Section 4 : Cats deep dive Flashcards
Why is historical loss data insufficient for catastrophe modeling?
Because catastrophe losses are rare and highly volatile, requiring simulations of hypothetical future events.
!! What are the five key components of a catastrophe model?
- Event Module: Defines stochastic events (e.g., hurricanes, earthquakes).
- Hazard Module: Determines physical risk (e.g., wind speed, flood depth).
- Vulnerability Module: Converts hazard impact into damage estimates.
- Financial Analysis Module: Translates damages into insured losses based on policy terms.
- Exposure Module: Represents insured properties and assets.
What does an Average Annual Loss (AAL) represent?
The expected value of catastrophe losses in a given year.
What are Occurrence EP and Aggregate EP curves?
- OEP Curve: Probability that the largest event in a year exceeds a threshold.
- AEP Curve: Probability that total losses from all events in a year exceed a threshold.
!! How do catastrophe models assist insurers in setting rates and capital requirements?
Pricing: Helps determine risk-based premiums by estimating probable losses.
Reinsurance decisions: Identifies optimal coverage to transfer extreme risks.
Regulatory capital: Supports solvency assessments and capital adequacy calculations.
Portfolio risk management: Assesses exposure concentration and diversification strategies.
What is secondary uncertainty in catastrophe modeling?
Variability in damage outcomes for the same event due to structural differences.
What is Tail Value at Risk (TVAR)?
The average loss expected beyond a specific exceedance probability.
How does the vulnerability module work?
It estimates the potential damage to buildings and infrastructure given the hazard intensity at a location.
What is a return period in catastrophe modelling?
A return period estimates the average time between events of a certain magnitude.
Example: A 1-in-100-year flood has a 1% probability of occurring each year.
How does climate change impact catastrophe models?
Historical data may underestimate future risks due to changing climate patterns.
New scenarios are integrated into models to adjust for rising temperatures, sea levels, and extreme weather events.
Imagine you are an insurance company using catastrophe models. A new residential area is being developed in a flood-prone region. How would you use catastrophe models to determine the insurance premium for homes in this area?
I would analyze historical flood data, use the hazard module to assess flood risks, apply the vulnerability module to estimate property damage, and use financial modelling to determine expected losses and appropriate premiums.
What is a Stochastic Event Set?
It is a set of simulated catastrophe events based on historical data and scientific understanding, including possible future events that may have never occurred before.
What does the event module in a catastrophe model do?
It defines a database of potential disaster events, specifying their location, intensity, and probability of occurrence.
What is the function of the hazard module in catastrophe modelling?
It determines the intensity of a disaster at various locations (e.g., wind speed for cyclones, ground shaking for earthquakes).
How does the financial analysis module work in catastrophe modelling?
It applies insurance policy conditions (e.g., deductibles, coverage limits) to calculate the insured losses from a catastrophe event.
Why is the exposure module important in catastrophe modelling?
It contains data on insured buildings and assets, helping determine how vulnerable an area is to disasters.
!! What are some challenges faced in catastrophe modelling?
- Data limitations: Lack of historical flood data, especially for low-frequency events.
- Climate change impact: Increased variability and unpredictability in flood patterns.
- Model validation: Difficulty in assessing accuracy due to limited real-world comparisons.
- Regulatory constraints: Different jurisdictions have varying flood insurance requirements.
How do reinsurers use catastrophe models?
They use models to estimate potential losses, set premium prices, and determine how much reinsurance coverage to offer insurers.
How do catastrophe models help insurers manage risk?
- Pricing policies based on expected losses
- Identifying high-risk areas
- Making informed reinsurance and capital allocation decisions
Why might relying only on historical data for catastrophe modelling be problematic? Can you think of an example?
Climate change and urbanization alter risk factors. For example, hurricane models based on past decades might underestimate the frequency and intensity of future hurricanes due to warming ocean temperatures.
Two catastrophe models predict vastly different expected losses for the same event. How should an insurer interpret and act on this?
