The List_Tree - an innovative solution to customer portfolio representation

The scale of BT's operations necessitates the use of very large scale computing systems, and the storage and management of large volumes of data. Customer product portfolios are an important form of data which can be difficult to store in a space efficient way. The difficulties arise from the inherently structured form of product portfolios, and the fact that they change over time as customers add or remove products. This paper introduces a new data-modelling abstraction called the List_Tree. It has been designed specifically to support the efficient storage and manipulation of customer product portfolios, but may also prove useful in other applications with similar general requirements.

Impacts of compressed air energy storage plant on an electricity market with a large renewable energy portfolio

Renewable energy generation is expected to continue to increase globally due to renewable energy targets and obligations to reduce greenhouse gas emissions. Some renewable energy sources are variable power sources, for example wind, wave and solar. Energy storage technologies can manage the issues associated with variable renewable generation and align non-dispatchable renewable energy generation with load demands. Energy storage technologies can play different roles in each of the step of the electric power supply chain. Moreover, large scale energy storage systems can act as renewable energy integrators by smoothing the variability. Compressed air energy storage is one such technology. This paper examines the impacts of a compressed air energy storage facility in a pool based wholesale electricity market in a power system with a large renewable energy portfolio.

Covariance Averaging for Improved Estimation and Portfolio Allocation

We propose a new method for estimating the covariance matrix of a multivariate time series of nancial returns. The method is based on estimating sample covariances from overlapping windows of observations which are then appropriately weighted to obtain the nal covariance estimate. We extend the idea of (model) covariance averaging o ered in the covariance shrinkage approach by means of greater ease of use, exibility and robustness in averaging information over different data segments. The suggested approach does not su er from the curse of dimensionality and can be used without problems of either approximation or any demand for numerical optimization.

Computing probable maximum loss in catastrophe reinsurance portfolios on multi-core and many-core architectures

In the reinsurance market, the risks natural catastrophes pose to portfolios of properties must be quantified, so that they can be priced, and insurance offered. The analysis of such risks at a portfolio level requires a simulation of up to 800 000 trials with an average of 1000 catastrophic events per trial. This is sufficient to capture risk for a global multi-peril reinsurance portfolio covering a range of perils including earthquake, hurricane, tornado, hail, severe thunderstorm, wind storm, storm surge and riverine flooding, and wildfire. Such simulations are both computation and data intensive, making the application of high-performance computing techniques desirable.

In this paper, we explore the design and implementation of portfolio risk analysis on both multi-core and many-core computing platforms. Given a portfolio of property catastrophe insurance treaties, key risk measures, such as probable maximum loss, are computed by taking both primary and secondary uncertainties into account. Primary uncertainty is associated with whether or not an event occurs in a simulated year, while secondary uncertainty captures the uncertainty in the level of loss due to the use of simplified physical models and limitations in the available data. A combination of fast lookup structures, multi-threading and careful hand tuning of numerical operations is required to achieve good performance. Experimental results are reported for multi-core processors and systems using NVIDIA graphics processing unit and Intel Phi many-core accelerators.

The Valuation Implications of Enterprise Risk Management Maturity

Enterprise Risk Management (ERM) is the discipline by which enterprises monitor, analyze, and control risks from across the enterprise, with the goal of identifying underlying correlations and thus optimizing the risk-taking behavior in a portfolio context. This study analyzes the valuation implications of ERM Maturity. We use data from the industry leading Risk and Insurance Management Society Risk Maturity Model over the period from 2006 to 2011, which scores firms on a five-point maturity scale. Our results suggest that firms that have reached mature levels of ERM are exhibiting a higher firm value, as measured by Tobin's Q. We find a statistically significant positive relation to the magnitude of 25 percent. Upon decomposition of the maturity score, we find that the most important aspects of ERM from a valuation perspective relate to the level of top–down executive engagement and the resultant cascade of ERM culture throughout the firm. Firms that have successfully integrated the ERM process into both their strategic activities and everyday practices display superior ability in uncovering risk dependencies and correlations across the entire enterprise and as a consequence enhanced value when undertaking the ERM maturity journey ceteris paribus.

Exploring Consumer Opportunism Conundrum in the Insurance Industry: The Role of Marketing

Drawing from the extant literature, this paper explores the prevalent consumer opportunism in the insurance transactions, its links to consumers’ perception, and the relevance of marketing strategies in curbing the menace. It shows that insurance opportunism could be perpetrated by any party in the insurance transaction system and at any stage of the process involved. Among factors identified as prompting this conundrum are economic motive, resentment towards the insurance companies, laxity in the application processing/asymmetric information, and insiders’ collaborations. Nonetheless, the paper suggests that strong commitment of insurance marketers to creating and delivering value to the customers more robustly through a proactive and all-embracing implementation of marketing strategies vis-àvis relationship marketing could significantly enhance consumers’ positive perception of insurance business and consequently result in a healthier insurance industry.