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The reaction of asset prices to monetary policy is essential for investors andpolicymakers. However, previous research on the area in Sweden is limited, and there isno evidence of any impact on stock market prices from repo rate changes. This study estimates how stock market indices respond to repo rate changes, including different sector indices. The repo rate is the primary interest rate tool for the Swedish central bank. The utilised methodology is based on previous studies and follows a regression methodology. The paper's findings are that some sectoral stock market indices are affected by changes in repo rate. Bank and Financial sector indexes are positivelyaffected, while Health, Technology, Construction & Materials, Mid Cap, Small Cap,and Financial Services indices are negative. The result is estimated using two different variables for expectations of repo rate changes. The results are justified using a larger sample, including all monetary policy meetings. The results do only have a slight change in coefficients. This paper can be used to further investigate the impact of monetary policy on asset prices in Sweden.

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Yes / In this paper, a stochastic method is proposed to assess the amount of active and reactive power that can be injected/absorbed to/from grid within a distribution market environment. Also, the impact of wind power penetration on the reactive and active distribution-locational marginal prices is investigated. Market-based active and reactive optimal power flow is used to maximize the social welfare considering uncertainties related to wind speed and load demand. The uncertainties are modeled by Scenario-based approach. The proposed model is examined with 16-bus UK generic distribution system. / Supported by the Higher Education Ministry of Iraqi government.

This study examines the impact of the Mini-Holland Programme, an active travelinfrastructure intervention, on residential housing prices in Outer London boroughs.The programme, aimed at promoting walking and cycling, may have unintendedeconomic consequences. Employing a Difference-in-Differences approach and anevent study design, I analyse housing price data from 2002 to 2022. My findingsindicate a statistically significant 10% increase in housing prices in boroughs affectedby the programme, with the effect becoming evident post-announcement in 2014.These results suggest that while active travel infrastructure can enhance urbanmobility and health, it may also contribute to rising housing costs, potentiallyexacerbating socio-economic disparities. Further research at the micro-level isrecommended to fully understand the socio-economic long-term and distributionaleffects, while policymakers must aim for the benefits of improved infrastructure whilelimiting potential displacement effects on lower-income residents.

In this thesis, we study three revenue management problems where we propose simple algorithms with provable guarantees. While online marketplaces provide retailers with tremendous flexibility, they are often large, noisy, have multiple stakeholders, and could be more challenging to characterize. These complexities give rise to a preference for simple, interpretable policies. Further, traditional marketplaces such as brick-and-mortar stores cannot always leverage tools designed for online environments due to physical constraints, higher latency, etc. With these motivations in mind, we develop algorithms for assortment optimization and pricing that are easy to implement in practice and have theoretical justifications for their performance. In Chapter 1, we consider a dynamic assortment optimization problem where the seller has a fixed inventory of multiple substitutable products to sell over a fixed time horizon. We consider two modifications to the traditional problem. First, we simplify the assortment planning by restricting assortment changes to "product retirements". When a product is retired, it becomes unavailable to all future customers. Second, we assume the seller has flexibility regarding which customers to approach. In each period, the seller chooses which subset of products to retire and selects a customer to visit. The selected customer then receives an option to purchase one of the available products, i.e., non-retired products with positive remaining inventory. We provide two policies for this problem. Our first policy guarantees a constant fraction of the best possible revenue. Our second policy is near-optimal but requires the problem to have a specific structure. In Chapter 2, we study the fundamental joint pricing and inventory management problem. The optimal policy for the model we consider is known to be an (s, S, p) policy: when the inventory level drops to s units, the seller immediately places an order to replenish the inventory to S units. Specifically, the optimal pricing policy p has a different price for every inventory state. We proposed simple policies requiring no more than three prices and prove that these policies are near-optimal compared to optimal policies which require more prices and are less robust. In particular, when orders cannot be backlogged, we show that a single price is sufficient for good performance. In Chapter 3, we analyze assortment optimization and pricing with opaque products. An opaque product is one for which only partial information is available to the buyer at the time of purchase. When a customer selects the opaque product, the seller can fulfill the purchase using any of the offered products. Opaque products can help sellers boost total sales. We propose simple policies for assortment optimization with provable constant factor guarantees, which are near-optimal in numerical experiments. We also provide upper bounds for the advantage of selling opaque products.

An effective risk management program requires reliable risk measurement. Failure to assess inherited risks in mortgage-backed securities in the U.S. market contributed to the financial crisis of 2007–2008, which has prompted government regulators to pay greater attention to controlling risk in banks, investment funds, credit unions, and other financial institutions to prevent bankruptcy and financial crisis in the future. In order to calculate risk in a reliable manner, this thesis has focused on the statistical modeling of expected return and volatility. The primary aim of this study is to propose a framework, based on the probabilistic reduction approach, to reliably quantify market risk using statistical models and historical data. Particular emphasis is placed on the importance of the validity of the probabilistic assumptions in risk measurement by demonstrating how a statistically misspecified model will lead the evaluation of risk astray. The concept of market risk is explained by discussing the narrow definition of risk in a financial context and its evaluation and implications for financial management. After highlighting empirical evidence and discussing the limitations of the ARCH-GARCH-type volatility models using exchange rate and stock market data, we proposed Student's t Autoregressive models to estimate expected return and volatility to measure risk, using Value at Risk (VaR) and Expected Shortfall (ES). The misspecification testing analysis shows that our proposed models can adequately capture the chance regularities in exchange rates and stock indexes data and give a reliable estimation of regression and skedastic functions used in risk measurement. According to empirical findings, the COVID-19 pandemic in the first quarter of 2020 posed an enormous risk to global financial markets. The risk in financial markets returned to levels prior to the COVID-19 pandemic in 2021, after COVID-19 vaccine distribution started in developed countries. / Doctor of Philosophy / Reliable risk measurement is necessary for any effective risk management program. Hence, the primary purpose of this dissertation was to propose a framework to quantify market risk using statistical models and historical data, with a particular emphasis placed on checking the validity of probabilistic assumptions underlying models. After discussing the concept of market risk and its evaluation methods in financial management, we explored the empirical evidence in financial data and highlighted some limitations of other well-known modeling approaches. In order to ameliorate limitations, this study proposed Student's t Autoregressive models to estimate the conditional mean and the conditional variance of the financial variables and use them to measure risk via two popular methods: Value at Risk (VaR) and Expected Shortfall (ES). Further investigation shows that our proposed models can adequately model exchange rates and stock indexes data and give reliable estimations to use in risk measurement. We used our model to quantify risk in global financial markets in recent years. The results show that the COVID-19 pandemic posed an enormous risk to global financial markets in the first quarter of 2020. In 2021, the level of risk in financial markets returned to levels before the COVID-19 pandemic, after COVID-19 vaccine distribution started in developed countries.