grundlage von beispielhaften Anpassungsplänen aus internationalen Fallstudienvorgeschlagen werden. Drei stochastische Wettergeneratoren wurden untersucht, um das Klima und den Klimawandel in Hilla zu verstehen. Stochastische Wettergeneratoren wurden in verschiedenen Untersuchungen und Studien zum Beispiel in der Hydrologie sowie im Hochwasser-Management, Siedlungswasser-Design- und Analyse, und Umweltschutz eingesetzt. Damit solche Studien effizient sind, ist es wichtig, lange Datensätze (in der Regel Tageswerte) haben, so dass der Wettergenerator synthetische tägliche Wetterdaten erzeugen kann, dieauf einem soliden statistischen Hintergrund basieren. Einige Wettergeneratoren können Klimaszenarien für verschiedene Arten von globalen Klimamodellen erzeugen. Sie können unter Verwendung von Interpolationsverfahren auch synthetische Daten für einen Standort generieren, für den nicht genügend Daten vorliegen. Um sicherzustellen, dass der Wettergenerator dem Klima der Region optimal entspricht, sollte gegen die beobachteten Daten geprüft werden, ob die synthetischen Daten ausreichend ähnlich sind. Gleichzeitig unterscheidet sich die Genauigkeit des Wettergenerator von Region zu Region und abhängig von den jeweiligen Klimaeigenschaften. Der Zweck des ersten Teils dieser Studie ist es daher, drei Wettergeneratoren, namentlich GEM6, ClimGen und LARS-WG, an acht Klimastationen in der Region des Gouvernements Babylon / Irak zu testen. LARS-WG verwendet eine semi-parametrische Verteilung (entwickelte Verteilung), wohingegen GEM6 und ClimGen eine parametrische Verteilung (weniger komplizierte Verteilung) verwenden. Verschiedene statistische Tests wurden ausgewählt, um die beobachteten und synthetischen Wetterdaten für identische Parameter zu vergleichen, zum Beispiel die Niederschlags- und Temperaturverteilung (Nass-und Trockenzeit). Das Ergebnis zeigt, dass LARS-WG die beobachteten Daten für die Region Babylon akkurater abzeichnet, als ClimGen, wobei GEM6 die beobachteten Daten zu verfehlen scheint. Die synthetischen Daten werden für eine erste Simulation des städtischen Run-offs in der Regenzeit sowie der Folgen des Klimawandels für das Design und Re-Design des städtischen Entwässerungssystems in Hilla verwendet. Der stochastische Wettergenerator LARS wird dann verwendet, um Gruppen zukünftiger Wetterdaten unter Verwendung von fünf globalen Klimamodellen (GCM), die das gesamte Spektrum der Unsicherheit am besten abdecken, zu generieren. Diese globalen Klimamodelle werden verwendet, um zukünftige Klimaszenarien der Temperatur und des Niederschlags für die Region Babylon zu konstruieren. Die Ergebnisse zeigen, eine Steigerung der monatlichen Temperaturen und eine Abnahme der Gesamtmenge der Regen, wobei es jedoch extremere Regenereignissen mit höherer Intensivität in kürzerer Zeit geben wird. Veränderungen der Höhe, des Zeitpunkt und der Intensität der Regenereignisse können die Menge des Abflusses von Regenwasser, die kontrolliert werden muss, beeinflussen. Die Klimawandel-Prognosen können bestehende regenwasserbedingte Überschwemmungen verschlimmern. Verschiedene Bezirke in Hilla können stärker von Regenfluten betroffen werden als bisher aufgrund der Prognosen. Alle Ergebnisse, die von den globalen Klimamodellen übernommen wurden, sind in täglicher Auflösung und um das Regenwasser-Management-Modell anzuwenden, ist es wichtig, dass alle Daten in einer Mindestauflösung von einer Stunde vorliegen. Zur Erfüllung dieser Bedingung wurde ein eine Aufschlüsselungs-Modell verwendet. Einige Stunden-Niederschlagsdaten waren erforderlich, um das zeitliche Aufschlüsselungs-Modell zu kalibrieren. Da weder die Klimastationen noch die Regen-Messgeräte im Interessenbereich über stundenauflösende Daten verfügt, wurden die Stundendaten von Flughäfen in Bagdad verwendet. Die Veränderungen in den Hochwasserrückkehrperioden sind in den projizierten Ergebnissen des Klimawandels ersichtlich, und eine Rückkehrperiode wird nur dann über Zeit gültig bleiben, wenn sich die Umweltbedingungen nicht ändern. Dies bedeutet, dass Wiederkehrperioden, die für Planungszwecke verwendet werden, öfter als bisher aktualisiert werden müssen, da die auf Grundlage von Daten der letzten 30 Jahre berechneten Werte innerhalb einer relativ kurzen Zeitspanneunrepräsentativ werden können. Während Wiederkehrperioden bieten nützliche Hinweise für die Planung die Effekte von Überschwemmungen und die damit verbundenen Auswirkungen, müssen aber mit Vorsicht verwendet werden, und Extreme, die öfter eintreten könnten als erwartet, sollten berücksichtigt werden. Im Studienbereich mit getrennten Regenwassersystemen zeigt die Simulation des Regenwasser-Management-Modells, dass sich die Anzahl der Oberflächenhochwasser sowie der Überschwemmungen im Zeitraum 2050e-2080 erhöhen wird. Zukünftige Niederschläge werdensowohl die Hochwasser-Frequenz als auch die Dauer von Überschwemmungen erhöhen. Daher ist die Notwendigkeit offensichtlich, zukünftige Situationen in städtischen Entwässerungssystemen zu berücksichtigen und eine gut geplante Strategie zu haben, um zukünftige Bedingungen zu bewältigen. Die gesamten Auswirkungen auf die Siedlungsentwässerungssyteme aufgrund der Zunahme von intensiven Niederschlagsereignissen müssen angepasst werden. Aus diesem Grund wurden Empfehlungen für die Anpassung an den Klimawandel in der Stadt Hilla vorgeschlagen. Diese wurden durch die Zusammenführung von Informationen aus der Prüfung von fünf Fallstudien, ausgewählt aufgrund der Menge und Qualität der verfügbaren Informationen, erarbeitet,. Die bewerteten Städte sind Seattle (USA), Odense (Dänemark), Teheran (Iran), und Khulna (Bangladesch).

No description available.

Perceptual decision making can be described as a process of accumulating evidence to a bound which has been formalized within drift-diffusion models (DDMs). Recently, an equivalent Bayesian model has been proposed. In contrast to standard DDMs, this Bayesian model directly links information in the stimulus to the decision process. Here, we extend this Bayesian model further and allow inter-trial variability of two parameters following the extended version of the DDM. We derive parameter distributions for the Bayesian model and show that they lead to predictions that are qualitatively equivalent to those made by the extended drift-diffusion model (eDDM). Further, we demonstrate the usefulness of the extended Bayesian model (eBM) for the analysis of concrete behavioral data. Specifically, using Bayesian model selection, we find evidence that including additional inter-trial parameter variability provides for a better model, when the model is constrained by trial-wise stimulus features. This result is remarkable because it was derived using just 200 trials per condition, which is typically thought to be insufficient for identifying variability parameters in DDMs. In sum, we present a Bayesian analysis, which provides for a novel and promising analysis of perceptual decision making experiments.

Probabilistic models of decision making under various forms of uncertainty have been applied in recent years to numerous behavioral and model-based fMRI studies. These studies were highly successful in enabling a better understanding of behavior and delineating the functional properties of brain areas involved in decision making under uncertainty. However, as different studies considered different models of decision making under uncertainty, it is unclear which of these computational models provides the best account of the observed behavioral and neuroimaging data. This is an important issue, as not performing model comparison may tempt researchers to over-interpret results based on a single model. Here we describe how in practice one can compare different behavioral models and test the accuracy of model comparison and parameter estimation of Bayesian and maximum-likelihood based methods. We focus our analysis on two well-established hierarchical probabilistic models that aim at capturing the evolution of beliefs in changing environments: Hierarchical Gaussian Filters and Change Point Models. To our knowledge, these two, well-established models have never been compared on the same data. We demonstrate, using simulated behavioral experiments, that one can accurately disambiguate between these two models, and accurately infer free model parameters and hidden belief trajectories (e.g., posterior expectations, posterior uncertainties, and prediction errors) even when using noisy and highly correlated behavioral measurements. Importantly, we found several advantages of Bayesian inference and Bayesian model comparison compared to often-used Maximum-Likelihood schemes combined with the Bayesian Information Criterion. These results stress the relevance of Bayesian data analysis for model-based neuroimaging studies that investigate human decision making under uncertainty.

Probabilistic models of decision making under various forms of uncertainty have been applied in recent years to numerous behavioral and model-based fMRI studies. These studies were highly successful in enabling a better understanding of behavior and delineating the functional properties of brain areas involved in decision making under uncertainty. However, as different studies considered different models of decision making under uncertainty, it is unclear which of these computational models provides the best account of the observed behavioral and neuroimaging data. This is an important issue, as not performing model comparison may tempt researchers to over-interpret results based on a single model. Here we describe how in practice one can compare different behavioral models and test the accuracy of model comparison and parameter estimation of Bayesian and maximum-likelihood based methods. We focus our analysis on two well-established hierarchical probabilistic models that aim at capturing the evolution of beliefs in changing environments: Hierarchical Gaussian Filters and Change Point Models. To our knowledge, these two, well-established models have never been compared on the same data. We demonstrate, using simulated behavioral experiments, that one can accurately disambiguate between these two models, and accurately infer free model parameters and hidden belief trajectories (e.g., posterior expectations, posterior uncertainties, and prediction errors) even when using noisy and highly correlated behavioral measurements. Importantly, we found several advantages of Bayesian inference and Bayesian model comparison compared to often-used Maximum-Likelihood schemes combined with the Bayesian Information Criterion. These results stress the relevance of Bayesian data analysis for model-based neuroimaging studies that investigate human decision making under uncertainty.

Perceptual decision making can be described as a process of accumulating evidence to a bound which has been formalized within drift-diffusion models (DDMs). Recently, an equivalent Bayesian model has been proposed. In contrast to standard DDMs, this Bayesian model directly links information in the stimulus to the decision process. Here, we extend this Bayesian model further and allow inter-trial variability of two parameters following the extended version of the DDM. We derive parameter distributions for the Bayesian model and show that they lead to predictions that are qualitatively equivalent to those made by the extended drift-diffusion model (eDDM). Further, we demonstrate the usefulness of the extended Bayesian model (eBM) for the analysis of concrete behavioral data. Specifically, using Bayesian model selection, we find evidence that including additional inter-trial parameter variability provides for a better model, when the model is constrained by trial-wise stimulus features. This result is remarkable because it was derived using just 200 trials per condition, which is typically thought to be insufficient for identifying variability parameters in DDMs. In sum, we present a Bayesian analysis, which provides for a novel and promising analysis of perceptual decision making experiments.