Vasicek_p_portfolio 2 testing .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0311713f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "81818ae2",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"C:/Users/user/Desktop/thesis_20240516/Vasicek_group1/vasicek_group1_portfolio1.csv\", low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a1e3e87a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Explicitly list the column names to be dropped\n",
    "columns_to_drop = [\n",
    "    'correlation', \n",
    "    'PD_700', 'Estimated_Loss_700',\n",
    "    'PD_750', 'Estimated_Loss_750',\n",
    "    'PD_800', 'Estimated_Loss_800',\n",
    "    'PD_850', 'Estimated_Loss_850',\n",
    "    'PD_900', 'Estimated_Loss_900',\n",
    "    'PD_950', 'Estimated_Loss_950',\n",
    "    'PD_990', 'Estimated_Loss_990',\n",
    "    'PD_999', 'Estimated_Loss_999'\n",
    "]\n",
    "\n",
    "# Drop the columns\n",
    "data = data.drop(columns=columns_to_drop)\n",
    "\n",
    "# Select rows where grade is 'A'\n",
    "df = data[data['grade'] == 'A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "86461baa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weighted Average Loss Rate for entire dataset where actual_class is 1:\n",
      "0.45601883882800387\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_32056\\2780636040.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['loss_rate'] = df['loss'] / df['exposure']\n"
     ]
    }
   ],
   "source": [
    "# Calculate the loss rate for each row\n",
    "df['loss_rate'] = df['loss'] / df['exposure']\n",
    "\n",
    "# Filter the dataset for rows where 'actual_class' is 1\n",
    "subset_actual_class_1 = df[df['actual_class'] == 1]\n",
    "\n",
    "# Calculate the weighted average loss rate for the entire dataset where 'actual_class' is 1\n",
    "if not subset_actual_class_1.empty:  # Check if the subset is not empty\n",
    "    weighted_avg_loss_rate_actual_class_1 = (subset_actual_class_1['loss_rate'] * subset_actual_class_1['exposure']).sum() / subset_actual_class_1['exposure'].sum()\n",
    "    print(\"Weighted Average Loss Rate for entire dataset where actual_class is 1:\")\n",
    "    print(weighted_avg_loss_rate_actual_class_1)\n",
    "else:\n",
    "    print(\"No data for actual_class = 1.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f0c85ab1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import norm\n",
    "\n",
    "\n",
    "correlation = 0.0050115\n",
    "\n",
    "loss_rate = 0.45601883882800387\n",
    "\n",
    "# Define the confidence levels and their corresponding quantiles\n",
    "confidence_levels = [0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.99, 0.999]\n",
    "\n",
    "# Create a new DataFrame for the output\n",
    "df_1 = df.copy()\n",
    "\n",
    "# Calculate PD at different confidence levels using the Vasicek model\n",
    "for i, conf_level in enumerate(confidence_levels):\n",
    "    pd_column_name = f'PD_{int(conf_level * 1000)}'\n",
    "    quantile = norm.ppf(conf_level)\n",
    "    df_1[pd_column_name] = norm.cdf(\n",
    "        (norm.ppf(df_1['uncon_PD']) + quantile * np.sqrt(correlation)) / np.sqrt(1 - correlation)\n",
    "    )\n",
    "    \n",
    "    # Calculate the estimated loss for the confidence level\n",
    "    estimated_loss_column_name = f'Estimated_Loss_{int(conf_level * 1000)}'\n",
    "    df_1[estimated_loss_column_name] = df_1[pd_column_name] * df_1['exposure'] * loss_rate\n",
    "\n",
    "df_12 = df_1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4421655c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   loss  Estimated_Loss_700  Estimated_Loss_750  \\\n",
      "Total Sum  8.683055e+07        1.690491e+08        1.719554e+08   \n",
      "\n",
      "           Estimated_Loss_800  Estimated_Loss_850  Estimated_Loss_900  \\\n",
      "Total Sum        1.752328e+08        1.791081e+08        1.840686e+08   \n",
      "\n",
      "           Estimated_Loss_950  Estimated_Loss_990  Estimated_Loss_999  \n",
      "Total Sum        1.915947e+08        2.062767e+08        2.236139e+08  \n"
     ]
    }
   ],
   "source": [
    "# List of columns to calculate the sum\n",
    "columns_to_sum = [\n",
