VIX_Real_Vol_Comparison.ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import time\n",
    "from bayesiankalman import mcmc\n",
    "from rpy2.robjects.packages import importr\n",
    "from rpy2.robjects import numpy2ri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "%matplotlib inline"
   ]
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   "cell_type": "code",
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   "outputs": [],
   "source": [
    "fracdiff = importr('fracdiff')\n",
    "numpy2ri.activate() "
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "VIX = pd.read_csv('Data/VIX_2011_01-2016_04.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": [
    "VIX.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vix = pd.Series(VIX['vix'].values, index=pd.to_datetime(VIX['Date'],format='%d%b%Y'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "vix.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": false
   },
   "outputs": [],
   "source": [
    "results = mcmc.read_nested_hdf('/home/sangrey/Repositories/LongMemoryModel/results/cond_gp_model.with_mean.hdf')\n",
    "estimates = {pd.to_datetime(key, format='%B_%d_%Y') : item for key, item in results['results'].items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "last_day = np.sort(list(estimates.keys()))[-1]\n",
    "present_dates = np.unique(estimates[last_day]['dates'].astype('datetime64[D]'))\n",
    "ivols = estimates[last_day]['ivols']\n",
    "ivols_df = pd.DataFrame(np.median(estimates[last_day]['ivols'].T, axis=1),index=present_dates)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "d, sd_asymp, sd_reg = fracdiff.fdGPH(np.log(vix.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "sd_reg"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "source": []
  }
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