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-rw-r--r--python/notebooks/Tranche calculator.ipynb67
1 files changed, 44 insertions, 23 deletions
diff --git a/python/notebooks/Tranche calculator.ipynb b/python/notebooks/Tranche calculator.ipynb
index fb30d134..13b0b194 100644
--- a/python/notebooks/Tranche calculator.ipynb
+++ b/python/notebooks/Tranche calculator.ipynb
@@ -9,8 +9,9 @@
"import pandas as pd\n",
"import numpy as np\n",
"\n",
- "from analytics import TrancheBasket, ManualTrancheBasket\n",
- "from datetime import date"
+ "from serenitas.analytics.tranche_basket import TrancheBasket, ManualTrancheBasket, DualCorrTranche\n",
+ "from datetime import date\n",
+ "from copy import deepcopy"
]
},
{
@@ -19,13 +20,23 @@
"metadata": {},
"outputs": [],
"source": [
+ "#Build previous series skew to price this new series\n",
"index_type = 'HY'\n",
- "series = 35\n",
+ "new_series = 39\n",
"value_date = date.today()\n",
- "new_index = ManualTrancheBasket(index_type, series, \"5yr\", value_date=value_date, ref=104.625, quotes=[43, 92.5, 110, 120.27])\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 "
+ "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"
]
},
{
@@ -34,7 +45,24 @@
"metadata": {},
"outputs": [],
"source": [
- "new_index.implied_ss()"
+ "#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})"
]
},
{
@@ -43,26 +71,19 @@
"metadata": {},
"outputs": [],
"source": [
- "#Build previous series skew to price this new series\n",
- "base_index = TrancheBasket(index_type, series-4, \"5yr\")\n",
- "base_index.tweak()\n",
- "base_index.build_skew()\n",
- "\n",
- "new_index.rho = base_index.map_skew(new_index)\n",
- "new_index"
+ "#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"
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -76,7 +97,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.5"
+ "version": "3.10.9"
}
},
"nbformat": 4,