Optimal portfolios have historically been computed using standard deviation as a risk measure.However, extreme market events have become the rule rather than the exception. To capturetail risk, investors have started to look for alternative risk measures such as Value-at-Risk andConditional Value-at-Risk. This research analyzes the financial model referred to as Markowitz 2.0 and provides historical context and perspective to the model and makes a mathematicalformulation. Moreover, practical implementation is presented and an optimizer that capturesthe risk of non-extreme events is constructed, which meets the needs of more customized investment decisions, based on investment preferences. Optimal portfolios are generated and anefficient frontier is made. The results obtained are then compared with those obtained throughthe mean-variance optimization framework. As concluded from the data, the optimal portfoliowith the optimal weights generated performs better regarding expected portfolio return relativeto the risk level for the investment.

This thesis explores the integration of sustainability into Modern Portfolio Theory (MPT) optimization by introducing stepwise filtration and optimization. This study acknowledges the growing importance of sustainability in investment strategies and modifies the traditional MPT framework to include environmental, social, and governance (ESG) factors. A systematic filtration process is conducted where each asset undergoes a step-by-step filtration based on different ESG criteria. For each filtration step, portfolio optimization is performed to find the most efficient portfolios under the filtered criteria. The effect of each filtration on portfolio risk and return profile provides insights into the trade-offs between financial performance and sustainability impacts. The findings indicate that investors considering the ethical aspects of ESG can achieve these goals without significantly affecting the portfolio risk and return. However, investors incorporating all aspects of ESG will experience a higher drop in the efficient frontier. Moreover, while investigating an additional index, including more companies, investors can attain a better portfolio profile while incorporating all aspects of ESG. A central ambition of this study has been enlighten investors regarding the different aspects of ESG and the trade-offs of integrating sustainability into investment practices. Thus, this study seeks to refine the investor decision-making process and encourage investors to make more informed sustainable decisions. / Detta examensarbete undersöker hur hållbarhet kan integreras i Modern Portfolio Theory (MPT) genom att introducera en stegvis filtrering och optimering. Denna studie framhäver den växande betydelsen av hållbarhet inom investeringsstrategier och modifierar det tradionella ramverket för MPT för att inkludera miljön, socialt ansvar och bolagsstyrning (ESG). En systematisk filtreringsprocess genomförs där varje tillgång genomför en iterativ filtrering baserad på ESG-kriterier. För varje filtreringssteg utförs portföljdoptimering för att hitta de mest effektiva portföljerna under den filtrerade kriterierna. Denna process ger insikt i avvägningarna mellan finansiell avkastning och hållbarhetseffekter. Resultaten indikerar att investerare som beaktar de etiska aspekter kan uppnå dessa mål utan att nämnvärt påverka portföljens risk och avkastningsprofil. Dock, investerare som beaktar samtliga aspekter av ESG kommer att uppleva en större minskning av den effektiva fronten. Dessutom kan investerare, genom utforskning av ett kompletterande index som innefattar ett större urval av företag, uppnå en förbättrad portföljprofiler samtidigt som alla ESG-aspekter beaktas. En central ambition för denna studie har varit att upplysa investerare om de olika aspekterna av ESG och avvägningarna med att integrera hållbarhet i investeringspraxis. Således, strävar denna studie efter att förbättra investerarnas beslutprocess och uppmuntra investerare till att fatta med informerade hållbara beslut.

