The Data Space

Input data shared by every by-policy projection.

The five 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/fx_whole_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 FXWholeLife_JP_S 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
charge_table_file	charge_table()	charge_table.csv
fx_path_file	fx_path_table()	fx_path_table.csv

The mortality table is a construction, not a copy

mort_table.csv is a [std] construction. 生保標準生命表2018（死亡保険用）is published free at a stable public URL by 日本アクチュアリー会 and anyone can go and read it, but that publisher’s site terms prohibit reproduction and transmission to third parties, so this library must not ship a copy of it. What it ships instead is the one canonical jplib proxy: a single table built once, for the whole library, by log-linear interpolation in ln q between the individual rates the library’s nine products quote from the published table, rounded to that table’s own five decimal places. Every row’s provenance column says which of the two it is — an ANCHOR row read from the IAJ table and quoted under attribution, or an INTERPOLATED [std] value — so the same attained age carries the same rate and the same provenance in every product that ships it. This file is that canonical table restricted to the attained ages this product’s model points can reach: ages 18 to the terminal age of each sex. The rates are identical to the other products’ at every shared cell and must not diverge. It is not the published table, and no conclusion about Japanese insured-lives mortality should be drawn from it.

The table is also a valuation table carrying an explicit margin sized to about 2σ, not best-estimate experience, so a best-estimate basis is a [std] adjustment of it either way. That adjustment lives on Projection as mort_be_factor and moves the decrement only; the cost-of-insurance charge reads the table unadjusted, because the charge basis is a pricing element the insurer sets and the decrement is an experience assumption. To swap in a licensed or 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 other four tables

model_point_table.csv is indexed by point_id; point 1 is the technical notes’ worked-example anchor cell. lapse_table.csv is indexed by shape and policy year, because the two shapes are two behavioural regimes an order of magnitude apart — a four-year cumulative exit of 60.90% on the single-premium shape against roughly 25% on the level-premium one. charge_table.csv holds the shape’s whole parameter set: the back-solved charge stack, the 解約控除 scale, the 低解約返戻金割合 ramp, the three constants of the 市場価格調整 reconstruction and the two 特別積立金 rates, each row tagged in its provenance column with the source it came from or with the fit that produced it. fx_path_table.csv is an optional monthly-anniversary FX path by policy year, read only where a model point sets fx_path; the base run holds the rate flat at the model point’s own fx_ttm.

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 technical notes’ worked-example anchor cell: male, 契約年齢 40, LEVEL shape, 基本保険金額 US$100,000, 60歳払込満了, 月払保険料 US$239.60, 積立利率 at the guaranteed floor of 3.00%.

mort_table()

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

The canonical jplib [std] proxy for 生保標準生命表2018（死亡保険用）, anchored to the individual rates the library quotes from it and never a copy of that table; see the Space docstring. Includes 高度障害, which the published table’s death rate already carries, so 高度障害 is not a second decrement anywhere in this model.

lapse_table()

The annual surrender rates by shape and policy year, from lapse_table.csv.

Two curves, because the two shapes are two behavioural regimes: the SINGLE curve is calibrated to a published four-year exit statistic, the LEVEL curve has no public anchor at all and is a [std] judgement. Policy years beyond the last row take that row.

charge_table()

The shape’s charge, surrender and MVA parameters, from charge_table.csv.

Indexed by shape and item name. Holds the back-solved 契約初期費用 and 維持費率, the 解約控除 scale, the 低解約返戻金割合 ramp, the three constants of the 市場価格調整 reconstruction and the two 特別積立金 rates, each with its own provenance.

fx_path_table()

The optional TTM path by policy year, from fx_path_table.csv.

Read only where a model point sets fx_path; the base run holds the reference TTM flat at the model point’s own fx_ttm, because this library models contractual cash flows and not an FX view. Policy years beyond the last row take that row.