The Data Space#

Input data shared by every by-policy projection.

The eight 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/guaranteed_ul/, 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 ULSG_US_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

coi_rates_file

coi_rates()

coi_rates.csv

corridor_file

corridor_factors()

corridor_factors.csv

mort_table_file

mort_table()

mort_table.csv

class_factor_file

class_factor_table()

class_factor_table.csv

lapse_table_file

lapse_table()

lapse_table.csv

surr_charge_file

surr_charge_table()

surr_charge_table.csv

rop_file

rop_table()

rop_table.csv

coi_rates.csv carries the guaranteed maximum annual rate per $1,000, by attained age; Projection.coi_rate_guar divides it by twelve, which is the notes’ simple-twelfth conversion and one of their named pitfalls. The current scale is that rate times Projection.coi_curr_factor (65%) and the shadow scale times Projection.coi_sg_factor (55%), because no carrier publishes either [std].

Both mortality tables are small illustrative ones [std], not published tables: the notes forbid hard-coding the licensed 2017 CSO and 2015 VBT families, so coi_rates.csv ships a Perks curve fitted to the two figures the notes state – the 8.615 per $1,000 per month guaranteed maximum at attained age 85, and a solved level lifetime no-lapse premium near $10,800 for the anchor cell – and mort_table.csv ships the same curve at 72% of the guaranteed basis. To swap in a licensed basis, replace a file with a same-schema one, or point its filename Reference at a different name, then clear the cache. No formula changes.

Cells Descriptions#

input_dir()[source]#

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()[source]#

The model point table, read from model_point_table.csv.

coi_rates()[source]#

Guaranteed maximum annual COI rates, read from coi_rates.csv.

Per $1,000 of net amount at risk, keyed by sex, rate class and attained age. The monthly rate is the annual rate divided by twelve [std] structure; [R3] requires the guaranteed maxima to be stated in the policy, and [REG-R17] names the 2017 CSO family the notes point at.

corridor_factors()[source]#

The GPT corridor factor table by attained age, read from corridor_factors.csv.

mort_table()[source]#

The best-estimate annual mortality table by attained age, read from mort_table.csv.

class_factor_table()[source]#

The underwriting-class factors, read from class_factor_table.csv.

lapse_table()[source]#

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

surr_charge_table()[source]#

The surrender charge schedules, read from surr_charge_table.csv.

One row per surr_charge_id, giving the initial charge per $1,000 of initial face and the number of years over which it runs off linearly.

rop_table()[source]#

The return-of-premium exercise windows, read from rop_table.csv.

One row per policy anniversary carrying a window: the refund ratio applied to cumulative premiums [S1] and the [std] exercise rate.