京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第20504号 / 情博第632号 / 新制||情||110(附属図書館) / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 鹿島 久嗣, 教授 山本 章博, 教授 阿久津 達也 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

MASTER THESIS MODELLING AND COMPARATIVE ANALYZES OF VOLATILITY SPILLOVER BETWEEN US, CZECH REPUBLIC AND SERBIAN STOCK MARKETS Abstract This paper estimates Serbian, Czech and US stock markets volatility. Few studies analyzed stock market linkages for these three markets. The mean equation is estimated using the vector auto- regression model. The second moments is further estimated using different multivariate GARCH models. We find that current conditional volatilities for each stock is highly affected by the past innovations. Cross-market correlations are significant as well. However, there is a higher conditional correlation between Czech and US stock market indices compared to the conditional correlation between Serbian and US stock indices.

Thesis Portfolio Credit Risk Modeling focuses on state-of-the-art credit models largely implemented by banks into their banking risk-assessment and complementary valuation system frameworks. Reader is provided in general with both theoretical and applied (practical) approaches that are giving a clear notion how selected portfolio models perform in real-world environment. Our study comprises CreditMetrics, CreditRisk+ and KMV model. In the first part of the thesis, our intention is to clarify theoretically main features, modeling principles and moreover we also suggest hypotheses about strengths/drawbacks of every scrutinized model. Subsequently, in the applied part we test the models in a lab-environment but with real-world market data. Noticeable stress is also put on model calibration. This enables us to con firm/reject the assumptions we made in the theoretical part. In the very end there follows a straightforward general overview of all outputs and a conclusion.

Set in a medium sized, suburban, two-year community college, this study explores the initial development of an actionable predictive risk model for first-year retention based on data currently collected and to identify target variables upon which data should be collected to improve future versions of the model. Institutional data were analyzed by means of Analysis of Variance and logistic regression and ordinary least squares regression analyses. Results suggested there are several important variables for which data as available but the risk model as developed has relatively low predictive power. The results of this study are used to inform College administrators, faculty, and staff about risk modeling, conclusions that can be drawn from existing data, and provide guidance on additional relevant variables upon which data should be collected. Additionally, recommendations for future research are discussed for this study’s institution and the field of education as it relates to Community Colleges. / Educational Leadership

The interdependencies that exist within and between infrastructures can cause unexpected system properties to emerge when their components fail due to large disruptions. As witnessed following emergencies such as Hurricane Katrina, the complexities of these interdependencies make it very difficult to effectively recover infrastructure because of the challenges they create in prioritizing the most critical components for repair. The Joint Infrastructure Interdependencies Research Program was initiated by Public Safety Canada (PSC) and the Natural Sciences and Engineering Research Council of Canada (NSERC) in 2005 to research methods for remedying this problem. As a part of this research, the University of British Columbia (UBC) is developing an infrastructure interdependency simulator, named I2Sim, to simulate disasters and develop strategies for dealing with emergencies. Part of this development is to construct a model of the UBC electrical distribution system and interface it with I2Sim. In this research, a general methodology for such a model is presented, which employs an off-the-shelf powerflow modeling tool. In addition, a model of the UBC information technology infrastructure is developed to provide a second infrastructure model to demonstrate the electrical model's usefulness in multi-infrastructure disaster recovery simulations. Simulations with these models have shown that the recovery of this two-infrastructure system can be carried out more effectively following an earthquake if both infrastructures are considered together in the repair approach, rather than individually. This difference was on the order of thirty percent. To extend this research from electrical distribution systems to electrical bulk systems, an interdependency model of the British Columbia Transmission Corporation bulk power network and its communications system was also developed, along with a post-blackout restoration procedure. Using these, simulations of a post-blackout recovery were carried out to study the level of risk that communications outages may pose to the electrical network's recovery. These simulations revealed a correlation between restoration time and the number of communication points lost. This research also demonstrates there is value in combining the results of such simulations with risk evaluation tools. Together these results provided a clearer indication of where vulnerabilities exist.