    "    'loss',\n",
    "    'Estimated_Loss_700',\n",
    "    'Estimated_Loss_750',\n",
    "    'Estimated_Loss_800',\n",
    "    'Estimated_Loss_850',\n",
    "    'Estimated_Loss_900',\n",
    "    'Estimated_Loss_950',\n",
    "    'Estimated_Loss_990',\n",
    "    'Estimated_Loss_999'\n",
    "]\n",
    "\n",
    "# Calculate the sums\n",
    "sums = df_12[columns_to_sum].sum()\n",
    "\n",
    "# Convert the series to a DataFrame for nicer display, and transpose it\n",
    "sum_matrix = pd.DataFrame(sums).transpose()\n",
    "\n",
    "# Rename index to 'Total Sum' for better understanding\n",
    "sum_matrix.index = [\"Total Sum\"]\n",
    "\n",
    "# Display the DataFrame\n",
    "print(sum_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b4cb024a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Ensure column names are stripped of any extra whitespace\n",
    "df_12.columns = df_12.columns.str.strip()\n",
    "\n",
    "# List of columns to plot\n",
    "loss_columns = [\n",
    "    'Estimated_Loss_700', 'Estimated_Loss_750', 'Estimated_Loss_800', 'Estimated_Loss_850',\n",
    "    'Estimated_Loss_900', 'Estimated_Loss_950', 'Estimated_Loss_990', 'Estimated_Loss_999'\n",
    "]\n",
    "\n",
    "# Rename columns for clarity in the plot\n",
    "column_names = {\n",
    "    'Estimated_Loss_700': 'Estimated Loss at 70% Confidence Level',\n",
    "    'Estimated_Loss_750': 'Estimated Loss at 75% Confidence Level',\n",
    "    'Estimated_Loss_800': 'Estimated Loss at 80% Confidence Level',\n",
    "    'Estimated_Loss_850': 'Estimated Loss at 85% Confidence Level',\n",
    "    'Estimated_Loss_900': 'Estimated Loss at 90% Confidence Level',\n",
    "    'Estimated_Loss_950': 'Estimated Loss at 95% Confidence Level',\n",
    "    'Estimated_Loss_990': 'Estimated Loss at 99% Confidence Level',\n",
    "    'Estimated_Loss_999': 'Estimated Loss at 99.9% Confidence Level'\n",
    "}\n",
    "\n",
    "# Plot the distributions\n",
    "plt.figure(figsize=(12, 8))\n",
    "for col in loss_columns:\n",
    "    sns.kdeplot(df_12[col], label=column_names[col], linewidth=2)\n",
    "\n",
    "plt.xlabel('Loss')\n",
    "plt.ylabel('Density')\n",
    "plt.title('Optimized Distribution of Estimated Losses in Portfolio 2')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "46151f95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Estimated_Loss_700': 9,\n",
       " 'Estimated_Loss_750': 9,\n",
       " 'Estimated_Loss_800': 8,\n",
       " 'Estimated_Loss_850': 8,\n",
       " 'Estimated_Loss_900': 7,\n",
       " 'Estimated_Loss_950': 5,\n",
       " 'Estimated_Loss_990': 2,\n",
       " 'Estimated_Loss_999': 1}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group the DataFrame by 'issue_year' and 'issue_month'\n",
    "grouped = df_12.groupby(['issue_y', 'issue_m'])\n",
    "\n",
    "# Initialize a dictionary to store results\n",
    "results = {\n",
    "    'loss': [],\n",
    "    'Estimated_Loss_700': [],\n",
    "    'Estimated_Loss_750': [], \n",
    "    'Estimated_Loss_800': [],\n",
    "    'Estimated_Loss_850': [],\n",
    "    'Estimated_Loss_900': [],\n",
    "    'Estimated_Loss_950': [],\n",
    "    'Estimated_Loss_990': [],\n",
    "    'Estimated_Loss_999': []\n",
    "}\n",
    "\n",
    "# Calculate sums for each group\n",
    "for name, group in grouped:\n",
    "    for key in results.keys():\n",
    "        results[key].append(group[key].sum())\n",
    "\n",
    "# Convert to DataFrame for easier comparison\n",
    "results_df = pd.DataFrame(results, index=grouped.size().index.rename(['Year', 'Month']))\n",
    "\n",
    "# Frequency of actual loss sum exceeding estimated loss sums\n",
    "frequencies = {}\n",
    "for col in results_df.columns[1:]:  # Skip the first column which is 'loss'\n",
    "    frequencies[col] = np.sum(results_df['loss'] > results_df[col])\n",
    "\n",
    "frequencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "41807eb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Estimated_Loss_700': {'Test Statistic': 17.968789285498545,\n",
       "  'p-value': 2.2455678799196215e-05,\n",
       "  'Model Accepted': False},\n",
       " 'Estimated_Loss_750': {'Test Statistic': 10.901646584746999,\n",
