The fixed income market is not as exploited as other markets and has a more complex structure compared with the equity market. On the other hand, it has been seen that demand for research for the fixed income market has increased, which in turn has created greater interest in studying the characteristics of holdings in the market. This work studies whether it is possible to replicate indices through requirements for credit rating, sectors and mathematical key figures such as Duration, convexity, duration time spread (DTS) and option adjusted spread (OAS). Replication is made through linear programming in the program Python. By implementing lasso regression, this study examines whether it is possible to exceed the return by reducing the requirements for key figures that are not selected efter selection of variables in the regression. The investment company Alfred Berg has provided relevant data for this report. The data consists of information on all assets included in the index EUR Investment grade (ER00) over the period 2017-2021. The result of the replication follows the index returns, with small deviations, and the lasso regression selects the key figures DTS and OAS in its model. It is difficult to excess index return by focusing only on the key figures DTS and OAS. Analysis of other key figures and variables selected by the lasso regression can possibly create better results, as a suggestion for further work. / Räntemarknaden är inte lika exploaterad som andra marknader och har en mer komplex struktur jämfört med aktiemarknaden. Däremot har man sett att efterfrågan på forskning för räntemarknaden har ökat, vilket i sin tur skapat ett större intresse att studera egenskaperna av innehaven på marknaden. Detta arbete studerar om det går att replikera index genom krav på credit rating, sektor och matematiska nyckeltal som Duration, convexity, duration times spread (DTS) och option adjusted spread (OAS). Replikeringen sker genom linjär programmering i programmet Python. Genom att implementera Lasso regression undersöker detta arbete även om det går att överträffa vakastningen genom att minska kraven på nyckeltal som inte väljts ut efter urval av variabler i regressionen. Investmentbolaget Alfred Berg har bidragit med data för denna rapport. Datan består av information om alla tillgångar som ingår i indexet EUR Investment Grade (ER00) under perioden 2017-2021. Resultatet visar att replikeringen av index är möjlig, med små avvikelser, och lasso regressionen väljer nyckeltalen DTS och OAS i sin modell. Det är svårt att överträffa index genom att endast fokusera på nyckeltalen DTS och OAS. Analys av andra nyckeltal och variabler som väljs ut av lasso regressionen kan skapa ett bättre resultat.

Genom att analysera kortegenskaperna hos Magic the Gathering-kort harmodeller tagits fram för att bestämma deras inverkan på kortpriset. Tidigarestudier har inte fokuserat på spel-egenskaperna, vilket är vad som särskiljer dettaarbete från tidigare forskning. För att modellera effekten av spel-egenskapernahar dessa kvantifierats och undersökts med hjälp av Minsta-kvadratmetoden ochLasso-regression, med hjälp av programmeringsspråket R. Resultaten indikeraratt faktorer direkt kopplade till samlarbarhet och spelbarhet har den störstainverkan på priset för Magic the Gathering-kort. Dessa resultat har diskuteratsmed utgångspunkt från olika perspektiv, såsom Wizards of the Coast (utgivarenav Magic the Gathering), spelare, samlare och investerare. Genom att fokusera påspel-egenskaperna har denna studie bidragit till området på ett sätt som tidigareforskning inte har gjort, vilket ger en mer helhetsbild av Magic the Gathering-kortsvärde. / By analyzing the card properties of Magic the Gathering cards, models have beendeveloped to determine their impact on card prices. Previous studies have notfocused on gameplay properties, which distinguishes this work from previousresearch. To model the effect of gameplay properties, they have been quantifiedand examined using Least Squares Method and Lasso Regression, with the helpof the programming language R. The results indicate that factor directly relateradto collectability and playability have the greatest impact on the price of Magic theGathering cards. These results have been discussed from various perspectives,such as Wizards of the Coast (the publisher of Magic the Gathering), players,collectors, and investors. By focusing on gameplay properties, this study hascontributed to the field in a way that previous research has not, providing a morecomprehensive understanding of the value of Magic the Gathering cards.

