Measuring the risk of a credit portfolio is a challenge for financial institutions because of the regulations brought by the Basel Committee. In recent years lots of models and state-of-the-art methods, which utilize Monte Carlo simulation, were proposed to solve this problem. In most of the models factors are used to account for the correlations between obligors. We concentrate on the the normal copula model, which assumes multivariate normality of the factors. Computation of value at risk (VaR) and expected shortfall (ES) for realistic credit portfolio models is subtle, since, (i) there is dependency throughout the portfolio; (ii) an efficient method is required to compute tail loss probabilities and conditional expectations at multiple points simultaneously. This is why Monte Carlo simulation must be improved by variance reduction techniques such as importance sampling (IS). Thus a new method is developed for simulating tail loss probabilities and conditional expectations for a standard credit risk portfolio. The new method is an integration of IS with inner replications using geometric shortcut for dependent obligors in a normal copula framework. Numerical results show that the new method is better than naive simulation for computing tail loss probabilities and conditional expectations at a single x and VaR value. Finally, it is shown that compared to the standard t statistic a skewness-correction method of Peter Hall is a simple and more accurate alternative for constructing confidence intervals. (author´s abstract) / Series: Research Report Series / Department of Statistics and Mathematics

The goal of the paper is to compare impacts of interest rate risk measuring meth-ods on capital requirements. The first section identifies methods for measuring interest rate risk and capital requirements for interest rate risk set by regulators. The second section compares capital requirements of model portfolio calculated by using standardized methods as well as internal models.

Stein's method is a powerful technique that can be used to obtain bounds for approximation errors in a weak convergence setting. The method has been used to obtain approximation results for a number of distributions, such as the normal, Poisson and Gamma distributions. A major strength of the method is that it is often relatively straightforward to apply it to problems involving dependent random variables. In this thesis, we consider the adaptation of Stein's method to the class of Variance-Gamma distributions. We obtain a Stein equation for the Variance-Gamma distributions. Uniform bounds for the solution of the Symmetric Variance-Gamma Stein equation and its first four derivatives are given in terms of the supremum norms of derivatives of the test function. New formulas and inequalities for modified Bessel functions are obtained, which allow us to obtain these bounds. We then use local approach couplings to obtain bounds on the error in approximating two asymptotically Variance-Gamma distributed statistics by their limiting distribution. In both cases, we obtain a convergence rate of order n^-1 for suitably smooth test functions. The product of two normal random variables has a Variance-Gamma distribution and this leads us to consider the development of Stein's method to the product of r independent mean-zero normal random variables. An elegant Stein equation is obtained, which motivates a generalisation of the zero bias transformation. This new transformation has a number of interesting properties, which we exploit to prove some limit theorems for statistics that are asymptotically distributed as the product of two central normal distributions. The Variance-Gamma and Product Normal distributions arise as functions of the multivariate normal distribution. We end this thesis by demonstrating how the multivariate normal Stein equation can be used to prove limit theorems for statistics that are asymptotically distributed as a function of the multivariate normal distribution. We establish some sufficient conditions for convergence rates to be of order n^-1 for smooth test functions, and thus faster than the O(n^-1/2) rate that would arise from the Berry-Esseen Theorem. We apply the multivariate normal Stein equation approach to prove Variance-Gamma and Product Normal limit theorems, and we also consider an application to Friedman's X² statistic.

Part one provides a literature review on the development of attitude theory in marketing research and addresses concerns regarding the effects of common method variance (CMV) in published studies based on the reasoned-action paradigm of consumer behavior and decision making. The results of a marker-variable analysis, logit analysis, and reanalysis of path estimates support the validity of self-report survey research designs. Part two employs a survey design to develop and validate a scale to measure a consumer s tendency to use a complex decision style (CDS) in conceptualizing and negotiating high-stakes decision situations. Drawing from literature on cognitive style theory and complexity science, a complex approach to decision making is characterized as being complexity-focused; decision makers tend to rely more heavily on strategies such as collaborating with others and integrating a variety of information. The CDS scale is also applied within a conceptual model of choice of elective healthcare treatment, specifically, women s decisions regarding the use of hormone therapy, commonly referred to as HRT.

