Probability_debt_default.ipynb

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        "# Estimating Probability of Debt Default"
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              "            Prior  Likeli_Neg  Joint1  Posterior1\n",
              "Default       0.1         0.7    0.07        0.16\n",
              "No Default    0.9         0.4    0.36        0.84"
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              "      <th>Default</th>\n",
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              "      <th>No Default</th>\n",
              "      <td>0.9</td>\n",
              "      <td>0.4</td>\n",
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      "source": [
        "# Import Python libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# Create a dataframe for your bond analysis\n",
        "bonds = pd.DataFrame(index=['Default', 'No Default'])\n",
        "\n",
        "# The prior probability of default\n",
        "# P(Default) = 0.10 and P(No Default) = 0.90\n",
        "\n",
        "bonds['Prior'] = 0.10, 0.90\n",
        "\n",
        "# The likelihood functions for observing negative ratings\n",
        "# P(Negative|Default) = 0.70 and P(Negative|No Default) = 0.40\n",
        "\n",
        "bonds['Likeli_Neg'] = 0.70, 0.40\n",
        "\n",
        "# Joint probabilities of seeing a negative rating depending on\n",
        "# default or no default\n",
        "# P(Negative|Default) * P(Default) and P(Negative|No Default) * P(No Default)\n",
        "\n",
        "bonds['Joint1'] = bonds['Likeli_Neg'] * bonds['Prior']\n",
        "\n",
        "# Add the joint probabilities to get the marginal likelihood or unconditional\n",
        "# probability of observing a negative rating\n",
        "# P(Negative) = P(Negative|Default) * P(Default) + P(Negative|No Default) * P(No Default)\n",
        "\n",
        "prob_neg_data = bonds['Joint1'].sum()\n",
        "\n",
        "# Use the inverse probability rule to calculate the updated probability of\n",
        "# default based on the new negative rating and then print the data table.\n",
        "\n",
        "bonds['Posterior1'] = bonds['Likeli_Neg'] * bonds['Prior']/prob_neg_data\n",
        "bonds.round(2)"
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        "#Our new prior probability is our previous posterior probability, Posterior1.\n",
        "#Compute and print the table.\n",
        "\n",
        "bonds['Joint2'] = bonds['Likeli_Neg'] * bonds['Posterior1']\n",
        "prob_neg_data = bonds['Joint2'].sum()\n",
        "bonds['Posterior2'] = bonds['Likeli_Neg'] * bonds['Posterior1']/prob_neg_data\n",
        "bonds.round(2)"
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              "            Prior  Likeli_Neg  Joint1  Posterior1  Joint2  Posterior2\n",
              "Default       0.1         0.7    0.07        0.16    0.11        0.25\n",
              "No Default    0.9         0.4    0.36        0.84    0.33        0.75"
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              "      <th>Prior</th>\n",
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              "      <td>0.07</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.25</td>\n",
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              "    <tr>\n",
              "      <th>No Default</th>\n",
              "      <td>0.9</td>\n",
              "      <td>0.4</td>\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-28ab59ef-2512-4cde-854f-bc0433d7b55d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a new table so that you can plot a graph with the appropriate information\n",
        "table = bonds[['Prior', 'Posterior1', 'Posterior2']].round(2)\n",
        "\n",
        "# Change columns so that x axis is the number of negative ratings\n",
        "table.columns = ['0', '1', '2']\n",
        "\n",
        "# Select the row to plot in the graph and print it.\n",
        "default_row = table.iloc[0]\n",
        "default_row.plot(figsize = (7,6), grid = True,\n",
        "xlabel = 'Updates based on recent negative ratings',\n",
        "ylabel = 'Probability of default', title = 'XYZ Bonds');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "cGzPErR85jYq",
        "outputId": "5a6234b9-c32d-4bd7-e105-5cd006110721"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}