       "  'p-value': 0.0009607881622090068,\n",
       "  'Model Accepted': False},\n",
       " 'Estimated_Loss_800': {'Test Statistic': 6.834136583428489,\n",
       "  'p-value': 0.008943191190051996,\n",
       "  'Model Accepted': False},\n",
       " 'Estimated_Loss_850': {'Test Statistic': 2.2221072265589044,\n",
       "  'p-value': 0.13604725944422952,\n",
       "  'Model Accepted': True},\n",
       " 'Estimated_Loss_900': {'Test Statistic': 0.2732655597868856,\n",
       "  'p-value': 0.6011495716844243,\n",
       "  'Model Accepted': True},\n",
       " 'Estimated_Loss_950': {'Test Statistic': 0.1515808572266124,\n",
       "  'p-value': 0.6970291891314362,\n",
       "  'Model Accepted': True},\n",
       " 'Estimated_Loss_990': {'Test Statistic': 1.1662588918272014,\n",
       "  'p-value': 0.28017127303593437,\n",
       "  'Model Accepted': True},\n",
       " 'Estimated_Loss_999': {'Test Statistic': 3.1319123018977155,\n",
       "  'p-value': 0.07677363526051298,\n",
       "  'Model Accepted': True}}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import chi2\n",
    "# Total number of trials (days)\n",
    "n = 84\n",
    "# Observed number of exceedances for each confidence level\n",
    "exceedances = {'Estimated_Loss_700': 9,\n",
    " 'Estimated_Loss_750': 9,\n",
    " 'Estimated_Loss_800': 8,\n",
    " 'Estimated_Loss_850': 8,\n",
    " 'Estimated_Loss_900': 7,\n",
    " 'Estimated_Loss_950': 5,\n",
    " 'Estimated_Loss_990': 2,\n",
    " 'Estimated_Loss_999': 1}\n",
    "\n",
    "# Calculate Kupiec's test statistic and p-value for each confidence level\n",
    "results = {}\n",
    "for level, exceptions in exceedances.items():\n",
    "    # Extract the numeric value from the level key\n",
    "    confidence_percentage = int(level.split('_')[-1])\n",
    "    # Calculate p from the confidence percentage\n",
    "    p = (1000 - confidence_percentage) / 1000\n",
    "    \n",
    "    # Handle edge cases for log calculations\n",
    "    p = max(p, 1e-10)  # prevent log(0)\n",
    "    q = 1 - p\n",
    "    q = max(q, 1e-10)  # prevent log(0)\n",
    "    proportion = exceptions / n\n",
    "    proportion = max(proportion, 1e-10)  # prevent log(0)\n",
    "    one_minus_proportion = 1 - proportion\n",
    "    one_minus_proportion = max(one_minus_proportion, 1e-10)  # prevent log(0)\n",
    "    \n",
    "    # Compute the components for the Kupiec test\n",
    "    ln_part_model = (n - exceptions) * np.log(q) + exceptions * np.log(p)\n",
    "    ln_part_actual = (n - exceptions) * np.log(one_minus_proportion) + exceptions * np.log(proportion)\n",
    "    test_statistic = -2 * (ln_part_model - ln_part_actual)\n",
    "    \n",
    "    # Calculate the p-value\n",
    "    p_value = chi2.sf(test_statistic, 1)  # survival function for chi-squared distribution\n",
    "\n",
    "    # Store the results\n",
    "    results[level] = {\n",
    "        'Test Statistic': test_statistic,\n",
    "        'p-value': p_value,\n",
    "        'Model Accepted': p_value > 0.05\n",
    "    }\n",
    "\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ac6f83bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 225005 entries, 7 to 1305496\n",
      "Data columns (total 10 columns):\n",
      " #   Column           Non-Null Count   Dtype  \n",
      "---  ------           --------------   -----  \n",
      " 0   id               225005 non-null  int64  \n",
      " 1   exposure         225005 non-null  float64\n",
      " 2   grade            225005 non-null  object \n",
      " 3   actual_class     225005 non-null  int64  \n",
      " 4   predicted_class  225005 non-null  int64  \n",
      " 5   uncon_PD         225005 non-null  float64\n",
      " 6   loss             225005 non-null  float64\n",
      " 7   issue_y          225005 non-null  int64  \n",
      " 8   issue_m          225005 non-null  int64  \n",
      " 9   loss_rate        225005 non-null  float64\n",
      "dtypes: float64(4), int64(5), object(1)\n",
      "memory usage: 18.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b29926dd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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