Publications by arthur charpentier
Longevity and mortality dynamics with R
Following the previous post on life contingencies and actuarial models in life insurance, I upload additional material for the short course at the 6th R/Rmetrics Meielisalp Workshop & Summer School on Computational Finance and Financial Engineering organized by ETH Zürich, https://www.rmetrics.org/. The second part of the talk (on Actuarial mo...
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Claims reserving and IBNR with R
Following previous posts on life contingencies and longevity and mortality models, I upload additional material for the short course at the 6th R/Rmetrics Meielisalp Workshop & Summer School on Computational Finance and Financial Engineering organized by ETH Zürich, https://www.rmetrics.org/. The third part of the talk (on Actuarial models wit...
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Do you still have time to sleep ?
Last week, @3wen (Ewen) helped me to write nice R functions to extract tweets in R and build datasets containing a lot of information. I’ve tried a couple of time on my own. Once on tweet contents, but it was not convincing and once on the activity on Twitter following an event (e.g. the death of someone famous). I have to admit that I am not a...
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Date of death, birthday and Elvis Presley
10 days ago, a study published on http://www.annalsofepidemiology.org/ mentioned that “Death has a preference for birthdays” (as claimed in the title). The conclusion of the paper is that, in general, birthdays do not evoke a postponement mechanism but appear to end up in a lethal way more frequently than expected (“anniversary reaction”)...
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Pricing options on multiple assets (part 1) with trees
I am a big fan of trees. It is a very nice way to see how financial pricing works, for derivatives. An with a matrix-based language (R for instance), it is extremely simple to compute almost everything. Even multiple assets options. Let us see how it works. But first, I have to assume that everyone knows about trees (or at least is familiar), an...
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Actuarial models with R, Meielisalp
I will be giving a short course in Switzerland next week, at the 6th R/Rmetrics Meielisalp Workshop & Summer School on Computational Finance and Financial Engineering organized by ETH Zürich, https://www.rmetrics.org/. The long version of the slides for the course on Actuarial models with R can be found online with the #Rmetrics tag, and the...
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Simple and heuristic optimization
This week, at the Rmetrics conference, there has been an interesting discussion about heuristic optimization. The starting point was simple: in complex optimization problems (here we mean with a lot of local maxima, for instance), we do not necessarily need extremely advanced algorithms that do converge extremly fast, if we cannot ensure that th...
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Visualizing uncertainty using Jackknife
Once again, I (re)discovered last week at the Rmetrics conference that old toolds can be extremely interesting to illustrate complex ideas, like uncertainty in fnancial markets, and stock prices. For instance a 99.5% quantile: we look for the scenario that occur with a probability of 1 out of 200. Are there nice ways to illustrate that quantity ?...
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Border bias and weighted kernels
With Ewen (aka @3wen), not only we have been playing on Twitter this month, we have also been working on kernel estimation for densities of spatial processes. Actually, it is only a part of what he was working on, but that part on kernel estimation has been the opportunity to write a short paper, that can now be downloaded on hal. The problem wi...
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Inference and autoregressive processes
Consider a (stationary) autoregressive process, say of order 2, for some white noise with variance . Here is a code to generate such a process, > phi1=.5 > phi2=-.4 > sigma=1.5 > set.seed(1) > n=240 > WN=rnorm(n,sd=sigma) > Z=rep(NA,n) > Z[1:2]=rnorm(2,0,1) > for(t in 3:n){Z[t]=phi1*Z[t-1]+phi2*Z[t-2]+WN[t]} Here, we have to estimate two sets ...
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