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ExperienceAnalysis.jl
EconomicScenarioGenerators.jl
@@ -689,20 +689,131 @@
-
+
https://JuliaActuary.org/blog.html
- 2024-06-17T03:29:44.486Z
+ 2024-06-18T03:39:01.368Z
https://JuliaActuary.org/packages.html
- 2024-06-17T02:46:38.178Z
+ 2024-06-18T03:39:01.368Z
https://JuliaActuary.org/index.html
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+ 2024-06-18T03:39:01.368Z
https://JuliaActuary.org/posts/mortality-comparison/index.html
- 2024-06-16T21:18:09.490Z
+ 2024-06-18T03:39:01.377Z
https://JuliaActuary.org/posts/exposures-example/index.html
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https://JuliaActuary.org/posts/stochastic-mortality/index.html
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https://JuliaActuary.org/posts/academy-generator-rates/index.html
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https://JuliaActuary.org/posts/hacktoberfest/index.html
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https://JuliaActuary.org/posts/bayes-vs-limited-fluctuation/index.html
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https://JuliaActuary.org/posts/mortalitytables-and-dataframes/index.html
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https://JuliaActuary.org/posts/nested-policy-projections/index.html
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https://JuliaActuary.org/posts/bayesian-claims-demo/index.html
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https://JuliaActuary.org/posts/treasury-interactive/index.html
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https://JuliaActuary.org/posts/cashflow-interactive/index.html
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https://JuliaActuary.org/community.html
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https://JuliaActuary.org/posts/finance-models-announcement/index.html
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https://JuliaActuary.org/posts/policy-diffeq/index.html
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+ 2024-06-18T03:39:01.378Z
https://JuliaActuary.org/posts/life-modeling-problem/index.html
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https://JuliaActuary.org/posts/bayesian-intro/index.html
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https://JuliaActuary.org/posts/julia-for-actuaries/index.html
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https://JuliaActuary.org/posts/poisson-approximation/index.html
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https://JuliaActuary.org/posts/fitting-yield-curves/index.html
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https://JuliaActuary.org/posts/fitting-survival-data/index.html
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https://JuliaActuary.org/posts/getting-started-for-actuaries/index.html
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https://JuliaActuary.org/posts/coding-the-future/index.html
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+ 2024-06-18T03:39:01.374Z
https://JuliaActuary.org/posts/academy-generator/index.html
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+ 2024-06-18T03:39:01.371Z
https://JuliaActuary.org/benchmarks.html
- 2024-06-15T13:32:36.848Z
+ 2024-06-18T03:39:01.368Z
https://JuliaActuary.org/all-posts.html
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https://JuliaActuary.org/learn.html
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diff --git a/styles.css b/styles.css
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--- a/styles.css
+++ b/styles.css
@@ -122,6 +122,12 @@ pre {
font-weight: 400;
}
+@media (max-width: 800px) {
+ .hero-grid {
+ flex-direction: column;
+ }
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flex: 1;
}
On this page
ExperienceAnalysis.j
Meeting your exposure calculation needs.
QuickStart
-using ExperienceAnalysis
-using Dates
+Quickstart
+df = DataFrame(
+ policy_id = 1:3,
+ issue_date = [Date(2020,5,10), Date(2020,4,5), Date(2019, 3, 10)],
+ end_date = [Date(2022, 6, 10), Date(2022, 8, 10), Date(2022,12,31)],
+ status = ["claim", "lapse", "inforce"]
+)
+
+df.policy_year = exposure.(
+ ExperienceAnalysis.Anniversary(Year(1)),
+ df.issue_date,
+ df.end_date,
+ df.status .== "claim"; # continued exposure
+ study_start = Date(2020, 1, 1),
+ study_end = Date(2022, 12, 31)
+)
+
+df = flatten(df, :policy_year)
-issue = Date(2016, 7, 4)
-termination = Date(2020, 1, 17)
-basis = ExperienceAnalysis.Anniversary(Year(1))
-exposure(basis, issue, termination)
-This will return an array of tuples with a from and to date:
-4-element Array{NamedTuple{(:from, :to),Tuple{Date,Date}},1}:
- (from = Date("2016-07-04"), to = Date("2017-07-04"))
- (from = Date("2017-07-04"), to = Date("2018-07-04"))
- (from = Date("2018-07-04"), to = Date("2019-07-04"))
- (from = Date("2019-07-04"), to = Date("2020-01-17"))
+df.exposure_fraction =
+ map(e -> yearfrac(e.from, e.to + Day(1), DayCounts.Thirty360()), df.policy_year)
+# + Day(1) above because DayCounts has Date(2020, 1, 1) to Date(2021, 1, 1) as an exposure of 1.0
+# here we end the interval at Date(2020, 12, 31), so we need to add a day to get the correct exposure fraction.policy_idInt64 |
+issue_dateDate |
+end_dateDate |
+statusString |
+policy_year@NamedTuple{from::Date, to::Date, policy\_timestep::Int64} |
+exposure_fractionFloat64 |
+
|---|---|---|---|---|---|
| 1 | +2020-05-10 | +2022-06-10 | +claim | +(from = Date(“2020-05-10”), to = Date(“2021-05-09”), policy_timestep = 1) | +1.0 | +
| 1 | +2020-05-10 | +2022-06-10 | +claim | +(from = Date(“2021-05-10”), to = Date(“2022-05-09”), policy_timestep = 2) | +1.0 | +
| 1 | +2020-05-10 | +2022-06-10 | +claim | +(from = Date(“2022-05-10”), to = Date(“2023-05-09”), policy_timestep = 3) | +1.0 | +
| 2 | +2020-04-05 | +2022-08-10 | +lapse | +(from = Date(“2020-04-05”), to = Date(“2021-04-04”), policy_timestep = 1) | +1.0 | +
| 2 | +2020-04-05 | +2022-08-10 | +lapse | +(from = Date(“2021-04-05”), to = Date(“2022-04-04”), policy_timestep = 2) | +1.0 | +
| 2 | +2020-04-05 | +2022-08-10 | +lapse | +(from = Date(“2022-04-05”), to = Date(“2022-08-10”), policy_timestep = 3) | +0.35 | +
| 3 | +2019-03-10 | +2022-12-31 | +inforce | +(from = Date(“2020-01-01”), to = Date(“2020-03-09”), policy_timestep = 1) | +0.191667 | +
| 3 | +2019-03-10 | +2022-12-31 | +inforce | +(from = Date(“2020-03-10”), to = Date(“2021-03-09”), policy_timestep = 2) | +1.0 | +
| 3 | +2019-03-10 | +2022-12-31 | +inforce | +(from = Date(“2021-03-10”), to = Date(“2022-03-09”), policy_timestep = 3) | +1.0 | +
| 3 | +2019-03-10 | +2022-12-31 | +inforce | +(from = Date(“2022-03-10”), to = Date(“2022-12-31”), policy_timestep = 4) | +0.808333 | +