Premium financing for life insurance policies

Clone this repository

git clone https://github.com/xujiahuayz/premfin.git

Navigate to the directory of the cloned repo

cd premfin

Set up the repo

Give execute permission to your script and then run setup_repo.sh

chmod +x setup_repo.sh
./setup_repo.sh

or follow the step-by-step instructions below

Create a python virtual environment

• MacOS

python3 -m venv venv

• Windows

python -m venv venv

Activate the virtual environment

• MacOS

. venv/bin/activate

• Windows (in Command Prompt, NOT Powershell)

venv\Scripts\activate.bat

Install the project in editable mode

pip install -e ".[dev]"

Git Large File Storage (Git LFS)

All files in data/ are stored with lfs.

To initialize Git LFS:

git lfs install

git lfs track data/**/*

To pull data files, use

git lfs pull

Run scripts

cd scripts

plot betas

python plot_betas.py

create a clean mortality_experience_clean.xlsx:

python process_empirical_table.py

calculate standard LE (with mortality rate of 1) of each cohort

python get_le.py

get surrender value, max loan rate acceptable by policyholder, lender profit at max loan rate in one go:

python get_surrendervalue_maxloanrate_lenderprofit.py

get different untapped profit based on different VBT tables and mortality rates from the perspective of policyholder when their cost of capital is at various levels:

python get_untappedprofit_policyholder.py

plot life insurance value to policyholders by LE bin

python plot_le_distr.py

1. plot money left based on different VBT on the table from the perspective of policyholder in comparison with real estate value loss during the financial crisis:
2. plot distribution of life insurance value to policyholders on gender and age:
3. plot average value to policy holders of different face value amount:

python plot_moneyleft.py

1. get median value loss from common household mistakes
2. And its distribution on age and gender:

python get_median.py

plot value to policy holders of different net worth band:

python plot_wealth_distr.py

plot health spending as a fraction of income by age

python fetch_fred.py
python plot_income_by_age.py

1. plot economic value to policy holders of different net worth based on Face Amount Band:
2. plot economic value to policy holders of different net worth based on Face Amount Band, Attained age and Gender:

python plot_ecovalue.py

1. plot lapsed life inusrance economic value and Food stamp, Medicare and Medicaid.
2. Break down the life insurance economic value and see its distribution on gender and age

python plot_wealthtransferprogram.py

Synchronize with the repo

Always pull latest code first

git pull

Make changes locally, save. And then add, commit and push

git add [file-to-add]
git commit -m "update message"
git push

Best practice

Coding Style

We follow PEP8 coding format. The most important rules above all:

1. Keep code lines length below 80 characters. Maximum 120. Long code lines are NOT readable.
2. We use snake_case to name function, variables. CamelCase for classes.
3. We make our code as DRY (Don't repeat yourself) as possible.
4. We give a description to classes, methods and functions.
5. Variables should be self explaining and just right long:
   • implied_volatility is preferred over impl_v
   • implied_volatility is preferred over implied_volatility_from_broker_name

Do not

1. Do not place .py files at root level (besides setup.py)!
2. Do not upload big files > 100 MB.
3. Do not upload log files.
4. Do not declare constant variables in the MIDDLE of a function