{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c323995f-13b4-4f19-b096-1702667be6c0",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "# Discrétisation d'une EDS: erreur faible\n",
    "\n",
    "Soit $(x_t)_{t \\in [0,T]}$ solution d'une EDS \n",
    "\\begin{equation*}\n",
    "    \\operatorname{d}\\! x_t = b(x_t) \\operatorname{d}\\! t + \\sigma(x_t) \\operatorname{d}\\! W_t, \\quad x_0 \\in \\mathbf{R^d}.\n",
    "\\end{equation*}\n",
    "On se donne des instants de discrétisation $0=t_0 < t_1 < \\dots < t_N = T$ et dans la suite on spécifie la grille homogène $t_n = n \\frac{T}{N} = n h$ avec $h = \\frac{T}{N}$ appelé le pas de discrétisation.\n",
    "\n",
    "**Schéma d'Euler**   \n",
    "On rappelle le schéma d'Euler pour une diffusion:\n",
    "\\begin{equation*}\n",
    "    X_{t_{n+1}} = X_{t_n} + b(X_{t_n}) h + \\sigma(X_{t_n}) \\sqrt{h} G_{n+1}, \\quad X_0 = x_0.\n",
    "\\end{equation*}\n",
    "où dans toute la suite $(G_1,\\dots,G_N)$ est un vecteur Gaussien centré réduit (composantes indépendantes). La loi de $\\sqrt{h} G_{n+1}$ est celle de l'accroissement $W_{t_{n+1}} - W_{t_n}$.\n",
    "\n",
    "**Schéma de Milstein**  \n",
    "Avec les mêmes notations que précédemment on définit le schéma de Milstein pour une diffusion (en dimension 1):\n",
    "\\begin{equation*}\n",
    "    X_{t_{n+1}} = X_{t_n} + b(X_{t_n}) h + \\sigma(X_{t_n}) \\sqrt{h} G_{n+1} + \\frac{1}{2} (\\sigma \\sigma')(X_{t_n})(G_{n+1}^2 - 1) h.\n",
    "\\end{equation*}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1bb383e8-5124-4169-b672-77bd10f1b6d1",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2026-01-30T14:39:24.219741Z",
     "iopub.status.busy": "2026-01-30T14:39:24.219649Z",
     "iopub.status.idle": "2026-01-30T14:39:24.917599Z",
     "shell.execute_reply": "2026-01-30T14:39:24.917100Z",
     "shell.execute_reply.started": "2026-01-30T14:39:24.219730Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_theme() \n",
    "from numpy.random import default_rng\n",
    "rng = default_rng()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "705622fa-3f3c-47a4-aaa0-2ce75a1b5ed9",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Diffusion Black-Scholes comme \"benchmark\"\n",
    "\n",
    "L'équation différentielle stochastique (EDS) est donnée par \n",
    "\\begin{equation*}\n",
    "    \\operatorname{d}\\! x_t = r x_t \\operatorname{d}\\! t + \\sigma x_t \\operatorname{d}\\! W_t, \\quad x_0 > 0\n",
    "\\end{equation*}\n",
    "\n",
    "**Rappel:** Pour Black-Scholes on connaît la vraie solution \n",
    "\\begin{equation*}\n",
    "    \\forall t \\in [0,T], \\quad x_t = x_0 e^{\\left(r - \\frac{\\sigma^2}{2} \\right) t + \\sigma W_t}.\n",
    "\\end{equation*}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "55ba712a-1ca9-4384-ac77-5882cd8424b7",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2026-01-30T14:39:24.917952Z",
     "iopub.status.busy": "2026-01-30T14:39:24.917861Z",
     "iopub.status.idle": "2026-01-30T14:39:24.920580Z",
     "shell.execute_reply": "2026-01-30T14:39:24.920192Z",
     "shell.execute_reply.started": "2026-01-30T14:39:24.917946Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class BlackScholes:\n",
    "    def __init__(self, x0, r, sigma, T):\n",
    "        self.x0 = x0\n",
    "        self.r = r\n",
    "        self.sigma = sigma\n",
    "        self.T = T        \n",
    "    def paths_exact(self, dW):\n",
    "        N, M = dW.shape\n",
    "        h = self.T / N\n",
    "        x = np.empty(shape=(N+1,M))\n",
    "        x[0] = self.x0\n",
    "        for n in range(1, N+1): \n",
    "            x[n] = x[n-1] * np.exp((self.r - 0.5 * self.sigma**2) * h + self.sigma * dW[n-1])\n",
    "        # on pourrait utiliser np.cumprod à la place de cette boucle...\n",
    "        return x\n",
    "    def paths_euler(self, dW):\n",
    "        N, M = dW.shape\n",
    "        h = self.T / N\n",
    "        X = np.empty(shape=(N+1,M))\n",
    "        X[0] = self.x0\n",
    "        for n in range(1, N+1):\n",
    "            X[n] = X[n-1] + self.r * X[n-1] * h + self.sigma * X[n-1] * dW[n-1]\n",
    "        return X\n",
    "    def paths_milstein(self, dW):\n",
    "        N, M = dW.shape\n",