Purpose: This thesis investigates the relationship concerning traditional safe haven assets, gold and US 10-year treasury bonds during periods of market instability, specifically during the economic concerns raised by the COVID-19 pandemic. It assesses the hedging and safe haven properties of these assets and their dynamic nature throughout two periods of unconventional monetary and fiscal policy measures by the Federal Reserve & US Congress respectively. Furthermore, the study explores a unique divergence between the price movements of the two assets, as well as potential changes in their properties and relationships. Theoretical Perspective: The study is anchored in theoretical concepts based on previous research such as Modern Portfolio Theory, Safe Haven Theory and Hedging Theory. These theories explain asset behaviours during financial turmoil and the relationship between gold and US 10-year treasury bonds during financial crises. The research gap and research questions were formulated based on the information gathered. Methodology: The research employs a quantitative, explanatory approach, anchoredin objectivism and realism, focusing on testing established theories through empirical data. Using a deductive methodology, it investigates potential changes in the dynamic between traditional safe haven assets, gold and US 10-year treasury bonds. Empirical Foundation: Based on a thorough literature review, this study integrates insights from past research and with new data emerging from the pandemic's influence on financial markets and subsequent policy action. The empirical evidence is integrated through quantitative analysis, leveraging ARCH/GARCH models and quantile regression to understand asset performance amid market shocks and policy changes. Conclusion: The findings indicate that gold did not initially act as a hedge against bonds but did so against other assets such as Oil, USD, and BTC during the height of COVID-19. In the recovery phase, this relationship shifted, with gold emerging as a hedge against bonds while its hedging capacity against Oil and Real Yield was negated. Additionally, gold's role as a safe haven against bonds was consistently unsupported across both periods studied. Furthermore, a portfolio analysis revealed a shift in investment strategy, from a balanced gold-bonds mix during the crisis to a sole preference for gold in the recovery phase, adapting to the evolving market conditions and policy changes.

The Covid-19 pandemic influenced the world with quarantines, travel bans, social distancing, and much more. Most remarkably, it brought the economy to a steep recession with changes in customer behavior and shocks to the financial markets. Combined with this, sustainable and green investing have grown in importance for firms that aim to incorporate sustainable practices in their businesses. This thesis evaluates the relationship between ESG and financial performance for Swedish issued mutual funds in the periods before, during, and after the covid pandemic. To capture the returns this study uses the Sharpe ratio, which penalizes volatility in the turns of funds and outputs risk-adjusted returns. The findings of this thesis highlight the importance of integrating non-financial metrics into portfolio management. The results found evidence that there is a difference in the performance between the two fund portfolios. Furthermore, this study will investigate the impact ESG variables have on the risk-adjusted rate of return. The results were that there is no compelling evidence of ESG variables impacting the returns. Moreover, significant insights from this study can be made to the Modern Portfolio Theory and Stakeholder Theory. By using Modern Portfolio Theory as the governing theory, a theoretical discussion has been made about the relationship between green investments and financial performance in times of sound economic markets and under financial crisis. This study is a quantitative study that has adopted the deduction approach. The authors of this thesis have retrieved 2 different portfolios, ESG and non-ESG, and compared the two groups against each other. 3 different periods have also been identified in the thesis: pre, during, and post Covid. The performance of the two portfolios has been examined during the three different stages to see how they have performed with hopes of finding empirical evidence that investing in ESG practices really is profitable. Statistical models such as OLS, heteroskedasticity, and more have been used in the study with the aim of helping the authors reach a conclusion.

High-dimensional data such as multi-modal neuroimaging data and large-scale networks carry excessive amount of information, and can be used to test various scientific hypotheses or discover important patterns in complicated systems. While considerable efforts have been made to analyze high-dimensional data, existing approaches often rely on simple summaries which could miss important information, and many challenges on modeling complex structures in data remain unaddressed. In this proposal, we focus on analyzing structured high-dimensional data, including functional data with important local regions and network data with community structures. The first part of this dissertation concerns the detection of ``important'' regions in functional data. We propose a novel Bayesian approach that enables region selection in the functional data regression framework. The selection of regions is achieved through encouraging sparse estimation of the regression coefficient, where nonzero regions correspond to regions that are selected. To achieve sparse estimation, we adopt compactly supported and potentially over-complete basis to capture local features of the regression coefficient function, and assume a spike-slab prior to the coefficients of the bases functions. To encourage continuous shrinkage of nearby regions, we assume an Ising hyper-prior which takes into account the neighboring structure of the bases functions. This neighboring structure is represented by an undirected graph. We perform posterior sampling through Markov chain Monte Carlo algorithms. The practical performance of the proposed approach is demonstrated through simulations as well as near-infrared and sonar data. The second part of this dissertation focuses on constructing diversified portfolios using stock return data in the Center for Research in Security Prices (CRSP) database maintained by the University of Chicago. Diversification is a risk management strategy that involves mixing a variety of financial assets in a portfolio. This strategy helps reduce the overall risk of the investment and improve performance of the portfolio. To construct portfolios that effectively diversify risks, we first construct a co-movement network using the correlations between stock returns over a training time period. Correlation characterizes the synchrony among stock returns thus helps us understand whether two or multiple stocks have common risk attributes. Based on the co-movement network, we apply multiple network community detection algorithms to detect groups of stocks with common co-movement patterns. Stocks within the same community tend to be highly correlated, while stocks across different communities tend to be less correlated. A portfolio is then constructed by selecting stocks from different communities. The average return of the constructed portfolio over a testing time period is finally compared with the SandP 500 market index. Our constructed portfolios demonstrate outstanding performance during a non-crisis period (2004-2006) and good performance during a financial crisis period (2008-2010). / PHD / High dimensional data, which are composed by data points with a tremendous number of features (a.k.a. attributes, independent variables, explanatory variables), brings challenges to statistical analysis due to their “high-dimensionality” and complicated structure. In this dissertation work, I consider two types of high-dimension data. The first type is functional data in which each observation is a function. The second type is network data whose internal structure can be described as a network. I aim to detect “important” regions in functional data by using a novel statistical model, and I treat stock market data as network data to construct quality portfolios efficiently

