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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import analytics.tranche_basket as bkt\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from analytics.scenarios import run_tranche_scenarios, run_portfolio_scenarios, run_tranche_scenarios_rolldown\n",
"from analytics import DualCorrTranche, TrancheBasket\n",
"from utils.db import dbconn\n",
"from datetime import date\n",
"\n",
"value_date = (date.today() - pd.offsets.BDay(1)).date()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"index_type = 'HY'\n",
"series = 35\n",
"tenor = '5yr'\n",
"value_date = date.today()\n",
"price = 103.875\n",
"at_det = [0, 15, 25, 35, 100] if index_type == 'HY' else ['0', '3', '7', '15', '100']\n",
"tranche_prices= [41.4, 90.6, 109.6, 119.7]\n",
"\n",
"#Build another skew to price this new series\n",
"base_index = TrancheBasket(\"HY\", 33, \"5yr\")\n",
"base_index.tweak()\n",
"base_index.build_skew()\n",
"skew=base_index.skew"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"results = []\n",
"for i in range(3):\n",
" #set up\n",
" rho_floor = tranche.rho[1] if i > 0 else 0.2\n",
" rho_min = rho_floor\n",
" rho_max = rho_floor + 0.4\n",
" tranche = DualCorrTranche(index_type, series, tenor, attach=at_det[i], detach=at_det[i+1], corr_attach = rho_floor, corr_detach = rho_min + (rho_max -rho_min)/2, tranche_running = 500, value_date=value_date)\n",
" tranche._index.tweak([price])\n",
" #now loop to find it\n",
" for j in range(20):\n",
" if tranche.price <= tranche_prices[i]:\n",
" rho_min = tranche.rho[1]\n",
" else:\n",
" rho_max = tranche.rho[1]\n",
" tranche.rho[1] = rho_min + (rho_max - rho_min)/2\n",
" results.append([tranche.rho[1], tranche.price, tranche.delta, tranche.gamma, tranche.theta(skew=skew), tranche.delta * float(tranche._index.theta())])\n",
"ss_corr = tranche.rho[1]\n",
"tranche = DualCorrTranche(index_type, series, tenor, attach=at_det[i+1], detach=100, corr_attach = ss_corr, corr_detach = .999, tranche_running = 500, value_date=value_date)\n",
"tranche._index.tweak([price])\n",
"results.append([tranche.rho[1], tranche.price, tranche.delta, tranche.gamma, tranche.theta(skew=skew), tranche.delta * float(tranche._index.theta())])\n",
"results = pd.DataFrame(results, columns = ['corr', 'price', 'delta', 'gamma', 'theta', 'delta * index_theta'])\n",
"results['theta_per_delta'] = results['theta'] / results['delta']\n",
"results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Using another skew\n",
"mapped_results = []\n",
"for i in range(3):\n",
" tranche = DualCorrTranche(index_type, series, tenor, attach=at_det[i], detach=at_det[i+1], corr_attach = np.nan, corr_detach = 0.1, tranche_running = 500, value_date=value_date)\n",
" tranche._index.tweak([price])\n",
" tranche.mark(skew=skew)\n",
" mapped_results.append([tranche.rho[1], tranche.price, tranche.delta, tranche.gamma, tranche.theta(skew=skew), tranche.corr01/tranche.notional])\n",
"ss_corr = tranche.rho[1]\n",
"tranche = DualCorrTranche(index_type, series, tenor, attach=at_det[i+1], detach=100, corr_attach = ss_corr, corr_detach = .999, tranche_running = 500, value_date=value_date)\n",
"tranche._index.tweak([price])\n",
"mapped_results.append([tranche.rho[1], tranche.price, tranche.delta, tranche.gamma, tranche.theta(skew=skew), np.nan])\n",
"mapped_results = pd.DataFrame(mapped_results, columns = ['corr', 'price', 'delta', 'gamma', 'theta', 'corr01'])\n",
"mapped_results['theta_per_delta'] = mapped_results['theta'] / mapped_results['delta']\n",
"mapped_results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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"language_info": {
"codemirror_mode": {
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"file_extension": ".py",
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|