    "        h = self.T / N \n",
    "        X = np.empty(shape=(N+1,M))\n",
    "        X[0] = self.x0\n",
    "        for n in range(1, N+1):\n",
    "            X[n] = X[n-1] + self.r * X[n-1] * h +\\\n",
    "                    self.sigma * X[n-1] * dW[n-1] +\\\n",
    "                    0.5 * self.sigma**2 * X[n-1] * (dW[n-1]**2-h)\n",
    "        return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aeb0b53d-23a6-4681-93d3-9342e7ec969d",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2026-01-30T14:39:24.920872Z",
     "iopub.status.busy": "2026-01-30T14:39:24.920818Z",
     "iopub.status.idle": "2026-01-30T14:39:24.922693Z",
     "shell.execute_reply": "2026-01-30T14:39:24.922334Z",
     "shell.execute_reply.started": "2026-01-30T14:39:24.920867Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = BlackScholes(x0=100, r=0.2, sigma=0.3, T=1)\n",
    "N, M = 20, 15\n",
    "dW = np.sqrt(X.T / N) * rng.standard_normal((N, M))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3aba2a91-43b0-4ae4-a124-01510a5ad96f",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2026-01-30T14:39:24.922937Z",
     "iopub.status.busy": "2026-01-30T14:39:24.922883Z",
     "iopub.status.idle": "2026-01-30T14:39:24.925571Z",
     "shell.execute_reply": "2026-01-30T14:39:24.925227Z",
     "shell.execute_reply.started": "2026-01-30T14:39:24.922932Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([119.46156536, 130.46437367, 123.55201175, 113.76734568,\n",
       "       126.21435506,  57.61536451,  88.82867419, 220.3396059 ,\n",
       "       148.36240909, 110.81700528,  75.3313825 , 174.08950571,\n",
       "       137.05828578, 109.78925138,  87.95078526])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.paths_exact(dW)[-1] # les valeurs terminales en T des 15 actifs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "addefd5b-2d0f-4ccb-badf-4184fc956dc7",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2026-01-30T14:39:24.925814Z",
     "iopub.status.busy": "2026-01-30T14:39:24.925764Z",
     "iopub.status.idle": "2026-01-30T14:39:24.978939Z",
     "shell.execute_reply": "2026-01-30T14:39:24.978591Z",
     "shell.execute_reply.started": "2026-01-30T14:39:24.925810Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.lines import Line2D\n",
    "tt = np.linspace(0, X.T, N+1)\n",
    "fig, ax = plt.subplots(layout='tight')\n",
    "\n",
    "ax.plot(tt, X.paths_exact(dW))\n",
    "ax.plot(tt, X.paths_euler(dW), linestyle='dotted')\n",
    "ax.plot(tt, X.paths_milstein(dW), linestyle='dashed')\n",
    "\n",
    "legend_elements = [Line2D([0], [0], color='grey', label='Exact'),\n",
    "                   Line2D([0], [0], color='grey', linestyle='dotted', label='Euler'),\n",
    "                   Line2D([0], [0], color='grey', linestyle='dashed', label='Milstein')]\n",
    "ax.legend(handles=legend_elements, loc='upper left')\n",
    "\n",
    "fig.suptitle(fr'M={M} trajectoires sur [0,{X.T}] discrétisées avec N={N} points')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0064bb7b-76a2-434c-889b-f11835591e07",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Erreur faible\n",
    "\n",
    "On rappelle que l'erreur faible entre la diffusion $\\bigl(x_t\\bigr)_{t \\in [0,T]}$ et un schéma numérique $\\bigl(X^N_{k}\\bigr)_{k=0,\\dots, N}$ le long d'une fonction $\\varphi$ vérifie le développement suivant\n",
    "\\begin{equation*}\n",
    "    \\exists c_1, \\dots, c_R, \\qquad \\mathbb{E} \\bigl[ \\varphi(x_T) \\bigr]\n",
    "    - \\mathbb{E} \\bigl[ \\varphi(X^N_N) \\bigr] = \\frac{c_1}{N} + \\frac{c_2}{N^2} + \\cdots + \\frac{c_R}{N^R} + \\mathcal{O}\\bigl( \\frac{1}{N^R} \\bigr)\n",
    "\\end{equation*}\n",
    "\n",
    "Le but est d'illustrer numériquement une partie de ce résultat.\n",
    "\n",
    "On illustre les résultats de convergence de l'erreur faible du schéma d'Euler et de Milstein dans le cas d'un pricing d'un call de strike $K$ à maturité $T$. Ainsi on a \n",
    "\\begin{equation*}\n",
    "    \\operatorname{d}\\! x_t = r x_t \\operatorname{d}\\! t \n",
    "    + \\sigma x_t \\operatorname{d}\\! W_t, \\quad x_0 > 0,\n",
    "\\end{equation*}\n",
    "et \n",
    "\\begin{equation*}\n",