As the world faces unprecedented environmental challenges, there is an urgent need for largescale investments in green infrastructure and technologies. If we are going to achieve carbon neutrality, significant investments are necessary, and therefore must the entire financial system unite and endorse sustainable investment activities in a market-oriented manner.   A green bond is a relatively new type of bond. It was first introduced in 2007 by the European Investment Bank (EIB). This was followed up by a collaboration between Skandinaviska Enskilda Banken (SEB) and the World Bank, a group of Swedish investors, pension funds, and SRI-focused investors. They issued their first green bond in 2008 intending to attract more investors. However, this attempt to increase the interest did not work, green bonds were almost nonexistent until 2013. One explanation for the slow development of the green bond market was the financial crisis in 2008. Further, the reason for the low interest in green bonds during this period was that traditional investors deemed these risky and non-profitable.  Using a deductive approach, this thesis investigates how green bonds perform compared to conventional bonds and stocks from the issuing company. The authors sampled green and conventional bonds from 33 companies that matured from 2018 to 2023. The sample data set contains bonds from Asia, Europe, South America, North America, and Australia. The data was tested using multiple hypotheses.  This thesis sets out to answer the research question: How do green bonds perform compared to conventional bonds and stocks?   The results indicated there is a significant difference between the three asset types. First, the stocks yield higher returns and higher standard deviations than green and conventional bonds. Second, the authors found no evidence for a difference in return thus a significant difference in standard deviation. The results also suggest there is a difference in modified duration, convexity, maturity, and yield to maturity. These findings indicate that green bonds performed better than conventional bonds, especially regarding risk and volatility. Therefore, could green bonds be useful when diversifying a portfolio.  The findings suggested that a portfolio composition that combines the three assets could be in line with both shareholder theory and stakeholder theory. The portfolio theory also provides interesting insights into the potential portfolio optimizations since there are differences between green and conventional bonds. Since no difference in the return was found for green and conventional bonds the authors find no reason to support the idea of herding behavior in the trading of green bonds.  However, the difference in standard deviation is interesting from a behavioral perspective, a lower standard deviation indicates that the green bond experiences lower volatility compared to conventional bonds.

Collective investments have become a very popular investment vehicle in South Africa because it is, among other things, transparent, liquid and easily accessible. Growing investor knowledge, good market returns and its suitability for diversification, which minimizes risk, also contributes to its popularity. A value chain that adds value to the investor has developed around the collective investment scheme. The role players in this chain include the investment manager, the management company and financial intermediaries. The growth in this part of the collective investment industry has been so dynamic that regulation and the introduction of various new intermediary layers are constantly affecting the value chain and the value added for the investor. Research was conducted to assess the impact of the value chain on the behaviour of the individual investor and the effect this has on wealth creation. The literary review established that the environment surrounding this dynamic and interdependent value chain is well-regulated and that costs and investor behaviour could have a significant impact on investment returns. The empirical study revealed that the average individual investor recognizes the impact of the value chain on his investment, but perceives himself as being knowledgeable enough to avert ineffectiveness in the chain by ensuring desired investment returns through good investment decisions. Over-diversification and irresponsible switching between funds by the investor can, however, destroy value and negate the effect of long-term returns. / Business Management / M. Com. (Business Management)

