This thesis reviewed various heavy tailed distributions and Extreme Value Theory (EVT) to estimate the catastrophic losses simulated from Florida Public Hurricane Loss Projection Model (FPHLPM). We have compared risk measures such as Probable Maximum Loss (PML) and Tail Value at Risk (TVaR) of the selected distributions with empirical estimation to capture the characteristics of the loss data as well as its tail distribution. Generalized Pareto Distribution (GPD) is the main focus for modeling the tail losses in this application. We found that the hurricane loss data generated from FPHLPM were consistent with historical losses and were not as heavy as expected. The tail of the stochastic annual maximum losses can be explained by an exponential distribution. This thesis also touched on the philosophical implication of small probability, high impact events such as Black Swan and discussed the limitations of quantifying catastrophic losses for future inference using statistical methods.

This thesis concerns the upgrade of APID One RPAS by The MainBase AB. Due to their wish to explore different options for tail drive of current RPAS and future heavier variant, several concepts have been generated and analyzed. Systemic elimination of proposed concepts has resulted with a belt drive transmission. Basic calculation has been performed to determine estimates of power requirements and positions of the new tail rotors relative to the fuselage. Furthermore, the design process encompassed components from the existing output power shaft within the fuselage to the final blades shaft, including the belt transmission, the truss-type structural support, and additional devices. The work includes material selection, FEA analyses of the structure, and bolts and welds calculations where loads were known. Final result is a tail boom that interchangeably fits both, new light and new heavy variant.

The thesis deals the preliminary draft of the small two-seat helicopter with a piston engine. The aim of the thesis is not to propose a helicopter across the extent of the problems. Therefore we have chosen only selected issues which can be managed in the range of work. The introduction is focused on defining the general requirements imposed on proposed helicopter, formulating the basic conceptual and structural design according to the building regulation the relevant category. These ranges create a based assumption for right evaluation of the statistical analysis of the helicopters of the same or very near parameters category. Subsequently, there were defined the basic parameters of the proposed helicopter which make possible to solve the performance characteristics in the vertical and backward flight. The work is also focused on design of the rotor head of main rotor for the proposed helicopter, the definition of load acting on the rotor head, waving analysis and calculation of centrifugal forces acting on the main rotor blades. The design of the rotor head and also the helicopter as a whole will be graphically processed in the program Dassault Systemes Catia.

Netflix and other streaming services have grown immensely since they started offering online streaming. In this paper I present a correlation matrix using ticket sales at the domestic box office and the number of Netflix subscribers. They are shown to be negatively correlated with one another, supporting many previous researchers’ thoughts on the topic. I also show using two OLS regressions with data from movies released in 2006-2007 and 2017-2018 that being a part of a franchise has a stronger correlation with increased revenue in the latter model compared to the previous one. In the models one can also see that the general quality of a movie, as measured by IMDb rating, is associated with a higher increase in revenue in the latter model. I argue that this is due to consumers being inclined to watch what they perceive to be high-quality movies in the theaters in the latter model as they can conveniently watch movies of a poorer quality on their streaming service, an option that was not available to the same extent previously. I also argue that consumers are more willing to commit to going to the cinema for a franchise movie, especially in the Marvel cinematic universe, as they are often effects driven movies which are better experienced on a large screen. The budget variable is significant in both models, but the coefficient is much smaller in the second model. I argue that this is due to the fact that a higher budget is required for movies released in 2017-2018 to maintain the same level of revenue as in 2006 and 2007 due to the competition that have come from streaming services. However, I conclude that more research is necessary before drawing definite conclusions as the market for cinema is highly uncertain and difficult to estimate accurately.

Modeling extreme events is one of the central tasks in risk management and planning, as catastrophes and crises put human lives and financial assets at stake. A common approach to estimate the likelihood of extreme events, using extreme value theory (EVT), studies the asymptotic behavior of the ``tail" portion of data, and suggests suitable parametric distributions to fit the data backed up by their limiting behaviors as the data size or the excess threshold grows. We explore an alternate approach to estimate extreme events that is inspired from recent advances in robust optimization. Our approach represents information about tail behaviors as constraints and attempts to estimate a target extremal quantity of interest (e.g, tail probability above a given high level) by imposing an optimization problem to find a conservative estimate subject to the constraints that encode the tail information capturing belief on the tail distributional shape. We first study programs where the feasible region is restricted to distribution functions with convex tail densities, a feature shared by all common parametric tail distributions. We then extend our work by generalizing the feasible region to distribution functions with monotone derivatives and bounded or infinite moments. In both cases, we study the statistical implications of the resulting optimization problems. Through investigating their optimality structures, we also present how the worst-case tail in general behaves as a linear combination of polynomial decay tails. Numerically, we develop results to reduce these optimization problems into tractable forms that allow solution schemes via linear-programming-based techniques.

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第18106号 / 理博第3984号 / 新制||理||1574(附属図書館) / 30964 / 京都大学大学院理学研究科生物科学専攻 / (主査)教授 中務 真人, 教授 山極 壽一, 教授 疋田 努 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

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This dissertation implements an optimal trading strategy based on the machine learning method and extreme value theory (EVT) to obtain an excess return on investments in the capital market. The trading strategy outperforms the benchmark S&P 500 index with higher returns and lower volatility through effective market timing. In addition, this dissertation starts by modeling the market tail risk using the EVT and reinforcement learning methods, distinguishing from the traditional value at risk method. In this dissertation, I used EVT to extract the characteristics of the tail risk, which are inputs for reinforcement learning. This process is proved to be effective in market timing, and the trading strategy could avoid market crash and achieve a long-term excess return. In sum, this study has several contributions. First, this study takes a new method to analyze stock price (in this dissertation, I use the S&P 500 index as a stock). I combined the EVT and reinforcement learning to study the price tail risk and predict stock crash efficiently, which is a new method for tail risk research. Thus, I can predict the stock crash or provide the probability of risk, and then, the trading strategy can be built. The second contribution is that this dissertation provides a dynamic market timing trading strategy, which can significantly outperform the market index with a lower volatility and a higher Sharpe ratio. Moreover, the dynamic trading process can provide investors an intuitive sense on the stock market and help in decision-making. Third, the success of the strategy shows that the combination of EVT and reinforcement learning can predict the stock crash very well, which is a great improvement on the extreme event study and deserves further study. / Business Administration/Finance

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