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#11




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#13




The slip may have been more Freudian than typo. Models can be addictive  especially your own models. Withdrawal can be traumatic, but it gets easier with time and experience.

#14




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It's messy. Just trying to think of a way of simplifying it so that the model is easier to use.
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https://www.variancejournal.org/arti...iesMeyers.pdf I was working at ISO at the time when I wrote a series of paper on financing insurance. We had loss distributions by line of business and by development period. We tried to make the "Cost of Financing" idea into a product. But we could not get enough sales to keep it going. If I were doing this now in an insurance company environment, I think I would use the assembling loss triangles approach as opposed to the collective risk model approach. 
#16




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This is no doubt true. The 3 big reinsurance brokers each have their own model. But in some sense, this is like asking the diamond merchant how much you should spend on a diamond. I may be a cynic, but I believe the result of the model is skewed to favor more purchases, and purchases in the brokers' most profitable lines (i.e. CAT)
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Come on. Let's go space truckin'. Come on! 
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#18




My thinking on financing insurance has been evolving for almost all of my actuarial career. At last November’s CAS meeting I attempted to describe this evolution. Here is the link to my presentation.
https://www.casact.org/education/ann...ations/C1.pdf There were two considerations that led me from the collective risk model approach dating from around 2000, to the stochastic loss reserving approach I suggested trying in my previous post. They were ease of implementation and ease of gaining acceptance. Implementation first  The collective risk model approach involved fitting a lot of severity distribution and making educated guesses (with some modeling assistance) of contagion parameters and correlations. As I gave presentations (sales pitches) to a number of insurers, I sensed significant unease with the number of estimates, from external data, that we had to string together. In short, the collective risk model approach was a “bleeding edge” approach. By contrast, as I showed in my recent risk margin paper, if you have a loss triangle, and can agree on some rates of return, you can calculate a risk margin for a line of insurance in minutes. There should be some discussion on how to combine lines, but I will say that the dependency problem is overrated. If you have a dependency problem, the real problem is the model selection problem. As for reinsurance optimization, if you have good claims IT support, you should be able to construct loss triangles for a variety of proposed reinsurance programs. It then becomes a matter of comparing the cost of capital for the various reinsurance proposals. Now acceptance  As my day job moved on to predictive modeling, in my extracurricular activities I got a chance to watch the development of Solvency II, IFRS 17 and related initiatives through my IAA/ASTIN participation. There I saw a growing acceptance of capital modeling and parallels between my thinking and others on cost of capital risk margins. The weak point in all of this was getting a distribution of capital cash flows. Enter Bayesian MCMC  As I was nearing retirement I, with the help of others, managed to get set up the CAS Loss Reserve Database. Also, I was able to latch onto Bayesian MCMC as it was beginning to mature. So I began my little retirement project. Bayesian MCMC is ideal for stochastic loss reserving. My sense is that it has yet to gain mainstream acceptance for loss reserving, but I like its trajectory. Bayesian MCMC will give you a lot (10,000) of equally likely cash flows for risk margin calculations. There is one thing I want to make clear  I never considered stochastic loss reserve modeling to be the end product. I was always after the risk margin. 
#19




Subscribe. Great thread so far. Reading Glenn’s MCMC paper is on my todo list. A coworker of mine implemented Glenn’s changing settlement rate method and I need to better understand how it works.
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har·bin·ger (här'binjer): One that indicates or foreshadows what is to come; a forerunner. 
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