THE REINSURANCE ACTUARY
  • Blog
  • Project Euler
  • Category Theory
  • Disclaimer

Converting a Return Period to a RoL

15/3/2019

 


I came across a useful way of looking at Rate on Lines last week, I was talking to a broker about what return periods to use in a model for various levels of airline market loss (USD250m, USD500m, etc.). The model was intended to be just a very high level, transparent market level model which we could use as a framework to discuss with an underwriter. We were talking through the reasonableness of the assumptions when the broker came out with the following:
 
'Well, you’d pay about 12.5 on line in the retro market at that attachment level, so that’s a 1 in 7 break-even right?'
 
My response was:

'ummmm, come again?'

 
His reasoning was as follows:
 
Suppose the ILW pays $1$ @ $100$% reinstatements, and that it costs $12.5$​% on line.
Then if the layer suffers a loss, the insured will have a net position on the contract of $75$%. This is the $100$% limit which they receive due to the loss, minus the original $12.5$% Premium, minus an additional $12.5$% reinstatement Premium. The reinsurer will now need another $6$ years at $12.5$% RoL $(0.0125 * 6 = 0.75)$ to recover the limit and be at break-even.
 
Here is a breakdown of the cashflow over the seven years for a $10m$ stretch at $12.5$% RoL:
 
Picture
So the loss year plus the six clean years, tells us that if a loss occurs once every 7 years, then the contract is at break-even for this level of RoL.

So this is kind of cool - any time we have a RoL for a retro layer, we can immediately convert it to a Return Period for a loss which would trigger the layer.
 
Generalisation 1 – various rates on line
 
We can then generalise this reasoning to apply to a layer with an arbitrary RoL. Using the same reasoning as above, the break-even return period ends up being:
 
$RP= 1 + \frac{(1-2*RoL)}{RoL}$
 
Inverting this gives:
 
$RoL = \frac{1}{(1 + RP)}$
 
So let's say we have an ILW costing $7.5$% on line, the break-even return period is:
 
$1 + \frac{(1-0.15)}{0.075} = 11.3$
 
Or let’s suppose we have a $1$ in $19$ return period, the RoL will be:
 
$0.05 = \frac{1}{(1 + 19)}$
 
Generalisation 2 – other non-proportional layers
 
The formula we derived above was originally intended to apply to ILWs, but it also holds any time we think the loss to the layer, if it occurs, will be a total loss. This might be the case for a cat layer, or a clash layers (layers which have an attachment above the underwriting limit for a single risk), or any layer with a relatively high attachment point compared to the underwriting limit.
 
Adjustments to the formulas
 
There are a few of adjustments we might need to make to these formulas before using them in practice.
 
Firstly, the RoL above has no allowance for profit or expense loading, we can account for this by converting the market RoL to a technical RoL, this is done by simply dividing the RoL by $120-130$% (or any other appropriate profit/expense loading). This has the effect of increasing the number of years before the loss is expected to occur.
 
Alternately, if layer does not have a paid reinstatement, or has a different factor than $100$%, then we would need to amend the multiple we are multiplying the RoL by in the formula above. For example, with nil paid reinstatements, the formula would be:
 
$RP = 1 + \frac{(1-RoL)}{RoL}$
 
Another refinement we might wish to make would be to weaken the total loss assumption.  We would then need to reduce the RoL by an appropriate amount to account for the possibility of partial losses. It’s going to be quite hard to say how much this should be adjusted for – the lower the layer the more it would need to be.


Your comment will be posted after it is approved.


Leave a Reply.

    Author

    ​​I work as an actuary and underwriter at a global reinsurer in London.

    I mainly write about Maths, Finance, and Technology.
    ​
    If you would like to get in touch, then feel free to send me an email at:

    ​LewisWalshActuary@gmail.com

      Sign up to get updates when new posts are added​

    Subscribe

    RSS Feed

    Categories

    All
    Actuarial Careers/Exams
    Actuarial Modelling
    Bitcoin/Blockchain
    Book Reviews
    Economics
    Finance
    Forecasting
    Insurance
    Law
    Machine Learning
    Maths
    Misc
    Physics/Chemistry
    Poker
    Puzzles/Problems
    Statistics
    VBA

    Archives

    March 2023
    February 2023
    October 2022
    July 2022
    June 2022
    May 2022
    April 2022
    March 2022
    October 2021
    September 2021
    August 2021
    July 2021
    April 2021
    March 2021
    February 2021
    January 2021
    December 2020
    November 2020
    October 2020
    September 2020
    August 2020
    May 2020
    March 2020
    February 2020
    January 2020
    December 2019
    November 2019
    October 2019
    September 2019
    April 2019
    March 2019
    August 2018
    July 2018
    June 2018
    March 2018
    February 2018
    January 2018
    December 2017
    November 2017
    October 2017
    September 2017
    June 2017
    May 2017
    April 2017
    March 2017
    February 2017
    December 2016
    November 2016
    October 2016
    September 2016
    August 2016
    July 2016
    June 2016
    April 2016
    January 2016

  • Blog
  • Project Euler
  • Category Theory
  • Disclaimer