Meta-analytic data highlight the value of the Implicit Association Test (IAT) as an indirect measure of personality. Based on evidence suggesting that confounding factors such as cognitive abilities contribute to the IAT effect, this study provides a first investigation of whether basic personality traits explain unwanted variance in the IAT. In a gender-balanced sample of 204 volunteers, the Big-Five dimensions were assessed via self-report, peer-report, and IAT. By means of structural equation modeling (SEM), latent Big-Five personality factors (based on self- and peer-report) were estimated and their predictive value for unwanted variance in the IAT was examined. In a first analysis, unwanted variance was defined in the sense of method-specific variance which may result from differences in task demands between the two IAT block conditions and which can be mirrored by the absolute size of the IAT effects. In a second analysis, unwanted variance was examined in a broader sense defined as those systematic variance components in the raw IAT scores that are not explained by the latent implicit personality factors. In contrast to the absolute IAT scores, this also considers biases associated with the direction of IAT effects (i.e., whether they are positive or negative in sign), biases that might result, for example, from the IAT's stimulus or category features. None of the explicit Big-Five factors was predictive for method-specific variance in the IATs (first analysis). However, when considering unwanted variance that goes beyond pure method-specific variance (second analysis), a substantial effect of neuroticism occurred that may have been driven by the affective valence of IAT attribute categories and the facilitated processing of negative stimuli, typically associated with neuroticism. The findings thus point to the necessity of using attribute category labels and stimuli of similar affective valence in personality IATs to avoid confounding due to recoding.

Social Capital and Open Innovation are important for organisational growth, as both of them influence Innovation, Learning and Job Satisfaction. The literature indicates that informal network measured as social capital and formal network measured as open innovation influences positively to organizational innovation performance, learning and job satisfaction. Most of the studies in this area have been carried out using univariate approaches, and only few dimensions of both Social Capital and Open Innovation have been considered. Current literature outlines the positive influence of Social Capital and Open Innovation on Learning, Innovation and Job Satisfaction. In this study, an attempt has been made to develop a multi-dimensional framework for Social Capital and Open Innovation, in order to better understand the nuances of Learning, Innovation and Job Satisfaction in an R&D setting. Another important factor influencing the R&D outcomes is Absorptive Capacity; it is the capacity of the organisation to identify and use external knowledge. The direct and moderating role of Absorptive Capacity has been examined. Both theoretical and conceptual models have been proposed, and a measurement scale in form of a questionnaire has been developed. Data was collected from 35 organisations across India operating in the field of defence R&D. The sample included Government-run R&D organisations, Public Sector Units (PSUs) and private firms, and total of 331 engineers/scientists responded to the survey. Data was analysed using statistical methods such as Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), Common Method Variance (CMV), and Analysis of Variance (ANOVA). Reliability and validity of the proposed scales for the constructs have been verified using appropriate techniques, and further inferences have been drawn by using regression techniques and Structural Equation Modeling (SEM) to assess the proposed relationships. It has been found that both Social Capital and Open Innovation have positive influence on Learning, Innovation and Job Satisfaction. Further, it was found that Absorptive Capacity had a positive association with both Innovation and Learning. However, Absorptive Capacity did not moderate the relationship of Social Capital with both Innovation and Learning, but was found to moderate the relationship between Open Innovation and Innovation, for both outbound and inbound approaches of Open Innovation. Bonding Social Capital had a relatively stronger positive association with Learning, while Bridging Social Capital was found to have a stronger relationship with Innovation. In summary, networking factors Social Capital and Open Innovation have strong positive association with R&D outcomes measured as innovation performance, learning and job satisfaction.

Meta-analytic data highlight the value of the Implicit Association Test (IAT) as an indirect measure of personality. Based on evidence suggesting that confounding factors such as cognitive abilities contribute to the IAT effect, this study provides a first investigation of whether basic personality traits explain unwanted variance in the IAT. In a gender-balanced sample of 204 volunteers, the Big-Five dimensions were assessed via self-report, peer-report, and IAT. By means of structural equation modeling (SEM), latent Big-Five personality factors (based on self- and peer-report) were estimated and their predictive value for unwanted variance in the IAT was examined. In a first analysis, unwanted variance was defined in the sense of method-specific variance which may result from differences in task demands between the two IAT block conditions and which can be mirrored by the absolute size of the IAT effects. In a second analysis, unwanted variance was examined in a broader sense defined as those systematic variance components in the raw IAT scores that are not explained by the latent implicit personality factors. In contrast to the absolute IAT scores, this also considers biases associated with the direction of IAT effects (i.e., whether they are positive or negative in sign), biases that might result, for example, from the IAT's stimulus or category features. None of the explicit Big-Five factors was predictive for method-specific variance in the IATs (first analysis). However, when considering unwanted variance that goes beyond pure method-specific variance (second analysis), a substantial effect of neuroticism occurred that may have been driven by the affective valence of IAT attribute categories and the facilitated processing of negative stimuli, typically associated with neuroticism. The findings thus point to the necessity of using attribute category labels and stimuli of similar affective valence in personality IATs to avoid confounding due to recoding.