My dissertation mainly focuses on the regularization of parameters in the multivariate linear regression under different assumptions on the distribution of the errors. It consists of two topics where we develop iterative procedures to construct sparse estimators for both the regression coefficient and scale matrices simultaneously, and a third topic where we develop a method for testing if the skewness parameter in the skew-normal distribution is parallel to one of the eigenvectors of the scale matrix. In the first project, we propose a robust procedure for constructing a sparse estimator of a multivariate regression coefficient matrix that accounts for the correlations of the response variables. Robustness to outliers is achieved using heavy-tailed t distributions for the multivariate response, and shrinkage is introduced by adding to the negative log-likelihood l1 penalties on the entries of both the regression coefficient matrix and the precision matrix of the responses. Taking advantage of the hierarchical representation of a multivariate t distribution as the scale mixture of normal distributions and the EM algorithm, the optimization problem is solved iteratively where at each EM iteration suitably modified multivariate regression with covariance estimation (MRCE) algorithms proposed by Rothman, Levina and Zhu are used. We propose two new optimization algorithms for the penalized likelihood, called MRCEI and MRCEII, which differ from MRCE in the way that the tuning parameters for the two matrices are selected. Estimating the degrees of freedom when penalizing the entries of the matrices presents new computational challenges. A simulation study and real data analysis demonstrate that the MRCEII, which selects the tuning parameter of the precision matrix of the multiple responses using the Cp criterion, generally does the best among all methods considered in terms of the prediction error, and MRCEI outperforms the MRCE methods when the regression coefficient matrix is less sparse. The second project is motivated by the existence of the skewness in the data for which the symmetric distribution assumption on the errors does not hold. We extend the procedure we have proposed to the case where the errors in the multivariate linear regression follow a multivariate skew-normal or skew-t distribution. Based on the convenient representation of skew-normal and skew-t as well as the EM algorithm, we develop an optimization algorithm, called MRST, to iteratively minimize the negative penalized log-likelihood. We also carry out a simulation study to assess the performance of the method and illustrate its application with one real data example. In the third project, we discuss the asymptotic distributions of the eigenvalues and eigenvectors for the MLE of the scale matrix in a multivariate skew-normal distribution. We propose a statistic for testing whether the skewness vector is proportional to one of the eigenvectors of the scale matrix based on the likelihood ratio. Under the alternative, the likelihood is maximized numerically with two different ways of parametrization for the scale matrix: Modified Cholesky Decomposition (MCD) and Givens Angle. We conduct a simulation study and show that the statistic obtained using Givens Angle parametrization performs well and is more reliable than that obtained using MCD.

Tekniska verken är en regional koncern som verkar inom många områden. Den här rapporten specificerar sig på avfallshantering och emissionerna av dessa. Tekniska verken har som mål att bli så miljövänliga som möjligt och med hjälp av denna rapport få en bättre insikt i vilka avfall som är bättre och sämre för miljön. Rapporten använder statistiska metoder för att visa vilka avfall eller bränslen som ger upphov till höga eller låga halter av farliga emissioner samt vilka av dem som har högt respektive lågt energiinnehåll. Metoder som används är Lasso-regression och korsvalidering för variabelselektion. Multipel linjär regression används för tolkning och förklaringsgrad. För kontroll av extremvärden och autokorrelation har Cook ́s distance respektive Durbin-Watson test används. Ett av resultaten som metoderna genererar är att den importerade bränslefraktionen RDFBAL ger upphov till höga vätekloridvärden. Under våren genomfördes en revision, alltså en medveten nedstängning där kraftverket renades och reparerades. Det visar sig att detta påverkar emissionerna både positivt och negativt. / Tekniska verken is a regional corporation involved in many areas. This report has focused on waste management and their emissions. Tekniska verken has as a goal of becoming as environmentally friendly as possible and with the help of this report aim to get better insight in which waste that is better and worse for the environment. The report wishes to show which fuels that produces high or low emissions of hazardous gases and which of those have high or low energy content respectively. Methods used for this purpose are Lasso-regression, cross-validation and multiple linear regression for interpretation and explanation. To control outliers and autocorrelation, Cook’sdistance respectively Durbin-Watson test have been used. One of the results generated by the methods is that the imported fuel fraction “RDFBAL”causes high hydrogen chloride emissions. During the spring, a revision is carried out, that is an intentional shutdown where the power plant is cleaned and repaired. It turned out that this impacted emissions both positively and negatively.