The interdependencies that exist within and between infrastructures can cause unexpected system properties to emerge when their components fail due to large disruptions. As witnessed following emergencies such as Hurricane Katrina, the complexities of these interdependencies make it very difficult to effectively recover infrastructure because of the challenges they create in prioritizing the most critical components for repair. The Joint Infrastructure Interdependencies Research Program was initiated by Public Safety Canada (PSC) and the Natural Sciences and Engineering Research Council of Canada (NSERC) in 2005 to research methods for remedying this problem. As a part of this research, the University of British Columbia (UBC) is developing an infrastructure interdependency simulator, named I2Sim, to simulate disasters and develop strategies for dealing with emergencies. Part of this development is to construct a model of the UBC electrical distribution system and interface it with I2Sim. In this research, a general methodology for such a model is presented, which employs an off-the-shelf powerflow modeling tool. In addition, a model of the UBC information technology infrastructure is developed to provide a second infrastructure model to demonstrate the electrical model's usefulness in multi-infrastructure disaster recovery simulations. Simulations with these models have shown that the recovery of this two-infrastructure system can be carried out more effectively following an earthquake if both infrastructures are considered together in the repair approach, rather than individually. This difference was on the order of thirty percent. To extend this research from electrical distribution systems to electrical bulk systems, an interdependency model of the British Columbia Transmission Corporation bulk power network and its communications system was also developed, along with a post-blackout restoration procedure. Using these, simulations of a post-blackout recovery were carried out to study the level of risk that communications outages may pose to the electrical network's recovery. These simulations revealed a correlation between restoration time and the number of communication points lost. This research also demonstrates there is value in combining the results of such simulations with risk evaluation tools. Together these results provided a clearer indication of where vulnerabilities exist.

L'objectif de cette thèse est de contribuer à améliorer le calcul de la valorisation des obligations d'entreprise, notamment en essayant de tirer des leçons de la récente crise économique et financière. Afin d'atteindre cet objectif, nous proposons une approche basée sur les spreads de crédit. Nous commençons, dans un premier chapitre, par une analyse des principaux modèles de valorisation existants, que nous reformulons du point de vue des spreads et que nous simulons numériquement. Nous montrons que, malgré les caractéristiques attrayantes des modèles de type structurel, ceux ci comportent plusieurs lacunes qui peuvent être trompeuses surtout en contexte de crise. Dans les deuxième et troisième chapitres, nous mettons l'accent sur les spreads empiriques, que nous analysons pendant les crises des subprimes et de la zone euro. Par l'intermédiaire : (i) d'une analyse descriptive, (ii) d'analyses en composantes principales, ainsi que (iii) d'analyses par régressions statistiques, nous parvenons à mettre la lumière sur plusieurs facteurs qui affectent les mouvements des spreads et qui ne sont pas pris en compte par les modèles existants. Parmi ces facteurs, nous montrons : (i) que la vague de sauvetage des banques pendant la crise a eu un effet considérable sur les spreads de crédit, et (ii) que la taille d'une firme a également un effet sur ses spreads. Sur la base de ces résultats empiriques, nous proposons dans un quatrième chapitre une contribution à la modélisation structurelle des obligations d'entreprise, qui prend en compte la possibilité des firmes de négocier un sauvetage en cas de détresse. À l'aide de ce modèle, nous parvenons, d'une part, à reproduire les observations empiriques de spreads plus faibles pour des probabilités de sauvetage plus élevées (comme c'est le cas pour les grandes banques), et d'autre part, à combler plusieurs lacunes des modèles existants, tels que les simples mécanismes de faillite, ou les faibles spreads de crédit pour les courtes maturités. / The aim of this thesis is to contribute to the improvement of the valuation of corporate bonds, particularly by drawing some lessons from the recent economic and financial crisis. In order to achieve this goal, we propose an approach based on corporate bonds' credit spreads. We start, in the first chapter, by analyzing the main existing valuation models, which we reformulate from the standpoint of credit spreads and which we simulate numerically. We show that, despite the attractive features that the structural models have, the latter exert contain several shortcomings which may be misleading especially in a crisis context. In the second and third chapters, we focus on the empirical credit spreads, which we analyze during the subprime crisis and the Eurozone crisis periods. By the means of : (i) a descriptive analysis, (ii) principal component analyses, and(iii) statistical regression analyses, we manage to shed light on a number of factors which affect the movements of the spreads and have not been addressed by the existing models. Among these factors, we show that : (i) the wave of bailouts that occurred during the crisis has had an important effect on the spreads, and (ii) the size of a firm is connected with its spreads. Based on these empirical results, we propose in the fourth chapter a contribution to the modeling of corporate bonds which accounts for the possibility of firms to negotiate a rescue plan in case of distress. This model allows us, on the one hand, to reproduce the empirical observations of lower credit spreads for higher probabilities of receiving a bailout (as it is the case for large banks), and on the other hand, to tackle several drawbacks of the existing models, such as the simple bankruptcy mechanisms or the low credit spreads for short maturities.