    "    P_{Call}(K) = e^{-r T} \\mathbb{E}\\bigl[ (x_T - K)_+ \\bigr] = \n",
    "    x_0 \\phi(d_1) - K e^{-r T} \\phi(d_2)\n",
    "\\end{equation*}\n",
    "avec $d_1 = \\frac{ \\log(S_0 / K) + (r+\\sigma^2/2) T}{\\sigma \\sqrt{T}}$, $d_2 = d_1 - \\sigma \\sqrt{T}$, et $\\phi$ la fonction de répartition d'une loi $\\mathcal{N}(0,1)$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "797faa59-a262-45c3-98a9-7bbabd75c77c",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "question"
    ]
   },
   "source": [
    "### Question: pricing du call Black-Scholes \n",
    "\n",
    "Ecrire une fonction `bs_call(X, K)` qui calcule le prix d'un call de strike `K` d'un objet `X` de classe Black-Scholes. On rappelle que l'objet `X` contient les paramètres de la maturité $T$, le taux $r$, la volatilité $\\sigma$ et la condition initiale $x_0$).  \n",
    "Dans la suite on prend $r=0.2$, $\\sigma=0.3$, $T=1$ et $K = x_0 = 100$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c93fd54e-71a5-4c35-91ea-2c284c46c5f2",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "aremplir"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prix exact de l'option : 22.2035\n"
     ]
    }
   ],
   "source": [
    "X = BlackScholes(x0=100, r=0.2, sigma=0.3, T=1)\n",
    "def bs_call(X : BlackScholes, K):\n",
    "    d1 = (np.log(X.x0/K) + (X.r + 0.5 * X.sigma**2) * X.T) / (X.sigma * np.sqrt(X.T))\n",
    "    d2 = d1 - X.sigma * np.sqrt(X.T)\n",
    "    return X.x0 * stats.norm.cdf(d1) - K * np.exp(-X.r * X.T) * stats.norm.cdf(d2)\n",
    "\n",
    "exact_price = bs_call(X, K=100)\n",
    "print(f'Prix exact de l\\'option : {exact_price:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba3605ca-faf6-4471-9868-d82114ee23fe",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "question"
    ]
   },
   "source": [
    "### Question: vérification du code, pricing Monte Carlo\n",
    "\n",
    "Faire un code Monte Carlo pour vérifier le prix obtenu en utilisant la méthode `paths_exact` de l'objet `X`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "72eb9629-98d3-48a4-8ae8-28d0e9acfd8c",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "aremplir"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prix estimé de l'option par Monte Carlo : 21.9049 ± 0.2622\n"
     ]
    }
   ],
   "source": [
    "def monte_carlo_call_price(X : BlackScholes, K, N, M):\n",
    "    dW = np.sqrt(X.T / N) * rng.standard_normal((N, M))\n",
    "    S_T = X.paths_exact(dW)[-1]\n",
    "    payoff = np.maximum(S_T - K, 0)\n",
    "    price = np.exp(-X.r * X.T) * np.mean(payoff)\n",
    "    stdev = np.exp(-X.r * X.T) * np.std(payoff) / np.sqrt(M)\n",
    "    return price, stdev\n",
    "\n",
    "N, M = 1000, 10000\n",
    "estimated_price, estimated_stdev = monte_carlo_call_price(X, K=100, N=N, M=M)\n",
    "print(f'Prix estimé de l\\'option par Monte Carlo : {estimated_price:.4f} ± {estimated_stdev:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "246d98ff-e79f-49bb-8ea0-26f211d45f30",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "question"
    ]
   },
   "source": [
    "### Question: estimation de l'erreur faible \n",
    "\n",
    "Ecrire une fonction `compute_call_bs(sde, scheme, N, M = 10000)` qui fait un code de Monte Carlo à partir de $M$ simulations d'un schéma de discrétisation (`scheme` est `\"euler\"` ou `\"milstein\"`) d'un nombre de pas de temps $N$. Le premier argument `sde` est un objet de la classe `BlackScholes`.\n",
    "\n",
    "Tester votre code en calculant le prix Monte Carlo biaisé en utilisant le schéma d'Euler puis du schéma de Milstein et en faisant varier $N$ de 2 à 128.\n",
    "\n",
    "Que peut-on dire des prix obtenus ? et des intervalles de confiances associés ? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f4510cdb-3812-4c57-b6dd-daf0bf454cf2",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "aremplir"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prix estimé de l'option par exact : 21.9850 ± 0.2604\n",
      "Prix estimé de l'option par euler : 22.0199 ± 0.2598\n",
      "Prix estimé de l'option par milstein : 22.1095 ± 0.2594\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>exact price ± stdev</th>\n",
       "      <th>euler price ± stdev</th>\n",