La biologie des systèmes complexes est le cadre idéal pour l'interdisciplinarité. Dans cette thèse, les modèles et les théories statistiques répondent aux modèles et aux expérimentations biologiques. Nous nous sommes intéressés au cas particulier de la leucémie lymphoïde chronique à cellules B, qui est une forme de cancer des cellules du sang. Nous avons commencé par modéliser le programme génique tumoral sous-jacent à cette maladie et nous l'avons comparé au programme génique d'individus sains. Pour ce faire, nous avons introduit la notion de réseau en cascade. Nous avons ensuite démontré notre capacité à contrôler ce système complexe, en prédisant mathématiquement les effets d'une expérience d'intervention consistant à inhiber l'expression d'un gène. Cette thèse s'achève sur la perspective d'une modulation orientée, c'est-à-dire le choix d'expériences d'intervention permettant de « reprogrammer » le programme génique tumoral vers un état normal. / System biology is a well-suited context for interdisciplinary. In this thesis, statistical models and theories closely meet biological models and experiments. We focused on a specific complex system model: the chronic B-cell chronic lymphocytic leukemia disease which is a cancer of the blood cells. We started by modeling the genetic program which underlies this disease and we compared it to the healthy one. This conduced us to introduce the concept of cascade networks. We then showed our ability to control this complex system by predicting with our mathematical model the effects of a gene inhibition experiment. This thesis ends with the perspective of oriented modulation, i.e. targeted interventional experiments on genes allowing to “reprogram” the cancerous genetic program toward a healthy normal state.

This dissertation provides a comprehensive and practical analysis of basis risk in the U.S. variable annuity market and examines effective fund mapping strategies to mitigate the level of basis risk while controlling for the associated transaction costs. Variable annuities are personal savings and investment products with long-term guarantees that expose life insurers to extensive financial risks. Liabilities associated with VA guarantees are the largest liability component faced by U.S. life insurers and have raised concerns to VA providers and regulators. And the hedging performance of these guarantee liabilities is impeded by the existence of basis risk. I look into 1,892 registered VA-underlying mutual funds and two VA separate accounts to estimate the basis risk faced by U.S. VA providers at the individual fund level and the separate account level. To evaluate the degree to which basis risk can be mitigated, I consider various proxy instrument sets and assess different variable selection models. The LASSO regression is shown to be most effective at identifying the most suitable (combination of) mapping instruments that minimize basis risk, compared to other test-based and screening-based models. I supplement it with the Sure Independence Screening (SIS) procedure to further limit the number of instruments requested in the hedging strategies, and modify it by introducing the diff LASSO regression to restrict the changes in instrument allocations across rebalancing periods and, therefore, control for transaction costs. I show that VA providers can reduce their exposure to basis risk by applying data analytic techniques in their mapping process, by hedging with ETFs instead of futures contracts, and through diversification at the separate account level. Combining the traditional fund mapping method with the machine learning algorithm, the proposed portfolio mapping strategy is efficient at reducing basis risk in VA separate accounts while controlling for the tractability and transaction costs of the mapping and hedging procedure, and is practical to incorporate newly-developed VA funds, as well as the varying compositions of separate accounts. Overall, this study presents that U.S. VA providers have the ability to mitigate basis risk to a greater extent than the limited literature on this topic has suggested. / Business Administration/Risk Management and Insurance

When we try to understand why some schools perform worse than others, if Covid-19 has struck harder on some demographics or whether income correlates with increased happiness, we may turn to regression to better understand how these variables are correlated. To capture the true relationship between variables we may use variable selection methods in order to ensure that the variables which have an actual effect have been included in the model. Choosing the right model for variable selection is vital. Without it there is a risk of including variables which have little to do with the dependent variable or excluding variables that are important. Failing to capture the true effects would paint a picture disconnected from reality and it would also give a false impression of what reality really looks like. To mitigate this risk a simulation study has been conducted to find out what variable selection algorithms to apply in order to make more accurate inference. The different algorithms being tested are stepwise regression, backward elimination and lasso regression. Lasso performed worst when applied to a small sample but performed best when applied to larger samples. Backward elimination and stepwise regression had very similar results.

