This empirical study has shown that optimal portfolios need approximately 10 securities to diversify away the unsystematic risk. This challenges previous studies of randomly chosen portfolios which states that at least 30 securities are needed. The result of this study sheds light upon the difference in risk diversification between random portfolios and optimal portfolios and is a valuable contribution for investors. The study suggests that a major part of the unsystematic risk in a portfolio can be diversified away with fewer securities by using portfolio optimization. Individual investors especially, who usually have portfolios consisting of few securities, benefit from these results. There are today multiple user-friendly software applications that can perform the computations of portfolio optimization without the user having to know the mathematics behind the program. Microsoft Excel’s solver function is an example of a well-used software for portfolio optimization. In this study however, MATLAB was used to perform all the optimizations. The study was executed on data of 140 stocks on NASDAQ Stockholm during 2000-2014. Multiple optimizations were done with varying input in order to yield a result that only depended on the investigated variable, that is, how many different stocks that are needed in order to diversify away the unsystematic risk in a portfolio. / <p>Osäker på examinatorns namn, tog namnet på den person som skickade mejl om betyg.</p>

In this study, we examine the dynamic relationship between tourism growth and economic growth, using a newly introduced spillover index approach. Based on monthly data for 10 European countries over the period 1995-2012, our analysis reveals the following empirical regularities. First, the tourism-economic growth relationship is not stable over time in terms of both magnitude and direction, indicating that the tourism-led economic growth (TLEG) and the economic-driven tourism growth (EDTG) hypotheses are time-dependent. Second, the aforementioned relationship is also highly economic event-dependent, as it is influenced by the Great Recession of 2007 and the ongoing Eurozone debt crisis that began in 2010. Finally, the impact of these economic events is more pronounced in Cyprus, Greece, Portugal and Spain, which are the European countries that have witnessed the greatest economic downturn since 2009. Plausible explanations of these results are provided and policy implications are drawn. (authors' abstract)

Les travaux présentes s'inscrivent dans le cadre de la réduction de variance pour les méthodes de Monte-Carlo et plus généralement dans l'optimisation de méthodes numériques à l'aide de couplage entre des méthodes déterministes et des méthodes probabilistes. Trois thèmes principaux seront abordés à l'aide de ces techniques: l'intégration numérique sur un hypercube, la résolution d' équations aux dérivées partielles linéaires et le calcul des éléments propres principaux (valeur propre et vecteur propre) de certains opérateurs linéaires.

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<p>This thesis focuses on measurement errors that could be ascribed to the interviewers. To study interviewer variability a measurement error model is formulated which makes a clear distinction between three sources of randomness: the sample selection, interviewer assignment, and interviewing. </p><p>In the first paper the variance of the observed sample mean is derived, and it is seen how this variance depends on parameters of the measurement error model and on the number of interviewers. An estimator of the interviewer variance, which is seen to be unbiased, and a biased intra-interviewer correlation estimator are suggested. In a simulation study it is seen that the simulation variance of the interviewer variance estimator increases for both high and low interviewer assignments and seems to have a minimum somewhere in between. </p><p>The second paper presents an expression of the variance of the observed sample mean under stratified random sampling. Two possible estimators of the variance of the mean are considered, one of which has a slight positive bias, the other a negative bias, which can be large. Two different estimators of the interviewer variance are studied. Only one of them makes it possible to find a reasonable estimate of the intra-interviewer correlation. </p><p>In the third paper an expression for the variance of the interviewer variance estimator is derived. This result may prove useful in designing future studies of interviewer variance. For a large population it will be possible to use an approximate variance, irrespective of the underlying distribution of the unknown true values.</p><p>The fourth paper deals with some issues in planning and analyzing an interviewer variance study. Three problems are considered: (i) Determining the number of interviewers and the appropriate size of the interviewer assignments; (ii) Finding the probability of negative estimates of the interviewer variance; (iii) Testing for interviewer variance.</p>

Monte Carlo sampling-based methods are frequently used in stochastic programming when exact solution is not possible. In this dissertation, we develop two sets of Monte Carlo sampling-based algorithms to solve classes of two-stage stochastic programs. These algorithms follow a sequential framework such that a candidate solution is generated and evaluated at each step. If the solution is of desired quality, then the algorithm stops and outputs the candidate solution along with an approximate (1 - α) confidence interval on its optimality gap. The first set of algorithms proposed, which we refer to as the fixed-width sequential sampling methods, generate a candidate solution by solving a sampling approximation of the original problem. Using an independent sample, a confidence interval is built on the optimality gap of the candidate solution. The procedures stop when the confidence interval width plus an inflation factor falls below a pre-specified tolerance epsilon. We present two variants. The fully sequential procedures use deterministic, non-decreasing sample size schedules, whereas in another variant, the sample size at the next iteration is determined using current statistical estimates. We establish desired asymptotic properties and present computational results. In another set of sequential algorithms, we combine deterministically valid and sampling-based bounds. These algorithms, labeled sampling-based sequential approximation methods, take advantage of certain characteristics of the models such as convexity to generate candidate solutions and deterministic lower bounds through Jensen's inequality. A point estimate on the optimality gap is calculated by generating an upper bound through sampling. The procedure stops when the point estimate on the optimality gap falls below a fraction of its sample standard deviation. We show asymptotically that this algorithm finds a solution with a desired quality tolerance. We present variance reduction techniques and show their effectiveness through an empirical study.

