Shahid Hamid

Portfolio selection and skewness: Evidence from international stock markets

Abstract This paper finds that the returns of the worlds 14 major stock markets are not normally distributed, and that the correlation matrix of these stock markets was stable during the January 1988–December 1993 time period. Polynomial goal programming, in which investor preferences for skewness can be incorporated, is utilized to determine the optimal portfolio consisting of the choices of 14 international stock indexes. The empirical findings suggest that the incorporation of skewness into an investors portfolio decision causes a major change in the construction of the optimal portfolio. The evidence also indicate that investors trade expected return of the portfolio for skewness.

Validation of a probabilistic model for hurricane insurance loss projections in Florida

The Florida Public Hurricane Loss Model is one of the first public models accessible for scrutiny to the scientific community, incorporating state of the art techniques in hurricane and vulnerability modeling. The model was developed for Florida, and is applicable to other hurricane-prone regions where construction practice is similar. The 2004 hurricane season produced substantial losses in Florida, and provided the means to validate and calibrate this model against actual claim data. This paper presents the predicted losses for several insurance portfolios corresponding to hurricanes Andrew, Charley, and Frances. The predictions are validated against the actual claim data. Physical damage predictions for external building components are also compared to observed damage. The analyses show that the predictive capabilities of the model were substantially improved after the calibration against the 2004 data. The methodology also shows that the predictive capabilities of the model could be enhanced if insurance companies report more detailed information about the structures they insure and the types of damage they suffer. This model can be a powerful tool for the study of risk reduction strategies.

Damage Characterization: Application to Florida Public Hurricane Loss Model

Florida’s population growth in recent decades has produced a steady increase in the concentration of population and infrastructure along its coastline. During this growth period, the vulnerability of the Florida coastline to hurricane impact has been amply demonstrated. The aftermaths unveiled the underlying potential for dramatic economic loss, which is disruptive for the state economy and has created a crisis in the property insurance market. To aid in preparedness efforts, the state of Florida commissioned a group of experts to develop the Florida Public Hurricane Loss Model (FPHLM). This paper describes how hurricane damage prediction techniques were developed and implemented in the vulnerability module of the FPHLM. The module consists of an external damage simulation engine coupled with internal damage and contents damage simulation engines. These components are described in this paper.

A web-based distributed system for hurricane occurrence projection

As an environmental phenomenon, hurricanes cause significant property damage and loss of life in coastal areas almost every year. Research concerning hurricanes and their aftermath is gaining more and more attention nowadays. This paper presents our work in designing and building a Web‐based distributed software system that can be used for the statistical analysis and projection of hurricane occurrences. Firstly, our system is a large‐scale system and can handle the huge amount of hurricane data and intensive computations in hurricane data analysis and projection. Secondly, it is a distributed system, which allows multiple users at different locations to access the system simultaneously and to share and exchange the data and data model. Thirdly, our system is a database‐centered system where the Oracle database is employed to store and manage the large amount of hurricane data, the hurricane model and the projection results. Finally, a three‐tier architecture has been adopted to make our system robust and resistant to the potential change in the lifetime of the system. This paper focuses on the three‐tier system architecture, describing the design and implementation of the components at each layer. Copyright

Marginal risk aversion and preferences in a betting market

An individuals behavioural attitudes toward variance and non-symmetry in the payoff distributions of pari-mutuel gambles are empirically examined using the von Neumann - Morgenstern expected utility of wealth paradigm. Preferences over payoff distributions for a representative bettor are estimated from observed payoffs at a greyhound racetrack. The results indicate that the representative bettor exhibits increasing absolute risk aversion and, given that the representative bettor is locally non-satiated with regard to wealth, exhibits preference for variance and aversion to positive skewness in the payoff distributions of the gambles examined.

A three-tier system architecture design and development for hurricane occurrence simulation

As an environmental phenomenon, hurricanes cause significant property damage and loss of life in coastal areas almost every year. Research concerning hurricanes and their aftermath is gaining more and more attention. The potential changeability of hurricane data and hurricane models requires robust, maintainable and easily extensible software system for hurricane simulation. Focusing on the design and implementation of the components in each layer, we describe a hurricane simulation system built on the three-tier architecture to achieve good flexibility, extensibility and resistance to potential changes.

A reliable Web-based system for hurricane analysis and simulation

In this paper, we present our research and development efforts toward a reliable, large-scale, Web-based system for hurricane analysis and simulation. The major contributions of this system lie in the following three aspects: (1) it supports the multidisciplinary research efforts in predicting and simulating the hurricane damages and insured losses; (2) it is a large-scale, distributed, Web-based system, which allows both the professional and general users to conduct computational intensive on-line analysis simultaneously and to share and exchange the information; and (3) database management techniques are employed in our system to manage the huge amount of hurricane-related historical and simulated data.

Cost-and-Benefit Evaluation of Windstorm Damage Mitigation Techniques in Florida

AbstractThe hurricanes of recent years have caused many insurers to raise their premiums in response to increased losses. In the long-term, the most effective solution to reduce damage and insurance costs is to apply mitigation techniques. This paper presents a methodology for evaluating the effectiveness of various mitigation measures in reducing wind storm losses to residential buildings. The individual mitigations were combined into different sets. These sets of mitigations were applied to typical timber box and masonry residential structures of different ages and quality of construction (from weak pre-1970 to stronger post-2002 construction). In each case, a detailed cost analysis of the unmitigated and mitigated building was performed, and the relative cost-effectiveness of mitigation was assessed through comparisons of the results of portfolio analyses with and without mitigation. The mitigation cost-effectiveness study includes component vulnerabilities from Monte Carlo simulation, overall building...

Domain Knowledge Assisted Data Processing for Florida Public Hurricane Loss Model (Invited Paper)

Catastrophes have caused tremendous damages in human history and triggered record high post-disaster relief from the governments. The research of catastrophic modeling can help estimate the effects of natural disasters like hurricanes, floods, surges, and earthquakes. In every Atlantic hurricane season, the state of Florida in the United States has the potential to suffer economic and human losses from hurricanes. The Florida Public Hurricane Loss Model (FPHLM), funded by the Florida Office of Insurance Regulation, has assisted Florida and the residential insurance industry for more than a decade. How to process big data for historical hurricanes and insurance companies remains a challenging research topic for cat models. In this paper, the FPHLMs novel integrated domain knowledge assisted big data processing system is introduced and its effectiveness of data processing error prevention is presented.

Modeling Methodology for Component Reuse and System Integration for Hurricane Loss Projection Application

Hurricanes are one of the deadliest and perilous natural calamities on the face of earth having a severe impact both on the lives of the people and economy of a nation. Attempts have been made to mitigate hurricane aftermath, by utilizing research and tools that can analyze hurricanes and estimate projected losses. The need for such research methodologies and tools stimulated the development of a multi-disciplinary cutting edge public hurricane model called public hurricane risk and insured loss projection model (PHRLM). The complex and diverse nature of the application raises the need for module abstraction, seamless integration and effective reusability to create a uniform generic environment. Providing efficient interaction between these complex multi-disciplinary modules using different abstractions, formalisms, data formats and communications and making each module transparent enough to be reusable becomes a complicated task. This paper presents a UML based formal modeling methodology, enabling component reuse and integration of the application