Through an analysis of the Swedish state budget, models have been developed to deepen the understanding of the effects that government expenditures have on reducing crime. This has been modeled by examining selected crime categories using the mathematical methods Ridge Regression, Lasso Regression and Principal Component Analysis. Combined with a qualitative study of previous research on the economic aspects of crime, an analysis has been conducted. The mathematical methods indicate that it may be more effective to invest in crime prevention measures, such as increased social protection and focus on vulnerable groups, rather than more direct efforts such as increased resources for the police force. However, the result contradicts some of the accepted economic conclusions on the subject, as these highlight the importance of increasing the number of police officers and harsher penalties. These do however also mention the importance of crime prevention measures such as reducing the gaps in society, which is in line with the results of this work. The conclusion should however be used with caution as the models are based on a number of assumptions and could be improved upon further analysis of these, together with more data points that would strengthen the validity of the analysis more. / Genom en analys av Sveriges statsbudget har modeller tagits fram för att försöka förstå de effekter olika samhällssatsningar har på brottslighet i Sverige. Detta har modellerats genom att undersöka utvalda brottskategorier med hjälp av de matematiska metoderna Ridge Regression, Lasso Regression samt Principal Component Analysis. Tillsammans med en kvalitativ undersökning av tidigare forskning gällande nationalekonomiska aspekter kring brottslighet har en analys sedan genomförts. De matematiska metoderna tyder på att det kan vara mer effektivt att satsa på brottsförebyggande åtgärder, såsom ökat socialt skydd och fokus på utsatta grupper, istället för mer direkta satsningar på brottsförhindrande åtgärder som exempelvis ökade resurser till polisväsendet. Däremot motsäger resultatet en del av de vedertagna nationalekonomiska slutsatserna om ämnet, då dessa belyser vikten av ökade antalet poliser och hårdare straff. De lyfter även fram vikten av brottsförebyggande åtgärder såsom att minska klyftorna i samhället, vilket går i linje med resultatet av detta arbete. Slutsatsen ska dock användas med försiktighet då modellerna bygger på flertalet antaganden och skulle kunna förbättras vid ytterligare analys utav dessa, tillsammans med fler datapunkter som skulle stärka validiteten.

If customers respond differently to a campaign, it is worthwhile to find those customers who respond most positively and direct the campaign towards them. This can be done by using so called incremental response analysis where respondents from a campaign are compared with respondents from a control group. Customers with the highest increased response from the campaign will be selected and thus may increase the company’s return. Incremental response analysis is applied to the mobile operator Tres historical data. The thesis intends to investigate which method that best explain the incremental response, namely to find those customers who give the highest incremental response of Tres customers, and what characteristics that are important.The analysis is based on various classification methods such as logistic regression, Lassoregression and decision trees. RMSE which is the root mean square error of the deviation between observed and predicted incremental response, is used to measure the incremental response prediction error. The classification methods are evaluated by Hosmer-Lemeshow test and AUC (Area Under the Curve). Bayesian logistic regression is also used to examine the uncertainty in the parameter estimates.The Lasso regression performs best compared to the decision tree, the ordinary logistic regression and the Bayesian logistic regression seen to the predicted incremental response. Variables that significantly affect the incremental response according to Lasso regression are age and how long the customer had their subscription.

Air pollution and meteorological conditions impact COVID-19 mortality rates. This research studied Quito, Ecuador, using Granger causality tests and regression models to investigate the relationship between pollutants, meteorological variables, human mobility, and excess deaths. Results suggested that Mobility as defined by Google Mobility Index, Facebook Isolation Index, in addition to Nitrogen Dioxide, and Sulphur Dioxide significantly impact excess deaths, while Carbon Monoxide and Relative Humidity have mixed results. Measures to reduce Carbon Monoxide emissions and increase humidity levels may mitigate the impact of air pollution on COVID-19 mortality rates. Further research is needed to investigate the impact of pollutants on COVID-19 transmission in other locations. Healthcare decision-makers must monitor and mitigate the impact of pollutants, promote healthy air quality policies, and encourage physical activity in safe environments. They must also consider meteorological conditions and implement measures such as increased ventilation and air conditioning to reduce exposure. Additionally, they must consider human mobility and reduce it to slow the spread of the diseases. Decisionmakers must monitor and track excess deaths during the pandemic to understand the impact of pollutants, meteorological conditions, and human mobility on human health. Public education is critical to raising awareness of air quality and its impact on health. Encouraging individuals to reduce their exposure to pollutants and meteorological conditions can play a critical role in mitigating the impact of air pollution on respiratory health during the pandemic.