Within the scope of this thesis, the Black-Litterman Asset Allocation Model (as presented in He & Litterman, 1999) is compared to the classical mean-variance framework by simulating past performance of portfolios constructed by both models using identical input data. A quantitative investment strategy which favours stocks with high dividend yield rates is used to generate private views about the expected excess returns for a fraction of the stocks included in the sample. By comparing the ex-post risk-return characteristics of the portfolios and performing ample sensitivity analysis with respect to the numerical values assigned to the input variables, we evaluate the two models’ suitability for different categories of portfolio managers. As a neutral benchmark towards which both portfolios can be measured, a third market-capitalization-weighted portfolio is constructed from the same investment universe. The empirical data used for the purpose of our simulations consists of total return indices for 23 of the 30 stocks included in the OMXS30 index as of the 21st of February 2014 and stretches between January of 2003 and December of 2013.   The results of our simulations show that the Black-Litterman portfolio has delivered risk-adjusted return which is superior not only to that of its market-capitalization-weighted counterpart but also to that of the classical mean-variance portfolio. This result holds true for four out of five simulated strengths of the investment strategy under the assumption of zero transaction costs, a rebalancing frequency of 20 trading days, an estimated risk aversion parameter of 2.5 and a five per cent uncertainty associated with the CAPM prior. Sensitivity analysis performed by examining how the results are affected by variations in these input variables has also shown notable differences in the sensitivity of the results obtained from the two models. While the performance of the Black-Litterman portfolio does undergo material changes as the inputs are varied, these changes are nowhere near as profound as those exhibited by the classical mean-variance portfolio.   In the light of our empirical results, we also conclude that there are mainly two aspects which the portfolio manager ought to consider before committing to one model rather than the other. Firstly, the nature behind the views generated by the investment strategy needs to be taken into account. For the implementation of views which are of an α-driven character, the dynamics of the Black-Litterman model may not be as appropriate as for views which are believed to also influence the expected return on other securities. Secondly, the soundness of using market-capitalization weights as a benchmark towards which the final solution will gravitate needs to be assessed. Managers who strive to achieve performance which is fundamentally uncorrelated to that of the market index may want to either reconsider the benchmark weights or opt for an alternative model.

Proliferation of non-linear, single-phase power electronics loads, such as personal computers, television sets, CFLs, has resulted in thousands of individual small harmonic current injectors connected to a distribution feeder network. Harmonic standard: IEC 1000-3-2 classifies such loads as Class D, “low-voltage” equipment with current emissions limited to 16A/Phase. Individual harmonic contributions of such loads appear insignificant; their collective contribution, however, is a matter of concern. The average order of voltage distortion usually varies between 4-6%; current distortion, however, is usually of the order of 100%. Limitations and high-costs associated with conventional harmonic mitigation measures, has furthered the need for regulation and alternative strategies. The objective of this research is to predict, and mitigate the effects of harmonic proliferation in the main supply current measured at the point of common coupling (PCC). An equivalent circuit model – an aggregation of single phase power electronics loads connected to the distribution feeder network is proposed as a part of a forward solution. Each load, individually, behaves as a harmonic current source; the proposed model combines these individual harmonic current injectors into a single harmonic source connected at the PCC and their collective contribution as a single composite harmonic signal. It represents harmonic conditions at the PCC and provides a theoretical measure of harmonic distortion in the supply current. Such a model finds application during harmonic compliance testing for single-phase power electronics loads; it simulates and predicts the harmonic response of such loads using a theoretical pure 60 Hz sine wave as the supply voltage diffcult to obtain physically, yet critical to such tests. The accuracy of the equivalent circuit model in predicting a harmonic response is pivotal to a successful forward solution. A feed-backwards mechanism is proposed. For a given harmonic supply voltage and circuit configuration of the equivalent circuit model, the feed-backwards method generates the modeled response and compares it to a reference physical response. Finally, it optimizes the circuit configuration to a unique Correction Factor that facilitates an accurate modeled response. Three optimization algorithms, labeled as Response Optimization algorithms have been developed to execute the feed-backwards mechanism. These algorithms are written in FORTRAN-90. / text