Preview Questions Actuary | Role Specific Skill

Question 1: In the context of insurance pricing, what is the key advantage of using a Generalized Linear Model (GLM)?

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It allows for the modeling of relationships between predictors and outcomes while accounting for skewness and heteroscedasticity.

It simplifies the estimation of premiums by reducing the number of variables.

It is computationally simpler than traditional regression models.

It works best with normally distributed data.

Question 2: Which model would be most appropriate for interpreting the tail risk of this distribution?

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Poisson distribution

Generalized Pareto Distribution (GPD)

Binomial distribution

Normal distribution

Question 3: When using Monte Carlo simulations in actuarial modeling, which of the following is typically modeled?

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The correlation between claims and investment returns

The historical trend of claims in a portfolio

The exact timing of future claims

The distribution of future claims or cash flows

Question 4: What is the primary difference between "systematic risk" and "unsystematic risk" in financial risk assessment?

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Systematic risk affects the entire market, while unsystematic risk affects specific companies or industries

Systematic risk is always higher than unsystematic risk

Systematic risk can be diversified away, while unsystematic risk cannot

Systematic risk is related to operational failures, while unsystematic risk is related to market fluctuations

Question 5: In a pricing model for annuities, what would you use to adjust for interest rate risk over the expected term of the policy?

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Liability-driven investment (LDI) strategy

Use of stochastic discount rates

Discounted cash flow (DCF) method

Modified duration analysis

Question 6: What is the significance of "Bayesian Inference" in actuarial modeling, and how do you apply it to update risk predictions with new data?

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Bayesian inference allows the updating of prior beliefs with new data to refine risk predictions, offering flexibility in handling uncertainty

Bayesian inference is only useful in academic research, not in practical actuarial applications

Bayesian inference is irrelevant in actuarial modeling as frequentist methods are preferred

Bayesian methods should be avoided due to their complexity and computational cost