Catastrophic Response and Disaster Recovery: An Industry Panel on Best Practices

Natural disasters and man-made failures or defects cause damage to buildings and its contents disrupting people’s lives, businesses and the communities they live in. On a worldwide scale billions of dollars are spent to mitigate, restore and recover from a one-time residential incident, a single site large loss or for an area wide catastrophe. This 2 part panel presentation will offer a perspective on the best practices from 3 different industry sectors from initial response to full recovery. Representatives from the Restoration, Demolition and Volunteer Assistance industries will provide valuable insights to best practices and offer solutions on how industry can collaborate with academia, government, the social sector and community based organizations to better serve the victims affected by a disaster. The ongoing goal of response and recovery should be for the stakeholders to share the lessons learned from previous disasters to help recover and build more resilient communities. Working together to develop emergency response plans and establishing a resource base before a disaster happens can better prepare for the recovery process. The recovery process is not only about the restoration of people\u27s dwellings and personal belonging, but also about restoring ones peace on mind so they can resume their life after the event! Dealing with an insurance company or government agency can often be a cumbersome and frightening ordeal for first time disaster victims The industry panel will share their experience on how to best handle financial considerations as well as the physical and emotional aspects of the loss recovery process

A stochastic programming model for dynamic portfolio management with financial derivatives

Stochastic optimization models have been extensively applied to financial portfolios and have proven their effectiveness in asset and asset-liability management. Occasionally, however, they have been applied to dynamic portfolio problems including not only assets traded in secondary markets but also derivative contracts such as options or futures with their dedicated payoff functions. Such extension allows the construction of asymmetric payoffs for hedging or speculative purposes but also leads to several mathematical issues. Derivatives-based nonlinear portfolios in a discrete multistage stochastic programming (MSP) framework can be potentially very beneficial to shape dynamically a portfolio return distribution and attain superior performance. In this article we present a portfolio model with equity options, which extends significantly previous efforts in this area, and analyse the potential of such extension from a modeling and methodological viewpoints. We consider an asset universe and model portfolio set-up including equity, bonds, money market, a volatility-based exchange-traded-fund (ETF) and over-the-counter (OTC) option contracts on the equity. Relying on this market structure we formulate and analyse, to the best of our knowledge, for the first time, a comprehensive set of optimal option strategies in a discrete framework, including canonical protective puts, covered calls and straddles, as well as more advanced combined strategies based on equity options and the volatility index. The problem formulation relies on a data-driven scenario generation method for asset returns and option prices consistent with arbitrage-free conditions and incomplete market assumptions. The joint inclusion of option contracts and the VIX as asset class in a dynamic portfolio problem extends previous efforts in the domain of volatility-driven optimal policies. By introducing an optimal trade-off problem based on expected wealth and Conditional Value-at-Risk (CVaR), we formulate the problem as a stochastic linear program and present an extended set of numerical results across different market phases, to discuss the interplay among asset classes and options, relevant to financial engineers and fund managers. We find that options’ portfolios and trading in options strengthen an effective tail risk control, and help shaping portfolios returns’ distributions, consistently with an investor's risk attitude. Furthermore the introduction of a volatility index in the asset universe, jointly with equity options, leads to superior risk-adjusted returns, both in- and out-of-sample, as shown in the final case-study

Modeling multi-population life expectancy: a cointegration approach

The continuous improvements in mortality rates and life expectancy of the last century have been given a great deal of attention by academics, life insurers, financial engineers, and pension planners, particularly in developed countries. Mortality-linked securities such as longevity bonds (EIB & BNP as well as the Swiss Re bond), survivor swaps, and mortality forward (q-forward) have appeared recently in the industry to help operators hedge such risks. A classic survivor bond has been proposed in the literature with coupon payment linked to the life time of the last survivor in an insurance reference portfolio. It appears therefore to be crucial to improve the accuracy of future life expectancy forecasts. In this paper, the authors investigate time-varying dependency associated with common trends that drive regional life expectancy within Canada. The aim is to compare three major models that have recently appeared in the literature, the autoregressive integrated moving average (ARIMA), the vector autoregressive model (VAR) and the vector error correction model (VECM), to analyze the common factors that have determined a progressive shift of life expectancy in specific Canadian regions. Results show that VECM performs better than VAR and ARIMA in terms of backtesting and its ability to capture the dynamics of common life expectancy. Findings from these analyses are useful for local insurers and demographers in their goal to project life expectancy improvements and also to forecast future trends

Projecting the long run relationship of multi-population life expectancy by race

Census data demonstrate that life expectancy has improved over the last 100 years. In this paper, we attempt to predict life expectancy in the USA by both race( black people, white people, and the general population) and gender. In doing this, we employ methods of cointegration analysis that have appeared recently in the actuarial and demography literature. We investigate the dependence(through cointegration analysis) between the six variables and it shows a better fit with better performance than others models such as VAR and ARIMA in predicting life expectancy at birth by race. We show that there are similar long-term trends in the average life expectancy of the members of all the main racial groups residing in the USA. Our study offers new insights to demographers with regard to predicting the average future life expectancy of members of different racial groups

Divergent Effects of Human Cytomegalovirus and Herpes Simplex Virus-1 on Cellular Metabolism

Viruses rely on the metabolic network of the host cell to provide energy and macromolecular precursors to fuel viral replication. Here we used mass spectrometry to examine the impact of two related herpesviruses, human cytomegalovirus (HCMV) and herpes simplex virus type-1 (HSV-1), on the metabolism of fibroblast and epithelial host cells. Each virus triggered strong metabolic changes that were conserved across different host cell types. The metabolic effects of the two viruses were, however, largely distinct. HCMV but not HSV-1 increased glycolytic flux. HCMV profoundly increased TCA compound levels and flow of two carbon units required for TCA cycle turning and fatty acid synthesis. HSV-1 increased anapleurotic influx to the TCA cycle through pyruvate carboxylase, feeding pyrimidine biosynthesis. Thus, these two related herpesviruses drive diverse host cells to execute distinct, virus-specific metabolic programs. Current drugs target nucleotide metabolism for treatment of both viruses. Although our results confirm that this is a robust target for HSV-1, therapeutic interventions at other points in metabolism might prove more effective for treatment of HCMV

Personalized goal-based investing via multi-stage stochastic goal programming

In this paper, we propose a goal-based investment model that is suitable for personalized wealth management. The model only requires a few intuitive inputs such as size of wealth, investment amount, and consumption goals from individual investors. In particular, a priority level can be assigned to each consumption goal and the model provides a holistic solution based on a sequential approach starting with the highest priority. This allows strict prioritization by maximizing the probability of achieving higher priority goals that are not affected by goals with lower priorities. Furthermore, the proposed model is formulated as a linear program that efficiently finds the optimal financial plan. With its simplicity, flexibility, and computational efficiency, the proposed goal-based investment model provides a new framework for automated investment management services