The interdependencies that exist within and between infrastructures can cause unexpected system properties to emerge when their components fail due to large disruptions. As witnessed following emergencies such as Hurricane Katrina, the complexities of these interdependencies make it very difficult to effectively recover infrastructure because of the challenges they create in prioritizing the most critical components for repair. The Joint Infrastructure Interdependencies Research Program was initiated by Public Safety Canada (PSC) and the Natural Sciences and Engineering Research Council of Canada (NSERC) in 2005 to research methods for remedying this problem. As a part of this research, the University of British Columbia (UBC) is developing an infrastructure interdependency simulator, named I2Sim, to simulate disasters and develop strategies for dealing with emergencies. Part of this development is to construct a model of the UBC electrical distribution system and interface it with I2Sim. In this research, a general methodology for such a model is presented, which employs an off-the-shelf powerflow modeling tool. In addition, a model of the UBC information technology infrastructure is developed to provide a second infrastructure model to demonstrate the electrical model's usefulness in multi-infrastructure disaster recovery simulations. Simulations with these models have shown that the recovery of this two-infrastructure system can be carried out more effectively following an earthquake if both infrastructures are considered together in the repair approach, rather than individually. This difference was on the order of thirty percent. To extend this research from electrical distribution systems to electrical bulk systems, an interdependency model of the British Columbia Transmission Corporation bulk power network and its communications system was also developed, along with a post-blackout restoration procedure. Using these, simulations of a post-blackout recovery were carried out to study the level of risk that communications outages may pose to the electrical network's recovery. These simulations revealed a correlation between restoration time and the number of communication points lost. This research also demonstrates there is value in combining the results of such simulations with risk evaluation tools. Together these results provided a clearer indication of where vulnerabilities exist. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

