In this thesis, a High Efficiency (IE2), 7.5 kW, 4-pole, induction motor (IM) is studied and redesigned using finite element method (FEM) software with the purpose of increasing its efficiency to the new Super Premium Efficiency (IE4) level. First, a pre-existing model of the motor is analyzed and improved. Then, different methods to improve the efficiency of the motor are investigated and the performance of the machine is studied after each modification. Thirdly, a complete optimization of the design is carried out with the goal of developing an IE4 IM compatible with IEC standard frames. Finally, a similar optimization of the motor is performed substituting the aluminum squirrel cage by a copper one. The pros and cons of this change are studied. The results show the feasibility of reaching the IE4 efficiency level by this type of IM with a standard shaft height. Preserving the original cross-section design of the stator, this efficiency level is only reached if a copper squirrel cage is used and if the rotor cross-section is redesigned. However, if the external diameter of the stator is increased and the rotor and stator cross-sections are redesigned, aluminum rotor bars and short-circuit rings can be used to reach the IE4 efficiency level. / I detta examensarbete analyseras en 7.5 kW 4-pol induktionsmotor med verkningsgradsklass IE2 med hjalp av Finita Element Metoden (FEM). Syftet med arbetet ar att utvardera olika metoder for att oka motorns verkningsgrad och att foresla designforandringar som kan mojliggora en uppgradering till verkningsgradsklass IE4. Forst analyseras och justeras en befintlig modell av motorn som sedan anvands for att utvardera effekterna av olika designforandringar. Sedan optimeras motorn for att oka verkningsgraden, detta gors bade med aluminium och med koppar som rotorledarmaterial. Resultaten visar att det ar mojligt att uppna verkningsgradsklassen IE4 genom att anvanda antingen aluminium eller koppar som rotorledarmaterial. For bada fallen kravs att motorns langd okas. Da koppar anvands kravs endast en ny tvarsnittsgeometri for rotorn medan da aluminium anvands behover bade rotor och statorgeometri andras.

Background: Since 1998 Sweden has individual accounts as a part of both public and occupational schemes (Sundén 2006). Yearly, 2,5% of the pensionable income is set aside to the premium pension (The Swedish Pension Agency 2021) Individuals are able to choose how the premiums should be paid in the system and in what way the money should be invested, either by choosing from the fund market or by refraining from making an active choice and instead let the Swedish pension agency management their money in the passive alternative called AP7 Såfa. The passive alternative AP7 Såfa is a portfolio which adapts to the age of the investor and is built to fit a long-term pension investment.  Purpose: This study will focus on evaluating if the passive alternative AP7 Såfa has an excess risk-adjusted return compared to given portfolios in the premium pension selection system, or if the investor would benefit from managing the portfolio more actively. The study will also search for benefits using the Treynor-Black model to check the optimal allocation between this actively managed portfolio versus the passive alternative AP7 Såfa.  Conclusion: This thesis has shown that there might be superior strategies rather than the index fund to find risk-adjusted excess return in the premium pension system. However, those strategies require professional analysts in order to forecast securities. For households choosing between active management themselves or the passive index fund AP7 Såfa, the most beneficial investment is to be passive and to not actively manage the portfolio. The optimum strategy is found to be the Treynor-Black model with a combined portfolio of the index fund and the active portfolio.

This thesis examined the effect of non-financial motives, namely pro-environmental or sustainability preference, in bond pricing on the European secondary market before and during the COVID-19 crisis over the period 02.01.2019-26.02.2021. To estimate the potential yield spread between green bonds and matched conventional bonds, we applied a stringent matching method and fixed-effect regression to explore the green bond premium. The result indicated a small positive premium of 0.46 bps before the COVID-19 (01.2019-02.2020) and a small negative premium of 0.2 bps during the ongoing COVID-19 crisis (03.2020-02.2021), and the premiums have significantly changed between the two study periods, implying that the COVID-19 had a significant effect on the GB premium. Thus, before the pandemic, investors demanded compensation in the form of a higher yield return on investing in green bonds; however, during the pandemic, investors are willing to accept a lower yield on the GBs in comparison to the equivalent CB to finance environmentally-friendly projects. Additionally, the paper investigated bond volatility by analyzing the standard deviation of the daily yield. Although green bonds tended to have a higher volatility, no robust conclusion could be drawn due to a lack of statistical significance.