The recent global financial crisis, regulatory and compliance requirements placed on organisations, and the need for scientific research in the project portfolio management discipline were factors that motivated this research. The interest and contribution to the body of knowledge in project portfolio management has been growing significantly in recent years, however, there still appears to be a misalignment between literature and practice. A particular area of concern is the decision-making, during the management of the portfolio, regarding which projects to accelerate, suspend, or terminate. A lack of determining the individual and cumulative contribution of projects to strategic objectives leads to poorly informed decisions that negate the positive effect that project portfolio management could have in an organisation. The focus of this research is, therefore, aimed at providing a mechanism to determine the individual and cumulative contribution of projects to strategic objectives so that the right decisions can be made regarding those projects. This thesis begins with providing a context for project portfolio management by confirming a definition and providing a theoretical background through related theories. An investigation into the practice of project portfolio management then provides insight into the alignment between literature and practice and confirms the problem that needed to be addressed. A conceptual model provides a solution to the problem of determining the individual and cumulative contribution of projects to strategic objectives. The researcher illustrates how the model can be extended before verifying and validating the conceptual model. Having the ability to determine the contributions of projects to strategic objectives affords decision makers the opportunity to conduct what-if scenarios, enabled through the use of dashboards as a visualization technique, in order to test the impact of their decisions before committing them. This ensures that the right decisions regarding the project portfolio are made and that the maximum benefit regarding the strategic objectives is achieved. This research provides the mechanism to enable better-informed decision- making regarding the project portfolio. / Computing / D. Phil. (Computer science)

The Nuclear Waste Fund constitutes a financial system that finances future costs of the management of spent nuclear fuel as well as decommissioning of nuclear power plants. The fund invests its capital under strict rules which are stipulated in the investment policy established by the board. The policy stipulates that the fund can only invest according to certain allocation limits, and restricts it to invest solely in nominal and inflation-linked bonds issued by the Swedish state as well as treasury securities. A norm portfolio is built to compare the performance of the NWF’s investments. On average, the NWF has outperformed the norm portfolio on recent years, but it may not always have been optimal. Recent studies suggest that allocation limits should be revised over time as the return and risk parameters may change over time. This study focused on simulating three different portfolios where the allocation limits and investment options were extended to see if these extensions would outperform the norm portfolio while maintaining a set risk limit. Portfolio A consisted of OMRX REAL and OMRX TBOND indexes, Portfolio B consisted of OMRX REAL, OMRX TBOND and S&P Sweden 1+ Year Investment Grade Corporate Bond Indexes, and Portfolio C consisted of OMXR REAL, OMRX TBOND and OMXSPI indexes. The return of each portfolio for different weight distributions of the assets were simulated in MATLAB, and polynomial regression models were built in order to optimize the return as a function of the assets’ weights using a Lagrange Multiplier approach for each portfolio. The results depicted that the maximal returns of Portfolios A, B and C were 4.00%, 4.13% and 7.93% respectively, outperforming the norm portfolio’s average return of 3.69% over the time period 2009-2016.

The aim of this study was to assess the feasibility of constructing optimal portfolios using the Johannesburg Securities Exchange tradable sector indices. Three indices were employed, namely Financials, Industrials and Resources and were benchmarked against the JSE All Share Index for the period January 2007 to December 2017. The period was split into three, namely before the 2007-2009 global financial crises, during the global financial crises and after the global financial crises. The Markowitz’s mean-variance optimisation framework was employed for the construction of global mean variance portfolios. The results of this study showed that it was feasible to construct mean-variance efficient portfolios using tradable sector indices from the Johannesburg Securities Exchange. It was also established that, on the other hand, global mean variance portfolios constructed in this study, outperformed the benchmark index in a bullish market in terms of the risk-return combinations. On the other hand, in bear markets, the global mean variance portfolios were observed to perform better than the benchmark index in terms of risk. Further, the results of the study showed that portfolios constructed from the three tradable indices yielded diversification benefits despite their positive correlation with each other. The results of the study corroborate the findings by other scholars that the mean-variance optimisation framework is effective in the construction of optimal portfolios using the Johannesburg Securities Exchange. The study also demonstrated that Markowitz’s mean-variance framework could be applied by investors faced with a plethora of investment choices to construct efficient portfolios utilising the Johannesburg Securities Exchange tradable sector indices to achieve returns commensurate with their risk preferences. / Business Management / M. Com. (Business Management)