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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import portfolio_var as port\n",
"from analytics import Swaption, BlackSwaption, Index, VolatilitySurface, Portfolio\n",
"from analytics.scenarios import run_swaption_scenarios, run_index_scenarios, run_portfolio_scenarios\n",
"import datetime\n",
"import pandas as pd\n",
"from pandas.tseries.offsets import BDay, BMonthEnd\n",
"\n",
"#import exploration.swaption_calendar_spread as spread\n",
"import exploration.swaption_calendar_spread as spread"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Oct ME Bond HY Equiv\n",
"report_date = (datetime.date.today() + BMonthEnd(-1)).date()\n",
"bond_HY_equiv = -.12088\n",
"percentile = .95"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#The 95%tile \n",
"df, spread, dur = port.rel_spread_diff(report_date)\n",
"stress = pd.DataFrame()\n",
"stress.at[('2SD_widen', 'spread')] = df.quantile(.975) \n",
"stress.at[('2SD_tighten', 'spread')] = df.quantile(.025) \n",
"stress.at[('worst_widen', 'spread')] = df.max()\n",
"stress['pts'] = -stress * spread * dur/100\n",
"stress['nav_impact'] = bond_HY_equiv * stress['pts']"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
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"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>spread</th>\n",
" <th>pts</th>\n",
" <th>nav_impact</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2SD_widen</th>\n",
" <td>0.157884</td>\n",
" <td>-2.208036</td>\n",
" <td>0.266907</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2SD_tighten</th>\n",
" <td>-0.163480</td>\n",
" <td>2.286301</td>\n",
" <td>-0.276368</td>\n",
" </tr>\n",
" <tr>\n",
" <th>worst_widen</th>\n",
" <td>0.359386</td>\n",
" <td>-5.026082</td>\n",
" <td>0.607553</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" spread pts nav_impact\n",
"2SD_widen 0.157884 -2.208036 0.266907\n",
"2SD_tighten -0.163480 2.286301 -0.276368\n",
"worst_widen 0.359386 -5.026082 0.607553"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stress"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" .dataframe thead tr:only-child th {\n",
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" text-align: left;\n",
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"\n",
" .dataframe tbody tr th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>level_1</th>\n",
" <th>notional</th>\n",
" <th>factor</th>\n",
" <th>coupon</th>\n",
" <th>duration</th>\n",
" <th>theta</th>\n",
" <th>price</th>\n",
" <th>closespread</th>\n",
" <th>clean_nav</th>\n",
" <th>accrued</th>\n",
" <th>onTR_notional</th>\n",
" <th>widen</th>\n",
" <th>tighten</th>\n",
" <th>total</th>\n",
" </tr>\n",
" <tr>\n",
" <th>strategy</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>HEDGE_MBS</th>\n",
" <td>3</td>\n",
" <td>-30500000.0</td>\n",
" <td>2.92</td>\n",
" <td>0.15</td>\n",
" <td>9.579586</td>\n",
" <td>0.091530</td>\n",
" <td>323.911577</td>\n",
" <td>728.775311</td>\n",
" <td>-2.454496e+06</td>\n",
" <td>-173891.666667</td>\n",
" <td>-2.271120e+07</td>\n",
" <td>-392951.746260</td>\n",
" <td>439165.979817</td>\n",
" <td>439165.979817</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SER_IGCURVE</th>\n",
" <td>1</td>\n",
" <td>-11000000.0</td>\n",
" <td>2.00</td>\n",
" <td>0.02</td>\n",
" <td>4.202955</td>\n",
" <td>0.005495</td>\n",
" <td>203.166063</td>\n",
" <td>38.112262</td>\n",
" <td>-2.220374e+04</td>\n",
" <td>-12833.333333</td>\n",
" <td>1.006705e+06</td>\n",
" <td>3787.727425</td>\n",
" <td>-3681.218467</td>\n",
" <td>3787.727425</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SER_ITRXCURVE</th>\n",
" <td>1</td>\n",
" <td>39300000.0</td>\n",
" <td>2.00</td>\n",
" <td>0.02</td>\n",
" <td>14.638235</td>\n",
" <td>0.021951</td>\n",
" <td>203.615045</td>\n",
" <td>138.590158</td>\n",
" <td>2.027082e+06</td>\n",
" <td>53396.910000</td>\n",
" <td>-3.481913e+06</td>\n",
" <td>-15658.882722</td>\n",
" <td>16533.674519</td>\n",
" <td>16533.674519</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" level_1 notional factor coupon duration theta \\\n",
"strategy \n",
"HEDGE_MBS 3 -30500000.0 2.92 0.15 9.579586 0.091530 \n",
"SER_IGCURVE 1 -11000000.0 2.00 0.02 4.202955 0.005495 \n",
"SER_ITRXCURVE 1 39300000.0 2.00 0.02 14.638235 0.021951 \n",
"\n",
" price closespread clean_nav accrued \\\n",
"strategy \n",
"HEDGE_MBS 323.911577 728.775311 -2.454496e+06 -173891.666667 \n",
"SER_IGCURVE 203.166063 38.112262 -2.220374e+04 -12833.333333 \n",
"SER_ITRXCURVE 203.615045 138.590158 2.027082e+06 53396.910000 \n",
"\n",
" onTR_notional widen tighten total \n",
"strategy \n",
"HEDGE_MBS -2.271120e+07 -392951.746260 439165.979817 439165.979817 \n",
"SER_IGCURVE 1.006705e+06 3787.727425 -3681.218467 3787.727425 \n",
"SER_ITRXCURVE -3.481913e+06 -15658.882722 16533.674519 16533.674519 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"port.cleared_cds_margins(report_date, percentile)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"120816.55576340854"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"port.index_curve_margins(report_date)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
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|
