Mildenhall Chapter 2 + 3 Flashcards

(9 cards)

1
Q

Risk: Uncertainty of achieving objectives

Pure/Insurance, Speculative/Asset, Financial Risk

A

Pure/Insurance risk - possible bad outcome with no potential of good outcome (e.g. loss on an insurance policy)

Speculative/Asset Risk - could have good or ad outcome (e.g. insurance, premium - loss/expense, is a speculative risk)

Financial Risk - uncertainty of financial outcomes - uncertainty in timing, amount or both (e.g. insurance reduces financial risk of the insured by covering a portion of the loss and specifiying payment dates)

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2
Q

Systematic Risk vs Systemic Risk

Defintion and example

A

Systematic Risk - cannot be reduced by diversification
(e.g. cats, will impact many insureds)

Systemic Risk - where an individual event can cause a chain reaction of additional events (cats are not systemic, does not trigger additional cats)

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3
Q

Labeling Risky Outcomes

Explicit Representation

What is it? Pro/Con

A

Identifies an event by providing specific details about it (sample space is interpretable sample points e.g. EQ magnitude, epicenter)

E.g 9-digit catastrophe identifier

Pros - distinguishes between different events/outcomes, enables outcomes to be linked across a book of business risk, helps modeling dependence risk

Cons - if there’s too many possible events, or if events only impact a small portion of your book … unrealistic to tie an event to each individual policyholder

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4
Q

Labeling Risky Outcomes

Implicit Representation

What is it? Pro/Con

A

Identifies an outcome with its value (sample space is -∞ to ∞)
e.g. for losses, size of the loss

Pros - good if we only care about loss outcome rather than the cause of loss

Cons
* difficult to aggregate, no way to link different outcomes of an event.
* difficult to specify dependence
* cannot distinguish between different events with same outcome

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5
Q

Labeling Risky Outcomes

Dual Implicit Representation, Exceedance/Nonexceedance Probability

What is it? Pro/Con

A

Identifies the rank of the outcomes (sample space is 0 to 1)
e.g. investors assess bonds based on p(default) or catastrophe models summarized by exceedance prob

Useful when we don’t care about the amount of loss

Exceedance s = S(x) = P(X > x)
Non-Exceedance p = F(x) = P(X ≤ x)

Pros - easy to make comparisons, F(X) always lies [0,1]

Cons - difficult to aggregate

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6
Q

Risk Measures

Risk Preference Properties

A
  1. Complete - for any pair of losses X & Y, can conclude X ≽ Y, Y ≽ X, or both
  2. Transitive if X ≽ Y and Y ≽ Z then X ≽ Z
  3. Monotonic if X ≤ Y for all outcomes then X ≽ Y
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7
Q

Risk Measures

Risk Measure

What is it? What impacts it?

A

Risk measure (p) quantifies risk preference (via a number)
X ≽ Y <–> p(X) ≤ p(Y)

Factors
* Volume - smaller risks are preferred
* Volatility - lower volatility (variance / SD) is preferred
* Tail - lower likelihood of extreme outcomes is preferred

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8
Q

Risk Measures

Capital Risk Measure

What is it? Use cases?

A

Determines the assets needed to back an existing or hypothetical portfolio at a given level of confidence

Use cases
* management determining how much assets to set aside
* regulator determining minimum capital requirement
* rating agency to rate how adequate an insurer’s capital is (measure VaR or TVaR)

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9
Q

Risk Measures

Pricing Risk Measure

A

Determine the expected profit insureds need to pay in total to make it worthwhile for investors to bear the portfolio’s risk

The margin (prem - expected loss) needs to be high enough to attract necessar capital form investors

More sensitive to volatility

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