Intersection crashes represent one-fifth of all police reported traffic crashes and one-sixth of all fatal crashes in the United States each year. Active safety systems have the potential to reduce crashes and injuries across all crash modes by partially or fully controlling the vehicle in the event that a crash is imminent. The objective of this thesis was to evaluate crash and injury reduction in a future United States fleet equipped with intersection advanced driver assistance systems (I-ADAS). In order to evaluate this, injury risk modeling was performed. The dataset used to evaluate injury risk was the National Automotive Sampling System / Crashworthiness Data System (NASS/CDS). An injured occupant was defined as vehicle occupant who experienced an injury of maximum Abbreviated Injury Scale (AIS) of 2 or greater, or who were fatally injured. This was referred to as MAIS2+F injury. Cases were selected in which front-row occupants of late-model vehicles were exposed to a frontal, near-, or far-side crash. Logistic regression was used to develop an injury model with occupant, vehicle, and crash parameters as predictor variables. For the frontal and near-side impact models, New Car Assessment Program (NCAP) test results were used as a predictor variable. This work quantitatively described the injury risk for a wide variety of crash modes, informing effectiveness estimates. This work reconstructed 501 vehicle-to-vehicle left turn across path / opposite direction (LTAP/OD) crashes in the United States which had been originally investigated in NMVCCS. The performance of thirty different I-ADAS system variations was evaluated for each crash. These variations were the combinations of five Time to Collision (TTC) activation thresholds, three latency times, and two different intervention types (automated braking and driver warning). In addition, two sightline assumptions were modeled for each crash: one where the turning vehicle was visible long before the intersection, and one where the turning vehicle was only visible after entering the intersection. For resimulated crashes which were not avoided by I-ADAS, a new crash delta-v was computed for each vehicle. The probability of MAIS2+F injury to each front row occupant was computed. Depending on the system design, sightline assumption, I-ADAS variation, and fleet penetration, an I-ADAS system that automatically applies emergency braking could avoid 18%-84% of all LTAP/OD crashes. An I-ADAS system which applies emergency braking could prevent 44%-94% of front row occupants from receiving MAIS2+F injuries. I-ADAS crash and injured person reduction effectiveness was higher when both vehicles were equipped with I-ADAS. This study presented the simulated effectiveness of a hypothetical intersection active safety system on real crashes which occurred in the United States, showing strong potential for these systems to reduce crashes and injuries. However, this crash and injury reduction effectiveness made the idealized assumption of full installation in all vehicles of a future fleet. In order to evaluate I-ADAS effectiveness in the United States fleet the proportion of new vehicles with I-ADAS was modeled using Highway Loss Data Institute (HLDI) fleet penetration predictions. The number of potential LTAP/OD conflicts was modeled as increasing year over year due to a predicted increase in Vehicle Miles Traveled (VMT). Finally, the combined effect of these changes was used to predict the number of LTAP/OD crashes each year from 2019 to 2060. In 2060, we predicted 70,439 NMVCCS-type LTAP/OD crashes would occur. The predicted number of MAIS2+F injured front row occupants in 2060 was 3,836. This analysis shows that even in the long-term fleet penetration of Intersection Active Safety Systems, many injuries will continue to occur. This underscores the importance of maintaining passive safety performance in future vehicles. / M.S. / Future vehicles will have electronic systems that can avoid crashes in some cases where a human driver is unable, unaware, or reacts insufficiently to avoid the crash without assistance. The objective of this work was to determine, on a national scale, how many crashes and injuries could be avoided due to Intersection Advanced Driver Assistance Systems (I-ADAS), a hypothetical version of one of these up-and-coming systems. This work focused on crashes where one car is turning left at an intersection and the other car is driving through the intersection and not turning. The I-ADAS system has sensors which continuously search for other vehicles. When the I-ADAS system determines that a crash may happen, it applies the brakes or otherwise alerts the driver to apply the brakes. Rather than conduct actual crash tests, this was simulated on a computer for a large number of variations of the I-ADAS system. The basis for the simulations was real crashes that happened from 2005 to 2007 across the United States. The variations that were simulated changed the time at which the I-ADAS system triggered the brakes (or alert) and the simulated amount of computer time required for the I-ADAS system to make a choice. In some turning crashes, the car cannot see the other vehicle because of obstructions, such as a line of people waiting to turn left across the road. Because of this, simulations were conducted both with and without the visual obstruction. For comparison, we performed a simulation of the original crash as it happened in real life. Finally, since there are two cars in each crash, there are simulations when either car has the I-ADAS system or when both cars have the I-ADAS system. Each simulation either ends in a crash or not, and these are tallied up for each system variation. The number of crashes avoided compared to the number of simulations run is crash effectiveness. Crash effectiveness ranged from 1% to 84% based on the system variation. For each crash that occurred, there is another simulation of the time immediately after impact to determine how severe the impact was. This is used to determine how many injuries are avoided, because often the crashes which still happened were made less severe by the I-ADAS system. In order to determine how many injuries can be avoided by making the crash less severe, the first chapter focuses on injury modeling. This analysis was based on crashes from 2008 to 2015 which were severe enough that one of the vehicles was towed. This was then filtered down by only looking at crashes where the front or sides were damaged. Then, we compared the outcome (injury as reported by the hospital) to the circumstances (crash severity, age, gender, seat belt use, and others) to develop an estimate for how each of these crash circumstances affected the injury experienced by each driver and front row passenger. A second goal for this chapter was to evaluate whether federal government crash ratings, commonly referred to as “star ratings”, are related to whether the driver and passengers are injured or not. In frontal crashes (where a vehicle hits something going forwards), the star rating does not seem to be related to the injury outcome. In near-side crashes (the side next to the occupant is hit), a higher star rating is better. For frontal crashes, the government test is more extreme than all but a few crashes observed in real life, and this might be why the injury outcomes measured in this study are not related to frontal star rating. Finally, these crash and injury effectiveness values will only ever be achieved if every car has an I-ADAS system. The objective of the third chapter was to evaluate how the crash and injury effectiveness numbers change each year as new cars are purchased and older cars are scrapped. Early on, few cars will have I-ADAS and crashes and injuries will likely still occur at roughly the rate they would without the system. This means that crashes and injuries will continue to increase each year because the United States drives more miles each year. Eventually, as consumers buy new cars and replace older ones, left turn intersection crashes and injuries are predicted to be reduced. Long into the future (around 2050), the increase in crashes caused by miles driven each year outpaces the gains due to new cars with the I-ADAS system, since almost all of the old cars without I-ADAS have been removed from the fleet. In 2025, there will be 173,075 crashes and 15,949 injured persons that could be affected by the I-ADAS system. By 2060, many vehicles will have I-ADAS and there will be 70,439 crashes and 3,836 injuries remaining. Real cars will not have a system identical to the hypothetical I-ADAS system studied here, but systems like it have the potential to significantly reduce crashes and injuries.

The master's thesis formulated the most important trends in the development of cybercrime in the banking sector. Based on the actual problems of cybersecurity management, a model for assessing cyber-risks for Russian commercial banks was reconstructed. / В магистерской диссертации сформулированы ключевые тенденции развития киберпреступности в банковском секторе. Исходя из актуальной проблематики управления кибербезопасностью, произведена реконструкция модели оценки кибер-рисков для российских коммерческих банков.