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The counterparty credit risk is particularly hard to simulate and this thesis is only the second work so far, which considers effective simulation of couterparty risk. There are two new approaches to stochastic modelling, which are useful with respect to ef- ficient simulation of counterparty risk. These are Path-Dependent Simulation (PDS) and Direct-Jump to Simulation date (DJS). It had been show that DJS is far more ef- fective, when it comes counterparty risk simulation of path-independent derivatives. We focus on a portfolio of interest rate swaps, which are effectively path-dependent. DJS approach yields estimates with much lower variance than PDS approach. But as expected, the DJS is also much more computationally intensive. The increase in computing time in majority of cases wipes out any gains in lower variance and PDS approach is shown to be more effective, when computing time is taken into account. We also show that in practice the convergence rate of Monte Carlo method signif- icantly underestimates the true reduction in variance, which can be achieved with increasing number of scenarios. JEL Classification C02, C15, C63, G01, G12, G32 Keywords Monte Carlo, CVA, Exposure, Variance Author's e-mail robberth.cz@gmail.com Supervisor's e-mail boril.sopov@gmail.com

Chung Chi Keung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 80-84). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Partition Maximum Likelihood Estimation of the General Model --- p.5 / Chapter 2.1 --- Introduction --- p.5 / Chapter 2.2 --- Model --- p.5 / Chapter 2.3 --- The Partition Maximum Likelihood Procedure --- p.8 / Chapter 2.3.1 --- PML estimation of pa --- p.9 / Chapter 2.3.2 --- PML estimation of pab --- p.13 / Chapter 2.3.3 --- Asymptotic properties of the first-stage PML estimates --- p.15 / Chapter 3 --- Bayesian Analysis of Stochastic Prior Information --- p.19 / Chapter 3.1 --- Introduction --- p.19 / Chapter 3.2 --- Bayesian analysis of the Model --- p.20 / Chapter 3.2.1 --- "Case 1, Γ = σ2I" --- p.21 / Chapter 3.2.2 --- Case 2，Г as diagonal matrix with different diagonal el- ements --- p.24 / Chapter 3.2.3 --- "Case 3, Г as a general positive definite matrix" --- p.26 / Chapter 4 --- Simulation Design and Numerical Example --- p.29 / Chapter 4.1 --- Simulation Design --- p.29 / Chapter 4.1.1 --- Model --- p.29 / Chapter 4.1.2 --- Methods of evaluation --- p.32 / Chapter 4.1.3 --- Data analysis --- p.33 / Chapter 4.2 --- Numerical Example --- p.34 / Chapter 4.2.1 --- Model --- p.35 / Chapter 5 --- Conclusion and Discussion --- p.42 / APPENDIX I to V --- p.44-50 / TABLES 1 to 10 --- p.51-77 / FIGURES 1 to 3 --- p.78-79 / REFERENCE --- p.80-84

In this study, I examined the relation between various construct relevant and irrelevant variables and a math problem solving assessment. I used independent performance measures representing the variables of mathematics content knowledge, general ability, and reading fluency. Non-performance variables included gender, socioeconomic status, language proficiency and special education qualification. Using a sequential regression and commonality analysis, I determined the amount of variance explained by each performance measure on the Oregon state math assessment in third grade. All variables were independently predictive of math problem solving scores, and used together, they explained 58% score variance. The math content knowledge measure explained the most variance uniquely (12%), and the measures of math content and general ability explained the most variance commonly (16%). In the second analysis, I investigated whether additional variance was explained once student demographic characteristics were controlled and how this affected the unique variance explained by each independent performance measure. By controlling for demographics, the model explained slightly more than 1% additional variance in math scores. The unique variance explained by each independent measure decreased slightly. This study highlighted the influence of various construct relevant and irrelevant variables on math problem solving scores, including the extent to which a language-free measure of general ability might help to inform likely outcomes. The use of variance partitioning expanded understanding of the unique and common underlying constructs that affect math problem solving assessment. Finally, this study provided more information regarding the influence demographic information has on outcomes related to state math assessments.

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<p>In this paper we investigate pricing of variance swaps contracts. The</p><p>literature is mostly dedicated to the pricing using replication with</p><p>portfolio of vanilla options. In some papers the valuation with stochastic</p><p>volatility models is discussed as well. Stochastic volatility is becoming</p><p>more and more interesting to the investors. Therefore we decided to</p><p>perform valuation with the Heston stochastic volatility model, as well</p><p>as by using replication strategy.</p><p>The thesis was done at SunGard Front Arena, so for testing the replica-</p><p>tion strategy Front Arena software was used. For calibration and testing</p><p>of the Heston model we used MatLab.</p>

This paper examines the time lag between the peak in the arrival rate and the peaks in the mean and variance for the number of customers in an M(t)/M(t)/1l system. We establish a necessary condition for the time at which the peak in the mean is achieved. In cases in which system utilization exceeds one during some period, we show that the peak in the mean occurs after the end of this period. / Revised October 1994

Thesis (M.S.)--Brigham Young University. Dept. of Mechanical Engineering, 2005. / Includes bibliographical references (p. 115-118).

