Data Challenges in High-Performance Risk Analytics

Risk Analytics is important to quantify, manage and analyse risks from the manufacturing to the financial setting. In this paper, the data challenges in the three stages of the high-performance risk analytics pipeline, namely risk modelling, portfolio risk management and dynamic financial analysis is presented

Parallel Simulations for Analysing Portfolios of Catastrophic Event Risk

At the heart of the analytical pipeline of a modern quantitative insurance/reinsurance company is a stochastic simulation technique for portfolio risk analysis and pricing process referred to as Aggregate Analysis. Support for the computation of risk measures including Probable Maximum Loss (PML) and the Tail Value at Risk (TVAR) for a variety of types of complex property catastrophe insurance contracts including Cat eXcess of Loss (XL), or Per-Occurrence XL, and Aggregate XL, and contracts that combine these measures is obtained in Aggregate Analysis. In this paper, we explore parallel methods for aggregate risk analysis. A parallel aggregate risk analysis algorithm and an engine based on the algorithm is proposed. This engine is implemented in C and OpenMP for multi-core CPUs and in C and CUDA for many-core GPUs. Performance analysis of the algorithm indicates that GPUs offer an alternative HPC solution for aggregate risk analysis that is cost effective. The optimised algorithm on the GPU performs a 1 million trial aggregate simulation with 1000 catastrophic events per trial on a typical exposure set and contract structure in just over 20 seconds which is approximately 15x times faster than the sequential counterpart. This can sufficiently support the real-time pricing scenario in which an underwriter analyses different contractual terms and pricing while discussing a deal with a client over the phone.Comment: Proceedings of the Workshop at the International Conference for High Performance Computing, Networking, Storage and Analysis (SC), 2012, 8 page

QuPARA: Query-Driven Large-Scale Portfolio Aggregate Risk Analysis on MapReduce

Stochastic simulation techniques are used for portfolio risk analysis. Risk portfolios may consist of thousands of reinsurance contracts covering millions of insured locations. To quantify risk each portfolio must be evaluated in up to a million simulation trials, each capturing a different possible sequence of catastrophic events over the course of a contractual year. In this paper, we explore the design of a flexible framework for portfolio risk analysis that facilitates answering a rich variety of catastrophic risk queries. Rather than aggregating simulation data in order to produce a small set of high-level risk metrics efficiently (as is often done in production risk management systems), the focus here is on allowing the user to pose queries on unaggregated or partially aggregated data. The goal is to provide a flexible framework that can be used by analysts to answer a wide variety of unanticipated but natural ad hoc queries. Such detailed queries can help actuaries or underwriters to better understand the multiple dimensions (e.g., spatial correlation, seasonality, peril features, construction features, and financial terms) that can impact portfolio risk. We implemented a prototype system, called QuPARA (Query-Driven Large-Scale Portfolio Aggregate Risk Analysis), using Hadoop, which is Apache's implementation of the MapReduce paradigm. This allows the user to take advantage of large parallel compute servers in order to answer ad hoc risk analysis queries efficiently even on very large data sets typically encountered in practice. We describe the design and implementation of QuPARA and present experimental results that demonstrate its feasibility. A full portfolio risk analysis run consisting of a 1,000,000 trial simulation, with 1,000 events per trial, and 3,200 risk transfer contracts can be completed on a 16-node Hadoop cluster in just over 20 minutes.Comment: 9 pages, IEEE International Conference on Big Data (BigData), Santa Clara, USA, 201

Multi-scale characterisation of the 3D microstructure of a thermally-shocked bulk metallic glass matrix composite

Bulk metallic glass matrix composites (BMGMCs) are a new class of metal alloys which have significantly increased ductility and impact toughness, resulting from the ductile crystalline phases distributed uniformly within the amorphous matrix. However, the 3D structures and their morphologies of such composite at nano and micrometre scale have never been reported before. We have used high density electric currents to thermally shock a Zr-Ti based BMGMC to different temperatures, and used X-ray microtomography, FIB-SEM nanotomography and neutron diffraction to reveal the morphologies, compositions, volume fractions and thermal stabilities of the nano and microstructures. Understanding of these is essential for optimizing the design of BMGMCs and developing viable manufacturing methods

Multiple Partnerships for Student Information Literacy: Library, Writing Center, Faculty, and Administrators

In May, 2007, a University of Central Florida regional campus team comprised of teaching faculty, librarians, administrators, and writing center coordinators received a three year Quality Enhancement Plan grant to study the impact of a library/writing center partnership on student information literacy. This presentation will share our project’s results and benefits. Using the ACRL Information Literacy Standards, the team developed modifications and interventions designed to improve students’ ability to gather, evaluate, and use information, and to enhance their technology literacy and critical thinking. The project’s development included ongoing discussions of progress, obstacles, program collaboration, and single location of services. Targeted student interventions included group workshops and one-on-one writing center/librarian sessions. The James Madison University Information Literacy Test, a research paper evaluation, and a student perception survey were used for assessment. Benefits included enhanced academic collaboration and the establishment and expansion of a successful writing center. The results should have broad application for other institutions

Time-resolved synchrotron X-ray micro-tomography datasets of drainage and imbibition in carbonate rocks

Multiphase flow in permeable media is a complex pore-scale phenomenon, which is important in many natural and industrial processes. To understand the pore-scale dynamics of multiphase flow, we acquired time-series synchrotron X-ray micro-tomographic data at a voxel-resolution of 3.28 μm and time-resolution of 38 s during drainage and imbibition in a carbonate rock, under a capillary-dominated flow regime at elevated pressure. The time-series data library contains 496 tomographic images (gray-scale and segmented) for the complete drainage process, and 416 tomographic images (gray-scale and segmented) for the complete imbibition process. These datasets have been uploaded on the publicly accessible British Geological Survey repository, with the objective that the time-series information can be used by other groups to validate pore-scale displacement models such as direct simulations, pore-network and neural network models, as well as to investigate flow mechanisms related to the displacement and trapping of the non-wetting phase in the pore space. These datasets can also be used for improving segmentation algorithms for tomographic data with limited projections

Characteristics of atmospheric organic and elemental carbon particle concentrations in Los Angeles

A fine particle air monitoring network was operated in the Los Angeles area during 1982. It was found that carbonaceous aerosols accounted for typically 40% of total fine particle mass loadings at most monitoring sites. The ratio of total carbon (TC) to elemental carbon (EC) in ambient samples and in primary source emissions was examined as an indicator of the extent of secondary organic aerosol formation. It was found that TC to EC ratios at all sites on average are no higher than recent estimates of the TC to EC ratio in primary source emissions. There is little evidence of the sustained summer peak in the ratio of TC to EC that one might expect if greatly enhanced secondary organics production occurs during the photochemical smog season. The TC to EC ratio does rise by the time that air masses reach the prevailing downwind edge of the air basin as would be expected if secondary organics are being formed during air parcel transport, but the extent of that increase is modest. These results suggest that primary particulate carbon emissions were the principal contributor to long-term average fine aerosol carbon concentrations in the Los Angeles area during 1982