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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"from serenitas.analytics.tranche_basket import TrancheBasket, ManualTrancheBasket, DualCorrTranche\n",
"from datetime import date\n",
"from copy import deepcopy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Build previous series skew to price this new series\n",
"index_type = 'HY'\n",
"new_series = 39\n",
"value_date = date.today()\n",
"new_index = ManualTrancheBasket(index_type, new_series, \"5yr\", \n",
" value_date=value_date, \n",
" ref=102.625, \n",
" quotes=[40, 94.5, 108.5, 117.36])\n",
"new_index.tweak()\n",
"new_index.build_skew() \n",
"new_index.implied_ss()\n",
"base_index = TrancheBasket(index_type, new_series-2, \"5yr\")\n",
"base_index.tweak()\n",
"base_index.build_skew()\n",
"\n",
"new_index.rho = base_index.map_skew(new_index)\n",
"new_index"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Tranchelet pricer\n",
"index = 'EU'\n",
"value_date = date.today()\n",
"orig_tranche = DualCorrTranche(index, 32, '5yr', attach=0, detach=3, corr_attach=.45, \n",
" corr_detach=.55, tranche_running=100, \n",
" value_date=value_date, notional=10000000, use_trunc=True)\n",
"orig_tranche.mark()\n",
"tranchelet = DualCorrTranche(index, 32, '5yr', attach=0, detach=1, corr_attach=0, \n",
" corr_detach=.1, tranche_running=100, \n",
" value_date=value_date, notional=10000000, use_trunc=True)\n",
"tranchelet.mark(**{'ref':34.0})\n",
"tranchelet_flat = deepcopy(tranchelet)\n",
"tranchelet_flat.rho[1] = orig_tranche.rho[1]\n",
"print({'extrapolated price': tranchelet.price, \n",
" 'flat price':tranchelet_flat.price, \n",
" 'extrapolated corr':tranchelet.rho[1],\n",
" 'flat corr': tranchelet_flat.rho[1],\n",
" 'corr 01': tranchelet_flat.corr01[1]/tranchelet.notional})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Tranchelet pricer: price the missing piece of tranchlet if extrapolated\n",
"tranchelet_stub = DualCorrTranche(index, 32, '5yr', attach=1, detach=3, \n",
" corr_attach=0,\n",
" corr_detach=.1, tranche_running=100, \n",
" value_date=value_date, notional=10000000, use_trunc=True)\n",
"tranchelet_stub.rho=np.array([tranchelet.rho[1], tranchelet_flat.rho[1]])\n",
"tranchelet_stub"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|