Ziel der Arbeit ist - ausgehend vom Kreditrisikotransfers von internationalen Konsortialkrediten - die Darstellung der Kalkulation barwertiger Kreditrisikoprämien unter Integration von Adressenausfall- und Residualrisiken. Zentrale Aspekte sind die Identifizierung, Bewertung und Integration von Residualrisiken. Residualrisiken entstehen im Spannungsfeld des Risikotransfers zwischen Kreditgeber (=Risikogeber) und Risikonehmer. Das Adressenausfallrisiko ist der Kernbestandteil des integrierten Kalkulationsmodells. Basierend auf dem versicherungstechnischen Modell zur Kalkulation des Adressenausfallrisikos werden die Bestandteile erwarteter und unerwarteter Verlust berücksichtigt. Das integrierte Kalkulationsmodell hat die Nachhaltigkeit des Risikotransfers als Nebenbedingung. Die faire Risikoprämie, die als Summe aus erwartetem Verlust, unerwartetem Verlust und auf den Risikonehmer entfallenden Residualrisiken berechnet wird, muss der Risikogeber aus der „all-in“ Marge des Konsortialkredites bedienen.

I propose a rare disaster model of an economy where the probability and intensity of climatic disasters are proxied by CO2 levels that are determined by inputs of carbons from the firms in the economy. Disasters affect the budgets, the labor allocations and investment decisions of households; the production and investment decisions of firms; and, monetary policy. Six propositions are developed relating carbon dioxide and climatic economic damages to financial variables: the risk-free rate, the price dividend ratio, and the risk premium. The six propositions are tested empirically using a unique data set for the United States over the period from March 1958 to December 2018. The data support the six propositions. For the strongest results, the carbon dioxide levels in the atmosphere are negatively related in the long run to the risk-free rate. Carbon dioxide levels are positively related to the risk premium in the long run.

The objective of this dissertation is to study the dynamics of size and value risk premia in an equilibrium model with belief dependent preferences and to analyze the impact of investor attention on asset pricing. There is ample evidence that size and value risk premia are non-constant and vary over the business cycle. Empirical patterns, however, are unknown and traditional equilibrium models cannot fit the observed dynamic patterns. The representative agent model with belief dependent preferences is known to fit both unconditional moments such as the equity premium as well as times-series features of volatilities and market prices of risk. The basic model is extended to capture the dynamics of size and value risk premia. The representative agent in this model is a rational Bayesian decision maker who updates her beliefs continuously when new information arrives. However, information processing costs are non-zero and opportunity costs of non-continuous updating of beliefs are higher during times of crisis. In the second part of this dissertation, the representative agent model with beliefs dependent preferences is extended to incorporate the notion of investor attention. The attention version of the model is shown to increase the dynamic fit of equilibrium asset pricing quantities by dampening the volatility of bond yields, market prices of risk, and stock volatility. As such the inattention version of the model with belief dependent preferences is shown to improve the intertemporal fit. Chapter 1 provides a overview of existing studies about the dynamics of size and value risk premia and investor attention. Chapter 2 investigates the dynamic features for size and value risk premia. An asset pricing model with regime dependent risk aversion and incomplete information about economic regimes is introduced to derive closed-form formulas for market prices of risk, asset prices, their volatilities, and risk premia of value and size style indices. Both size and value risk premia vary across normal, recession and boom periods. The premia amplify in recession times but tend to reverse or disappear during boom times. Such findings match the historical performances of small-minus-big (SMB) and high-minus-low (HML) portfolios. Chapter 3 integrates investor attention into regime-switching learning model with regime-dependent risk aversion. The model provides a good fit to the time series of stock volatility, bond volatility and bond yields. Investor attention at the aggregate level is captured by a new representative agent measure which combines the continuously updated beliefs about regimes of a rational Bayesian decision maker with those of a decision maker using steady state regime probabilities. The new representative agent measure can capture the scenario where investor updates her beliefs about economic regimes according to time-varying attention to the available market information. Equilibrium asset pricing quantities are obtained in closed form in the extended model with investor attention. Unconditional asset pricing model moments match their empirical counterparts including the equity premium, the stock volatility and the correlations between stock returns and consumption and dividends. Dynamics features of the data can be well captured. Stock and bond volatilities, bond yield and interest rate time series all have smaller mean square errors compared to the model which does not consider investor attentions. The scale and volatilities for these financial time series are also close to real financial data.