Dendrochronological research on Juniperus polycarpos growing in west and central Iran reveals that the radial growth in this species depends mainly on the amount of precipitation in the more arid regions. When the amount of rain is sufficient, i.e. above 450 mm, the prevailing summer temperature seems to become the limiting factor. Favorable conditions which prevailed during the periods 1685-1695 and 1790-1800 resulted in better width growth, while less favorable conditions which prevailed during the years 1725-1735 and 1855-1865 resulted in narrow rings.

Humans are continuously challenged by harmful endogenous and xenobiotic substances. Detoxification is the ability to neutralise and remove these substances from the body. Glycine N-acyltransferase, EC 2.3.1.13 (GLYAT) is a key enzyme in detoxification. GLYAT catalyses an amino acid (glycine) conjugation reaction in phase II of detoxification. It is expected that, similar to what has been observed in the Cytochrome P450 enzymes, variations within the GLYAT gene may lead to altered enzyme activity that may affect the efficacy of detoxification. The aim of this study was to identify genetic variations within the GLYAT gene of a cohort of individuals whose GLYAT activity has been biochemically characterized. Biochemical profiles of phase I and II detoxification of a number of individuals was screened to select those with possible aberrant GLYAT activity. Eighteen selected individuals agreed to participate in the study. The 23.21 kb GLYAT gene of the participants was amplified in four fragments and sent for pyrosequencing (Roche GS FLX titanium) at Inqaba Biotec. The results were analysed with the Lasergene software package from DNAStar (Madison, Wisconsin, USA). A total of 94 variations were identified from the Next Generation Sequencing data. Of these three found in the exons were known variations and four variations located in the exons were novel. A total of 62 known and 25 novel variations were identified in the introns of the GLYAT gene. Sanger sequencing verified 70.29% (68 in total) of the variation, which included 12 novel variations, of which one is located in exon six. Real-time quantitative PCR (qPCR) experiments were conducted and the data analysed using CopyCaller software to identify copy number variations within the cohort. It was found that participant 17 may have multiple copies of parts of the 3-terminal end of the gene (exons five and six), which might have an effect on GLYAT activity. Variations could possibly affect GLYAT activity, but the data was inconclusive and must be confirmed. Some of the variations could possibly affect GLYAT activity, but no correlation could be made between the variations identified during this study and the cohort’s detoxification ability. Further studies needs to be conducted to establish the effect of the variations in combination with one another on GLYAT activity. If some of these variations affect GLYAT activity such data might shed some light on variations observed between the glycine conjugation ability of individuals. Such information could eventually be of value in treatment of inborn errors of metabolism. / MSc (Biochemistry), North-West University, Potchefstroom Campus, 2014

Background: Gender Variance (GV) is an experience that the gender assigned at birth is different to one’s preferred gender identity. It includes the possibility of a preferred gender identity being different to either male or female. It is reported that around 4000 people per year access care from the NHS in relation to GV (Gender Identity Research and Education Society (GIRES), 2009) and both the physical and psychological elements of these experiences is well documented. However, little research specifically explores how Gender Variant (GVt) people make sense of their experiences and construct meaningful expression of their preferred identity. The aim of this study is to further the understanding of GV with a view to considering the implications for service provision to this population. Methodology: The study employed a qualitative method that explored the narratives of the participants. A purposive sample of seven participants self identified as GVt was recruited for a single interview. The interviews used a topic guide to elicit the narratives that these people tell about their experiences. All interviews were audio-recorded, transcribed, and analysed using a narrative approach to explore what the participants said and the way they said it. This was then situated within the local and broader social contexts within which the narratives exist. Analysis and findings: The findings are presented through a global impression of each of the individual narratives and then through discussion of the similarities and differences in relation to the collective storylines. Particular attention is paid to the identity construction and the emotional experiences that take place during the interviews. These two elements are told within and through each of the storylines. The local and wider narratives available to the participants are used to contextualise the analysis and findings, and so are reported within the analysis. The analysis offers the following findings: i) their first experiences of understanding GV was important, leading them to find others who felt the same to gain a sense of hope of a normal life ii) sharing their experiences with others was an anxiety provoking time and was part of a decision making process about treatment and establishing an acceptable gender expression iii) relationships with family, friends, peers and members of their social context influence sense making and identity constructions of GVt people and typify the challenges faced within their GVt experiences. Some of these challenges were reported as ongoing and illustrated throughout the stories of the day to day lives of the participants iv) for these participants distressing emotional challenges were often situated in the past and participants spoke of ‘overcoming’ challenges. This offered a counter to the more dominant isolation and loneliness narratives within the literature on GVt experiences The findings of the study are discussed in relation to its clinical implications, the strengths and limitations of the methodology, and directions for future research.

Engineering design often requires the optimisation of multiple objectives, and becomes significantly more difficult and time consuming when the response surfaces are multimodal, rather than unimodal. A surrogate model, also known as a metamodel, can be used to replace expensive computer simulations, accelerating single and multi-objective optimisation and the exploration of new design concepts. The main research focus of this work is to investigate the use of a neural network surrogate model to improve optimisation of multimodal surfaces. Several significant contributions derive from evaluating the Cascade Correlation neural network as the basis of a surrogate model. The contributions to the neural network community ultimately outnumber those to the optimisation community. The effects of training this surrogate on multimodal test functions are explored. The Cascade Correlation neural network is shown to map poorly such response surfaces. A hypothesis for this weakness is formulated and tested. A new subdivision technique is created that addresses this problem; however, this new technique requires excessively large datasets upon which to train. The primary conclusion of this work is that Cascade Correlation neural networks form an unreliable basis for a surrogate model, despite successes reported in the literature. A further contribution of this work is the enhancement of an open source optimisation toolkit, achieved by the first integration of a truly multi-objective optimisation algorithm.