The insurer should compare the assumptions behind the models, conduct sensitivity analysis, consult independent experts, and consider a range of possible outcomes instead of relying on a single model.
If a catastrophe model predicts extremely high losses for a specific region, leading insurers to deny coverage or raise premiums, what ethical concerns arise?
This could lead to insurance inaccessibility, disproportionately impacting low-income communities. Ethical concerns include data transparency, fairness, and the potential reinforcement of socioeconomic inequalities.
How should catastrophe models be adapted to account for climate change?
They should integrate updated climate projections, adjust hazard frequencies based on changing weather patterns, and consider future exposure shifts due to human development and migration.
You work for a reinsurance company. How would you use catastrophe models to decide whether to provide coverage for a region prone to earthquakes?
I would assess the frequency and severity of historical earthquakes, analyze exposure data, model worst-case financial impacts, and determine whether premiums can cover risks profitably.
!! What are the challenges in applying catastrophe models to flood insurance?
Data limitations: Lack of historical flood data, especially for low-frequency events.
Climate change impact: Increased variability and unpredictability in flood patterns.
Model validation: Difficulty in assessing accuracy due to limited real-world comparisons.
Regulatory constraints: Different jurisdictions have varying flood insurance requirements.
What is the difference between Event Loss Tables (ELT) and Year Loss Tables (YLT) in catastrophe modelling?
ELT: Shows losses for each individual simulated event.
YLT: Summarizes all events in a given simulated year, giving total annual losses.
How do actuaries ensure that catastrophe models provide reliable loss estimates?
- Event frequency to historical occurrences.
- Modelled damage distributions to real post-event data.
- Intensity footprints of past disasters (e.g., wind speed, flood depth) to recorded measurements.
Why is risk load added to catastrophe model outputs in insurance pricing?
Since catastrophe events have high volatility, risk load accounts for uncertainty in losses, helping insurers price policies to maintain profitability.
How does Canada’s approach to catastrophe insurance pricing differ from the United States?
Canada lacks federal flood insurance; private insurers price based on historical experience + models.
U.S. has government-backed flood insurance (NFIP), requiring insurers to follow state regulations.
Why do similar buildings in the same location sometimes experience very different losses in a catastrophe event?
Due to secondary uncertainty, where construction quality, building age, and local factors lead to variation in damage even under identical hazard conditions.
What is the key difference between an Exceedance Probability (EP) curve and Value at Risk (VaR)?
EP Curve: Shows probability of exceeding various loss thresholds.
VaR: Focuses on a specific probability level (e.g., 1% VaR is the worst loss at a 99% confidence level).
Why might traditional catastrophe models underestimate future risks due to climate change?
Many models rely on historical averages, which do not fully capture rising sea levels, increased storm intensity, or changing flood patterns due to climate change.
How can catastrophe models create ethical challenges in insurance pricing?
- High-risk areas (e.g., flood zones) may face unaffordable premiums, leading to insurance inaccessibility.
- Bias in models (e.g., outdated flood maps) can disadvantage certain communities.
- Lack of transparency in model assumptions can impact policyholder trust.
!! What are the primary outputs of catastrophe models, and why are they important?
- Occurrence Exceedance Probability (OEP): Probability of the largest event exceeding a loss threshold.
- Aggregate Exceedance Probability (AEP): Probability that total losses in a year exceed a given amount.
- Loss Cost Estimates: Expected loss per unit of exposure.
- Tail Value at Risk (TVaR): Measures extreme loss potential beyond a set threshold.
!! What factors influence the uncertainty in catastrophe model outputs?
- Primary uncertainty: Variability in event occurrence (e.g., where and when a flood happens).
- Secondary uncertainty: Differences in loss severity given an event occurs (e.g., buildings with similar exposure experiencing different damages).
- Data quality: Accuracy of input data, including property attributes and historical records.
- Model assumptions: Variability in hazard frequency, intensity, and damage curves.
