Multifactor Systematic Risk Analysis Based on Time-varying Signal Processing Models

This thesis applies the time-varying signal processing models to track the multifactor systematic risk in the Fama-French model. The mean reverting, random walk and random coefficient models are used to analyze the time-varying multifactor beta based on the multivariate Kalman filter algorithm. The sudden changes in the mutifactor beta ar e captured by the piecewise constant model. Our case studies explain the impacts of economic events on the sudden changes in betas for both individual stocks and industrial portfolios. We propose a new time-varying beta model based on a piecewise mean reverting process to express the effects of different types of events on the multifactor beta.The tracking of the piecewise mean reverting beta, using the modified multivariate Kalman filter with the maximum log likelihood estimator, outperforms the traditional piecewise constant and random walk models as demonstrated in our simulations. The empirical tests indicate that the new model effectively captures the different changes in beta depending on the type of event.

Multifactor Systematic Risk Analysis Based on Time-varying Signal Processing Models

This thesis applies the time-varying signal processing models to track the multifactor systematic risk in the Fama-French model. The mean reverting, random walk and random coefficient models are used to analyze the time-varying multifactor beta based on the multivariate Kalman filter algorithm. The sudden changes in the mutifactor beta ar e captured by the piecewise constant model. Our case studies explain the impacts of economic events on the sudden changes in betas for both individual stocks and industrial portfolios. We propose a new time-varying beta model based on a piecewise mean reverting process to express the effects of different types of events on the multifactor beta.The tracking of the piecewise mean reverting beta, using the modified multivariate Kalman filter with the maximum log likelihood estimator, outperforms the traditional piecewise constant and random walk models as demonstrated in our simulations. The empirical tests indicate that the new model effectively captures the different changes in beta depending on the type of event.

Reliability Analysis of Mooring Lines of Floating Structures Under Corrosion and Material Degradation

This paper presents a procedure for reliability analysis of mooring lines of floating units for oil and gas production considering corrosion and material degradation over time. The proposed procedure is limited to the ultimate limit state (ULS) and considers mooring lines made up of chain and polyester rope segments, although the same methodology can be applied to cases with steel wire segments. The proposed procedure can also be applied for mooring lines connected to any other type of floating offshore structure. For reliability assessments, it is necessary to consider the distributions and the probabilistic aspects of the random variables involved in the process. The weakest link system is used to model the strength of a mooring line segment. Simplified time-dependent probabilistic models for chain corrosion and polyester degradation are adopted to predict the strength degradation over time. The annual failure probability for different years is estimated by approximating the degraded strength by a piecewise constant model in order to perform a time variant reliability analysis. Monte Carlo simulations are used to determine the failure probability. A study case is also presented, where annual extreme top tension is obtained from long-term statistics considering Brazilian offshore environmental conditions acting on a turret moored floating, production, storage and offloading unit (FPSO).

Piecewise-Constant-Model-Based Interior Tomography Applied to Dentin Tubules

Dentin is a hierarchically structured biomineralized composite material, and dentin’s tubules are difficult to study in situ. Nano-CT provides the requisite resolution, but the field of view typically contains only a few tubules. Using a plate-like specimen allows reconstruction of a volume containing specific tubules from a number of truncated projections typically collected over an angular range of about 140°, which is practically accessible. Classical computed tomography (CT) theory cannot exactly reconstruct an object only from truncated projections, needless to say a limited angular range. Recently, interior tomography was developed to reconstruct a region-of-interest (ROI) from truncated data in a theoretically exact fashion via the total variation (TV) minimization under the condition that the ROI is piecewise constant. In this paper, we employ a TV minimization interior tomography algorithm to reconstruct interior microstructures in dentin from truncated projections over a limited angular range. Compared to the filtered backprojection (FBP) reconstruction, our reconstruction method reduces noise and suppresses artifacts. Volume rendering confirms the merits of our method in terms of preserving the interior microstructure of the dentin specimen.