In this paper we investigate pricing of variance swaps contracts. The literature is mostly dedicated to the pricing using replication with portfolio of vanilla options. In some papers the valuation with stochastic volatility models is discussed as well. Stochastic volatility is becoming more and more interesting to the investors. Therefore we decided to perform valuation with the Heston stochastic volatility model, as well as by using replication strategy. The thesis was done at SunGard Front Arena, so for testing the replica- tion strategy Front Arena software was used. For calibration and testing of the Heston model we used MatLab.

國立大學校院校務基金政策從民國八十四開始試辦，至今已推行多年，但本研究發現目前尚無研究探討各校校務基金決算書上所列示之差異原因。由於國立大學預算是學校承諾未來計畫之工作，而決算是實際執行之結果，兩者差異除了一些無法控制因素外，若詳細檢討差異可以回饋給學校及主管機關未來在執行計畫或活動時能夠加以修改或改善。本研究分析公立大學96年至99年44所學校預算數、決算數差異情況，並彙整各校院對於預、決算數差異之原因，嘗試透過標準成本制與差異分析法之應用，詳細探討造成各校於預、決算數差異之因素，使差異分析能具體提供資訊給學校作為未來預算編製及成本控制管理之參考運用。 本研究發現儘管各校在國立大學校務基金決算書中，有列示預、決算差異及在備註說明其差異之理由，但說明都相當簡略除非原因明顯，否則甚難瞭解。研究發現教學研究及訓輔成本、管理費用與總務費用的用人費用皆有高估之傾向，主要是因為教、職員預算員額編列高估所致，同時透過差異分析亦顯示，即便有數量上之有利差異，仍有價格上之不利差異，代表相關之人事成本也偏高。教學研究及訓輔成本與管理費用與總務費用之折舊、折耗及攤銷在預算編列時嚴重低估。學生公費及獎勵金之靜態預算不利差異是由於平均每單位學生成本決算數較預算數高，而部分學校也是因為數量之不利差異；而靜態預算有利差異則主要是由於價格之有利差異所造成。大部分學校在研究發展費用中，其靜態預算不利差異係因價格上不利差異，即便大部分學校也具有數量上之有利差異。在學雜費收入淨額部分，靜態預算有利差異大部分係由數量之有利差異所造成；而造成學雜費收入淨額靜態預算不利差異除了數量上的不利差異外，大部分的學校是因為價格上的不利差異。本研究建議預算差異說明部分應明確界定差異金額與％需強制說明原因，並考慮編列「彈性預算」及差異分析方式增加價格差異及數量差異部分。 / Public universities have adopted public university fund instead of government fund since 1995. However, no research tries to analyze the reasons of the variance between the budget and the actual accounts of public universities. Since the budget of a public university is a commitment for the future plans and the actual account is the school’s executed result. The variance between them, except some uncontrollable factors, not only can provide some feedback but also can provide for improvement of the future plans, if the analysis had been analyzed effectively. Therefore, this study attempts to apply variance analysis of the standard cost and analyzes the variance between budget and final account of 44 public universities from 2007 to 2011. The proposed variance analysis approach is expected to substantially provide some useful information for public universities for the budget preparation and cost control and management. This study reveals that many universities indeed listed and explained the variance, but most of the explanations are quite rough. However, based on schools’ variance analysis it is very difficult to understand why and how the variances stand for, unless the reason is quite obviously. The study founds that the employees’ payrolls of teaching and research, training and consulting expenses, and general and administration expenses are overestimated due to overestimate the number of teachers and staffs. Variance analysis indicates that even a favorable variance on quantity, the price variance is still unfavorable, which means that the average personnel expenses are higher than the budget. The depreciation expenses of teaching and research and general and administration expenses are underestimated for all schools. Most school has an unfavorable static budget variance on student grant mainly due to higher actual average cost per student than the budget, and some schools due to unfavorable quantity variance. Most of schools’ unfavorable research and development expenses are caused by unfavorable price variance, although most of them have favorable quantity variances. The net revenue on tuition and fees, the favorable static budget variance is mainly due to favorable quantity variance and the unfavorable variance of net revenue on tuition and fees results from price variance. The research suggests that Ministry of Education should establish a standard amount or percentage of variance and requires public universities to explain when variance is higher than the standard. The public universities should prepare the “flexible budget” and apply the the price variance and quantity variance to variance analysis.