The savings Library

Overview

The savings library is for modeling savings products, such as universal life, unit-linked, variable life and annuities. These saving products have investment features. Most part of premiums paid by the policyholder of such products is kept as the account value of the policy.

The models in this library include the account value logic, and differ from the basiclife models in the following points.

• In the basiclife models, premiums are calculated so that the present value of premiums cover the present value of claims. In the savings models, premiums are given as input, whether they are a single premium or recurring premiums.

• In the savings models, most part of the premium received from a policyholder is transferred to the account value. When the policy holder exits, whether by death, lapse or maturity, the account value of the policyholder is used to fund the claim paid to the policyholder.

• During each time step, the account value increases by the amount transferred from premiums, positive investment returns or interest credited, and decrease by the transferred amounts of claims, negative investment returns, and deducted fees. The beginning balance of the account value plus the changes in account value should reconcile to the ending balance of the account value.

• The death benefit amount is by default set equal to the premium amount, or the accumulated premium amount for recurring premium types of products. This spec should be customized by the user. When the account value increases above the premium amount, the account value is paid as the death benefit. By default, cost of insurance charges are deducted from the account value.

• The net cashflows of the saving models can be presented in two ways, gross of the account value and net of the account value. The gross presentation shows investment return on account value and change in account value, in addition to premiums, claims, expenses. The net presentation better depict the sources of profit and looses, and can be expressed as the sum of mortality margin adn expense margin.

The library currently includes 2 models.

• The CashValue_SE Model
• The CashValue_ME Model

CashValue_SE and CashValue_ME produce the exact same results but in different ways.

The CashValue_SE model defines and executes formulas for each model point separately, while the CashValue_ME model executes each formula at each time step for all model points at once. They produce the same results for the same model point. CashValue_SE is straight forward, and its formulas are easier to understand, but it runs slower. It’s suitable for validation purposes. CashValue_ME runs fast, but its formulas are expressed as vector operations and can be more complex in some places.

CashValue_SE and CashValue_ME project the cashflows of in-force policies at time 0 as well as new business policies at 0 or any future time. CashValue_ME is to CashValue_SE as BasicTerm_M is BasicTerm_S. Formulas in CashValue_ME apply to all the model point at a time while CashValue_SE carries out projection for each model point separately.

How to Use the Library

As explained in the Copying a Library section, Create you own copy of the savings library. For example, to copy as a folder named savings under the path C:\path\to\your\, type below in an IPython console:

>>> import lifelib

>>> lifelib.create("savings", r"C:\path\to\your\savings")

Examples

• 1. Simple Stochastic Example
• 2. Extended Stochastic Example
• 3. Memory-Optimized Mutiprocess Example
• 4. Profiling and Optimizing the Model

Library Contents

File or Folder	Description
CashValue_SE	The CashValue_SE model.
CashValue_ME	The CashValue_ME model.
cash_value_sample.xlsx	An Excel file that reproduces the results of a selected model point. The file also shows the derivation of the sample mortality rates.
CashValue_ME_EX1	The example model for savings_example1.ipynb
CashValue_ME_EX2	The example model for savings_example2.ipynb
CashValue_ME_EX4	The example model for savings_example4.ipynb
savings_example1.ipynb	Jupyter notebook 1. Simple Stochastic Example
savings_example2.ipynb	Jupyter notebook 2. Extended Stochastic Example
savings_example3.ipynb	Jupyter notebook 3. Memory-Optimized Mutiprocess Example
savings_example4.ipynb	Jupyter notebook 4. Profiling and Optimizing the Model
generate_100K_model_points.ipynb	Jupyter notebook used for generating the large model point data for 3. Memory-Optimized Mutiprocess Example.
plot_ex1_av_paths.py	Python script for Account value paths
plot_ex1_rand.py	Python script for Account value distribution
plot_ex1_option_value.py	Python script for Monte Carlo vs Black-Scholes-Merton
plot_ex2_comp_option_values.py	Python script for Black-Scholes-Merton on dividend paying stock
plot_ex2_lapse_decrement.py	Python script for Dynamic policy decrement
plot_ex2_av_to_pols.py	Python script for Account value to number of policies

Other Notebooks

• Generating 100K model points