The Data Space

Input data shared by every by-policy projection.

The four input CSVs are read here, once per model, and referenced from Projection as data. Projection is parameterized by point_id, so each Projection[N] is a separate ItemSpace with its own cells cache; if the readers lived there, every model point would re-read every file. Holding them in an unparameterized Space reads each file once no matter how many policies are projected.

Inputs are external files: plain CSVs in the model folder’s parent directory, products/term_life/, rather than data stored inside the model. The model folder therefore holds nothing but formulas — no _data/, no IOSpec, no embedded values — so a diff of the model shows logic changes only. This follows annuallife.TradLife_A; contrast basiclife.BasicTerm_S, which keeps its inputs inside the model through modelx’s IOSpec machinery.

The consequence worth knowing: the model is not portable on its own. Copying the Term_JP_A folder without its parent’s CSVs produces a model that reads and then fails on first evaluation.

input_dir() resolves the directory from _model.path.parent at run time, so the model works wherever the repository is checked out. Each table has a filename Reference and a reader Cells:

Reference	Cells	File
model_point_file	model_point_table()	model_point_table.csv
mort_table_file	mort_table()	mort_table.csv
lapse_table_file	lapse_table()	lapse_table.csv
prem_rate_file	prem_rate_table()	prem_rate_table.csv

prem_anchor_table() is derived from prem_rate_table() rather than read from a fifth file, so it costs no extra read.

The mortality table is a proxy, and deliberately so

生保標準生命表2018（死亡保険用）(seiho hyōjun seimeihyō 2018, the standard mortality table for death benefits) is published by 日本アクチュアリー会 (the Institute of Actuaries of Japan) free and in full at a stable public URL [REG-R18][R3][R4] — anyone can retrieve it and check a rate, which is the sharpest contrast in this repository with the UK term model, whose CMI tables cannot be read at all without a subscription. But the publisher’s site terms prohibit reproduction, alteration and transmission to third parties without written consent [REG-R21], so this library must not ship a copy of it.

mort_table.csv is therefore a [std] construction, not the table. It is the canonical jplib proxy — one construction shared by every product in this library rather than a per-product reconstruction, so that a cell carries the same rate and the same provenance wherever it is shipped. Its anchors are the union of the rates read from the published table across the library’s research passes [REG-R18], which is why more ages are anchored than this product’s own pass read [R4]; among them are the rates the technical notes quote — male q30 = 0.00068, q35 = 0.00077, q40 = 0.00118, q50 = 0.00285, q60 = 0.00653, q65 = 0.01015, female q30 = 0.00037 and q60 = 0.00363. Every other age is log-linear in ln q between its two neighbouring anchors, rounded to the five decimals of the published table’s own granularity, with no extrapolation anywhere. Every row says which of the two it is in its provenance column. The rows shipped here run from attained age 20 to attained age 80, the range this product’s model points can reach. The anchoring is what makes the model reproduce the notes’ worked-example rates exactly; the interpolated rows are a documented proxy and no conclusion about Japanese mortality should be drawn from them.

Two further distinctions the file does not blur. The shipped rates are table rates: Projection applies its own mort_be_factor to reach a best-estimate basis, because 標準生命表2018 is a valuation table carrying an explicit risk-theory margin sized near 2σ and capped at 130% of the unadjusted rate [REG-R20]. And the table includes 高度障害 inside its death rate [REG-R20], which is why the projection has one decrement and not two.

To swap in a company basis, replace mort_table.csv with a same-schema file, or point mort_table_file at a different name, and clear the cache. No formula changes.

The premium table is mostly sourced

prem_rate_table.csv is the one assumption file whose values are largely not standardizations. Japanese insurers publish rate cards, so the marginal rate per ¥5,000,000 of cover and the ¥248 flat monthly element decompose exactly out of published premiums [S2]. Four cells are published — male ages 30, 40 and 50 and female age 30, all at a ten-year term — and each carries the arithmetic of its decomposition in its provenance column. Ages 60 and 70 are published by no carrier and the anchor cell reaches both, so Projection extends the scale off the is_anchor row of the matching sex. The ¥248 is a premium component, not an expense recovery; crediting it against maintenance expense counts it twice.

Cells Descriptions

input_dir()

The directory holding the input CSVs: the model folder’s parent.

Inputs are external files, not data stored inside the model, so the model folder is pure formulas. The path is resolved at run time from where the model was read, following annuallife.TradLife_A.

model_point_table()

The model point table, read from model_point_table.csv.

Indexed by point_id; point_id = 1 is the anchor cell of the technical notes’ worked example.

mort_table()

The table mortality rates by sex and age, from mort_table.csv.

A [std] proxy for 生保標準生命表2018（死亡保険用）, anchored on the rates the technical notes quote and log-linearly interpolated between them; see the Space docstring for why the published table itself is cited rather than shipped. The rates include 高度障害 [REG-R20] and carry the table’s own valuation margin, which Projection.mort_be_factor removes.

lapse_table()

The ordinary lapse rates by policy year, read from lapse_table.csv.

[std] throughout: Japan’s only published industry-wide persistency figure is the LIAJ’s whole-market 解約・失効率 [REG-R31], which is a level and not a duration curve. Policy years beyond the last row take that row.

prem_rate_table()

The published premium rate cells, read from prem_rate_table.csv.

Indexed by (sex, issue_age, term_y). rate_per_5m is the marginal monthly rate per ¥5,000,000 of cover and policy_fee_m the flat monthly element, both decomposed out of published rate cards [S2]. is_anchor marks the one row per sex from which Projection extends the scale to unpublished ages.

prem_anchor_table()

The one is_anchor row per sex of prem_rate_table(), indexed by sex.

Derived from the table already in memory rather than read from a file of its own, so it costs no extra read. The male anchor is the published age-50 ten-year cell, the highest male cell any carrier publishes; the female anchor is the age-30 ten-year cell, the only published female cell in the source set [S2].