diff options
| -rw-r--r-- | python/analytics/option.py | 4 | ||||
| -rw-r--r-- | python/notebooks/swaption_risk.ipynb | 60 |
2 files changed, 62 insertions, 2 deletions
diff --git a/python/analytics/option.py b/python/analytics/option.py index f213f47e..ac1fcffd 100644 --- a/python/analytics/option.py +++ b/python/analytics/option.py @@ -413,7 +413,9 @@ class BlackSwaption(ForwardIndex): r = [] actual_params = [p for p in params if hasattr(self, p)] if isinstance(vol_surface, dict): - vol_surface = vol_surface[(self.index.index_type, self.index.series)] + vol_surface = vol_surface[ + (self.index.index_type, self.index.series, self.option_type) + ] for ss in spread_shock: self.index.spread = orig_spread * (1 + ss) # TODO: Vol floored at 20% for now. diff --git a/python/notebooks/swaption_risk.ipynb b/python/notebooks/swaption_risk.ipynb index f3a52bdf..8c38ecf8 100644 --- a/python/notebooks/swaption_risk.ipynb +++ b/python/notebooks/swaption_risk.ipynb @@ -84,6 +84,64 @@ "execution_count": null, "metadata": {}, "outputs": [], + "source": [ + "from analytics.scenarios import run_portfolio_scenarios\n", + "from analytics import BlackSwaptionVolSurface, CreditIndex\n", + "import analytics\n", + "import datetime\n", + "import numpy as np\n", + "\n", + "today = datetime.datetime.now()\n", + "yesterday = datetime.date.today() - pd.offsets.BDay()\n", + "\n", + "portf = get_swaption_portfolio(yesterday, conn, source_list=['GS'])\n", + "for i, amt in hedges.iteritems():\n", + " portf.add_trade(CreditIndex(i[:2], i[2:4], '5yr', value_date=yesterday, notional=amt), ('delta', i))\n", + "\n", + "vol_surface = {}\n", + "for trade in portf.swaptions:\n", + " vs = BlackSwaptionVolSurface(trade.index.index_type, trade.index.series, \n", + " value_date=today.date(), interp_method = \"bivariate_linear\")\n", + " vol_surface[(trade.index.index_type, trade.index.series, trade.option_type)] = vs[vs.list(source='GS', option_type=trade.option_type)[-1]]\n", + "\n", + "#Set original_pv as of yesterday's EOD levels, don't reset PV after this time\n", + "portf.mark(interp_method=\"bivariate_linear\", source_list=['GS'])\n", + "portf.reset_pv()\n", + "\n", + "#set ref to today's levels\n", + "portf.value_date = today\n", + "portf.mark(interp_method=\"bivariate_linear\", source_list=['GS'])\n", + "\n", + "spread_shock = np.round(np.arange(-.1, .1, .01), 4)\n", + "scens = run_portfolio_scenarios(portf, [today], params=['pnl', 'hy_equiv', 'sigma'],\n", + " spread_shock=spread_shock,\n", + " vol_shock=[0],\n", + " corr_shock=[0],\n", + " vol_surface=vol_surface)\n", + "pnl = scens.xs('pnl', level = 2, axis=1).sum(axis=1)\n", + "hy_equiv = scens.xs('hy_equiv', level = 2, axis=1).sum(axis=1)\n", + "\n", + "ig = CreditIndex('IG', 32, '5yr', value_date = today)\n", + "ig.mark()\n", + "\n", + "pnl.index = pnl.index.set_levels((1+pnl.index.get_level_values('spread_shock')) * ig.spread, level = 'spread_shock')\n", + "hy_equiv.index = pnl.index" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pnl, hy_equiv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [] } ], @@ -107,5 +165,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } |