Seminararbeit, 2012
29 Seiten, Note: 2,0
1 Introduction
2 Characteristics of Industry Loss Warranties
2.1 Examples of an Industry Loss Warranty contract
2.2 Basis Risk
2.3 Comparison with other risk-transfer instruments
3 Simulation Study
3.1 Approach
3.1.1 Measuring basis risk
3.1.2 Premium calculation
3.1.3 Risk measurement
3.2 Numerical analysis
3.2.1 Value at risk and tail value at risk
3.2.2 Varying coefficient of correlation
3.2.3 Varying industry loss trigger
3.2.4 Varying retention
3.2.5 Varying limit of protection
4 Summary
This paper examines the fundamental characteristics of Industry Loss Warranties (ILWs) and evaluates their hedging effectiveness compared to traditional reinsurance through a Monte Carlo simulation. The study specifically analyzes the sensitivity of basis risk and pricing to various input parameters to determine the practical suitability of these instruments for risk management.
2.2 Basis Risk
As the preceding paragraphs indicate the industry loss warranties always contain a certain basis risk. Zeng (2000) defines basis risk as a “conditional probability that the ILW policy does not pay off given the actual loss sustained by the policyholder exceeds a critical level.” (Zeng, 2000, p. 27). It means that sustained losses of the insured company are severe while the industry-wide losses are not triggered, so the damage is not covered. It can occur if the policyholder’s losses and industry-wide losses are not fully correlated. (see Zeng, 2000, p. 27; World Economic Forum (WEF), 2008, p. 18-19). Basis risk means a difference between index based payoff and the company’s actual losses (see Gatzert, Schmeiser and Toplek, 2007, p. 3; WEF, p. 8). On the opposite side, the discrepancy can also lead to a basis gain (see Zeng, 2000, p. 27), i.e. the actual loss is quite small whereas the industry-wide loss is substantial and exceeds the threshold leading to pay out.
Basis risk varies on the trigger used, on the portfolio of sponsor, on the index data quality and on the specific peril (see WEF, 2008, p. 18). There are various types of triggers; some of them imply greater basis risk than others:
- An industry loss index is based on the industry-wide index of losses provided by an independent reporting agency like PCS in the US.
- An indemnity trigger depends on the actual loss of the policyholder.
- A modeled loss trigger determines the losses by entering the actual physical data into an agreed-upon model, which then calculates the losses.
- In a Modeled Industry Trigger Transaction (“MITT”) the industry index is calculated post-event using modeled loss techniques.
- A pure parametric trigger is based on the actual reported peril, e.g. earthquake magnitude or wind speed of hurricane.
- A parametric index is a refined version of a pure parametric trigger using more complicated formulas and more detailed measurements (see WEF, 2008, p. 10; SwissRe, 2009, p. 6).
1 Introduction: This chapter outlines the research goal of analyzing basis risk and pricing sensitivity of ILWs using an Excel-based simulation study.
2 Characteristics of Industry Loss Warranties: Provides an overview of ILW types, triggers, and a comparison with other risk-transfer instruments like cat bonds and reinsurance.
3 Simulation Study: Details the methodological approach, including Monte Carlo simulation and Gaussian Copula concepts, and performs a numerical sensitivity analysis on key drivers.
4 Summary: Concludes the paper by synthesizing the findings regarding the competitive advantages and limitations of ILWs in modern risk management.
Industry Loss Warranties, ILW, Basis Risk, Risk Transfer, Reinsurance, Monte Carlo Simulation, Gaussian Copula, Catastrophe Bonds, Indemnity Trigger, Industry Trigger, Pricing Sensitivity, Hedging Effectiveness, Value at Risk, Tail Value at Risk, Insurance-linked Securities.
The paper focuses on Industry Loss Warranties (ILWs) and specifically analyzes their potential basis risk and pricing sensitivities through simulation.
The study primarily compares Industry Loss Warranties (both binary and indemnity-based) with traditional reinsurance contracts.
The primary goal is to perform a risk and pricing sensitivity analysis to assess the hedging effectiveness of ILWs in different catastrophe scenarios.
The author uses a Monte Carlo simulation approach combined with the Gaussian Copula concept to generate dependent random variables for company and industry losses.
The main body covers the characteristics of ILW contracts, the definition and measurement of basis risk, and a detailed numerical analysis of how parameters like correlation and triggers impact premiums.
The work is characterized by terms such as Basis Risk, ILW, Insurance-linked Securities, and Hedging Effectiveness.
The correlation coefficient is crucial because higher correlation between company and industry losses significantly reduces basis risk, making the instrument more attractive to buyers.
Increasing the industry loss trigger leads to higher basis risk, which often makes the ILW contract less attractive to potential buyers despite potential shifts in premium pricing.
Binary ILWs pay out based solely on the industry index trigger, whereas indemnity-based ILWs require both the industry trigger and the sponsor's own loss threshold to be met.
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