🐍 Light Economic Generator 🐍

The purpose of this repository is to create an open-source stochastic economic scenario generator using algorithms previously published by OSEM. As a prototype (NOT TESTED!!!!), LEG supports 3 models:

• Black Sholes
• Vasicek
• Hull-White

The stochastic scenarios are available in two modelities. As an index (I) or as a discounting factor (D).

The example of the input:

Calibration_ID	model	Type	NoOfPaths	NoOfSteps	T	a	sigma	epsilon	Country	selected_param_file	selected_curves_file	mu	gamma
11	HW	I	1000	600	50	0.02	0.02	0.01	Slovenia	Param_no_VA.csv	Curves_no_VA.csv	0	0
22	BS	D	1000	600	50	0	0.02	0.01	Slovenia	Param_no_VA.csv	Curves_no_VA.csv	0.02	0
33	V	D	1000	600	50	0	0.02	0.01	Slovenia	Param_no_VA.csv	Curves_no_VA.csv	0.02	0.3

This prototype generates a csv that contains 1000 stochastic scenarios for each of the 3 rows and append it one bellow the other.

The script that starts the prototype is (also in main.py):

import pandas as pd
from read_input import read_model_input
from term_structure import calculate_zero_coupon_price
from black_sholes import set_up_black_sholes
from vasicek import set_up_vasicek
from hull_white import set_up_hull_white

param_raw = pd.read_csv("Parameters.csv", sep=',', index_col=0)

combined_run = []

for run_id in param_raw.index:
    [modeling_parameters, curve_parameters] = read_model_input(run_id)
    
    zero_coupon_price = lambda t: calculate_zero_coupon_price(t, curve_parameters["target_maturities"], curve_parameters["calibration_vector"], curve_parameters["ultimate_forward_rate"], curve_parameters["convergence_speed"] )
    if modeling_parameters["run_type"] == "HW":
        run = set_up_hull_white(run_id, modeling_parameters, zero_coupon_price)        
    elif modeling_parameters["run_type"] == "BS":
        run = set_up_black_sholes(run_id, modeling_parameters, zero_coupon_price)
    elif modeling_parameters["run_type"] == "V":
        run = set_up_vasicek(run_id, modeling_parameters, zero_coupon_price)
    else:
        raise ValueError("Model type not available")

    if isinstance(combined_run,pd.DataFrame):
        combined_run = pd.concat([combined_run,run])
    else:
        combined_run = run

combined_run.to_csv("Output/run.csv")