本研究旨在檢證雙元自主性模型中自主能力與情境操弄對訊息區辨表現的範疇優勢性運作機制；並為了排除過去系列相關研究結果可能受到共同方法變異偏誤所造成的效度威脅，本研究採取訊息區辨表現作為非自陳式效標變項，重新檢證雙元自主性模型的各項主張觀點。 本文共包含兩個研究，研究一經由811位台灣北部與南部的高中職三年級學生，以班級作為單位進行團體施測，測量其個體化與關係雙元自主能力，並透過情境中強調不同適應訴求的操弄，探討雙元自主能力與情境操弄對訊息區辨表現的共同作用效果，以重複檢證雙元自主性模型中對（1）雙元自主能力可以共存，以及（2）不同自主能力各具有範疇優勢性的兩項主張觀點。研究一結果發現：個體化自主與關係自主兩種能力之間具有中度的正相關，再次支持雙元自主取向能力可以相互共存的主張觀點；但兩種自主能力彼此間仍具有可區辨的關聯效度，其中個體化自主能力主要作用於個人範疇的訊息區辨表現上，而關係自主能力主要作用於人際範疇訊息區辨表現上。另外也發現與區辨訊息範疇一致的情境操弄，相較於不一致的情境操弄或是不給予情境操弄，能強化範疇優勢性的作用效果。 為強化研究一的結果，研究二針對研究一的研究限制改善情境操弄的工具與訊息區辨作業，並以台灣北部大學生作為研究對象，其中61位雙元自主能力皆高的雙高組受試者，以及63位雙元自主能力皆低的雙低組受試者。分別邀請至實驗室進行個別的電腦施測，並透過情境脈絡中強調不同適應需求的操弄，再次探討雙元自主能力對訊息區辨表現的範疇優勢性運作機制，結果發現：首先，相對於雙低組，雙高組受試者在個人範疇與人際範疇的訊息區辨表現都比較好，重複檢證雙元自主能力對於訊息區辨的作用效果；其次，相較於不一致的情境操弄或是不給予情境操弄，與區辨訊息範疇一致的情境促發能提升個人範疇與人際範疇的訊息區辨表現。並且發現情境操弄的效果主要作用於發展出高度雙元自主能力的雙高組受試者，在雙低組受試者身上不同的情境操弄則沒有差異，不僅反映出雙元自主能力與情境操弄對訊息區辨表現的共同作用效果，並且重複地驗證雙元自主性模型中共存的理論觀點以及本研究對雙元自主性範疇優勢的運作機制推論。 本文並依據研究發現提出各項討論，論述雙元自主性模型與過去相關文獻與實徵發現的異同。像是（1）該模型採取適應能力的觀點重新詮釋自主性的多元發展取向，進行一系列的實徵研究並且獲得支持的研究結果；（2）採取情境與性格交互運作的系統觀點，說明雙元自主能力的共存與範疇優勢性動態機制；（3）透過非自陳式的訊息區辨表現杜絕共同方法變異的可能威脅，重複檢證該模型的各項理論觀點以及本研究的推論假設；（4）最後針對研究中各項不符合預期的結果提出合理解釋，並且說明未來值得繼續深入探討的相關議題。 / This article has two main goals about verifying the ideas of the Dual Autonomy Model (Yeh & Yang, 2006; Yeh, Bedford, & Yang, 2007; Yeh, Liu, Huang, & Yang, 2007). The first is testing the collective effect of dual autonomy and situational operation on message discernment to support the processing mechanism of the domain superiority hypothesis. The second is using the message discernments as performance criteria variable to prevent the common method variance bias and replicate the each hypotheses of dual autonomy. In study 1, 811 senior high school students from north and south of Taiwan as participants were asked to complete a group-survey in their classrooms. A medium correlation between individuating autonomy (IA) and relating autonomy (RA) replicates the coexistence hypothesis. It was also found a distinguishable criterion-validity confirms the domain superiority hypothesis that IA was greater associated with personal domain message discernment but RA was greater associated with interpersonal domain message discernment. This effect of domain superiority was enhanced only under the congruous situational operation condition, shows the collective effect of situation and autonomy capacity that verifies the processing mechanism of domain superiority. For strengthening the result of study 1, study 2 improves the situational operation materials and the message discernment tasks. 61 college students whose dual autonomy both are higher and 63 college students whose dual autonomy both are lower were recruited as participants for study 2 from school in north of Taiwan. They were invited to a small room separately and asked to complete all of the materials and tasks that were presented on computer. The participants with higher dual autonomy have better message discernments of both domains than the participants with lower dual autonomy, replicates the effect of dual autonomy on message discernments. The congruous situational operation produced greater message discriminations than incongruous or none situational operation. This situational effect was only significant on the participants with higher dual autonomy, shows the collective effect of situation and autonomy capacity, confirms the coexist of dual autonomy, and also verifies the processing mechanism of domain superiority. These results and limitations of these two studies, comparison with the past findings, and considerations for future research are discussed.