In this magister thesis I will critically analyse the possibility to transfer one’s premium pension rights to one’s spouse or registered partner using Carol Bacchi’s WPR approach. After a short presentation of the Swedish pension system, I will use delve deeper into the policy and discuss what it acknowledges, silences, and contributes to. Several suggestions of improvement are discussed, but there are no obvious solutions to be found. The policy can be questioned from a gender perspective and the conclusion is that it would be wise to take a step back and reflect on the policy before expanding it.

Issuing environmentally aligned green bonds has become an increasingly popular way to raise capital for green investments during the last decade. This thesis explores potential pricing differences between green and conventional bonds, known as the green premium, on the Swedish secondary bond market. Prior green bond research is inconclusive regarding the direction, size and even existence of such a premium. By creating a sample of 50 matched pairs of green and conventional bonds, we show an average positive green premium of 10 bps on the Swedish market, indicating that Swedish green bonds trade at higher yields than their conventional counterparts. We also study whether the size of the green premium is affected by credit ratings and third-party green certification but find no evidence of such effects. Overall, the results from this thesis add to current green bond research by showing a positive green premium, but the lack of shown effects from credit ratings and green certification indicate that further study is needed to fully understand the pricing mechanisms of green bonds.

[pt] A previsão de seguros é essencial para a indústria de seguros e resseguros. Ela fornece subsídios para estratégias de negócios de longo-prazo, e pode servir como um primeiro passo para o planejamento de linhas específicas de produtos. No contexto brasileiro, a previsão de seguros é de especial relevância. O Brasil possui o maior mercado segurador da América-Latina e tem potencial para se tornar um dos mais importantes centros seguradores do mundo no médio-longo- prazo. A SUSEP e a CNseg realizam previsões de carteiras do mercado de seguros brasileiro com base em modelos estatísticos. Entretanto, as séries temporais de prêmios utilizadas para essas previsões exibem comportamento não estacionário e não linear. Assim, a utilização de algoritmos de machine learning, na modelagem de séries de seguros, se justifica em função da habilidade desses algoritmos em capturar componentes de natureza não linear e dinâmica que possam estar presentes nessas séries, sem a necessidade de realizar suposições sobre o processo gerador dos dados. Com base no exposto, este trabalho investiga o uso de redes neurais Echo State (ESN) e GA-SVR na previsão de prêmios de seguros do mercado brasileiro. A base de dados utilizada neste trabalho foi disponibilizada pela SUSEP e compreende as carteiras de Automóveis, Vida e Previdência. Foram realizadas previsões univariadas e multivariadas com ESN e GA-SVR para as três carteiras mencionadas. Os resultados demonstram superioridade preditiva da ESN. / [en] Insurance forecasting is essential for the insurance industry. It provides support for long-term business strategies and can serve as a first-step for planning specific lines of products. In the Brazilian context, insurance forecasting is of special relevance. In the Latin American insurance market, Brazil is the leader in premium, and could become one of the most important insurance centers of the world in the medium- or long-term. SUSEP and CNseg forecast insurance products of the Brazilian market with statistical models. Nevertheless, premium time series exhibit nonstationary and nonlinear behavior. Therefore, the use of machine learning algorithms in the modeling of insurance series is justified, due to the ability of these algorithms in capturing nonlinear and dynamic components, which may be present in those series, without making assumptions about the data generating process. Based on this, this work investigates the use of Echo State neural networks (ESN) and GA-SVR in the forecast of insurance premium of the Brazilian market. The database used in this work was provided by SUSEP and consists of the products Automobiles, Life and Providence. Univariate and multivariate forecasts were made with ESN and GA-SVR for the three aforementioned products. The results show predictive superiority of ESN.

When entering the age of information technology, cash payments were slowly been replaced by other methods including credit cards and online payment applications. On one hand, it brings convenience, on the other hand, people potentially spend more money than before. Previous researchers suggest multiple ways to reduce the overspending behavior on credit card payments, but rarely have these methods been tested in an online payment solution context. This study adopted these methods and categorized them into two groups, visual cues, and financial management mechanics. These two groups of the solution then been translated and develop into three practical prototypes, two of each and one prototype having two groups combined. All prototypes are developed based on a commonly used online payment application named Swish in Sweden. These prototypes are later been tested by ten participants. The results show both groups of solutions have a positive influence to reduce users’ overspending behavior and having them combined provides further complement effects. The study provides empirical data and practical design implications in addressing the overspending issue of an online payment solution.