       "      <th>milstein price ± stdev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(22.114586560149707, 0.26304423938105465)</td>\n",
       "      <td>(21.04742560195535, 0.22273038247582966)</td>\n",
       "      <td>(20.5474759176418, 0.23770194210943096)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(22.204491226446354, 0.26316185331633996)</td>\n",
       "      <td>(21.513151953767018, 0.24047958560055768)</td>\n",
       "      <td>(21.011648370594155, 0.24441438202360846)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(22.54624028907915, 0.2696586514752491)</td>\n",
       "      <td>(22.14710901421763, 0.25513729407637337)</td>\n",
       "      <td>(21.732833835575992, 0.2538647106676578)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(22.30695116296572, 0.2667872526957249)</td>\n",
       "      <td>(21.53481682185777, 0.2536309233467119)</td>\n",
       "      <td>(22.151781908217394, 0.2612206999256248)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>(22.359634680337713, 0.26728940853832844)</td>\n",
       "      <td>(22.531500458802654, 0.26453199317491916)</td>\n",
       "      <td>(22.312647601644972, 0.2620479080486166)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>(22.16986753012122, 0.261740117596332)</td>\n",
       "      <td>(21.63902290667206, 0.25738097400265275)</td>\n",
       "      <td>(21.792986785787594, 0.262286589972619)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>(22.594202421855744, 0.2636929667449982)</td>\n",
       "      <td>(22.211646309940463, 0.26360002351366213)</td>\n",
       "      <td>(21.851706217418915, 0.26031469897088527)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           exact price ± stdev  \\\n",
       "2    (22.114586560149707, 0.26304423938105465)   \n",
       "4    (22.204491226446354, 0.26316185331633996)   \n",
       "8      (22.54624028907915, 0.2696586514752491)   \n",
       "16     (22.30695116296572, 0.2667872526957249)   \n",
       "32   (22.359634680337713, 0.26728940853832844)   \n",
       "64      (22.16986753012122, 0.261740117596332)   \n",
       "128   (22.594202421855744, 0.2636929667449982)   \n",
       "\n",
       "                           euler price ± stdev  \\\n",
       "2     (21.04742560195535, 0.22273038247582966)   \n",
       "4    (21.513151953767018, 0.24047958560055768)   \n",
       "8     (22.14710901421763, 0.25513729407637337)   \n",
       "16     (21.53481682185777, 0.2536309233467119)   \n",
       "32   (22.531500458802654, 0.26453199317491916)   \n",
       "64    (21.63902290667206, 0.25738097400265275)   \n",
       "128  (22.211646309940463, 0.26360002351366213)   \n",
       "\n",
       "                        milstein price ± stdev  \n",
       "2      (20.5474759176418, 0.23770194210943096)  \n",
       "4    (21.011648370594155, 0.24441438202360846)  \n",
       "8     (21.732833835575992, 0.2538647106676578)  \n",
       "16    (22.151781908217394, 0.2612206999256248)  \n",
       "32    (22.312647601644972, 0.2620479080486166)  \n",
       "64     (21.792986785787594, 0.262286589972619)  \n",
       "128  (21.851706217418915, 0.26031469897088527)  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "def compute_call_bs(sde : BlackScholes, scheme, N, M):\n",
    "    dW = np.sqrt(sde.T / N) * rng.standard_normal((N, M))\n",
    "    if scheme == 'exact':\n",
    "        S_T = sde.paths_exact(dW)[-1]\n",
    "    elif scheme == 'euler':\n",
    "        S_T = sde.paths_euler(dW)[-1]\n",
    "    elif scheme == 'milstein':\n",
    "        S_T = sde.paths_milstein(dW)[-1]\n",
    "    else:\n",
    "        raise ValueError(\"Scheme must be 'exact', 'euler' or 'milstein'\")\n",
    "    payoff = np.maximum(S_T - 100, 0)\n",
    "    price = np.exp(-sde.r * sde.T) * np.mean(payoff)\n",
    "    stdev = np.exp(-sde.r * sde.T) * np.std(payoff) / np.sqrt(M)\n",
    "    return price, stdev\n",
    "\n",
    "schemes = ['exact', 'euler', 'milstein']\n",
    "prices = [compute_call_bs(X, scheme, N, M) for scheme in schemes]\n",
    "for scheme, price in zip(schemes, prices):\n",
    "    print(f'Prix estimé de l\\'option par {scheme} : {price[0]:.4f} ± {price[1]:.4f}')\n",
    "\n",
    "N_list = [2**i for i in range(1, 8)]\n",
    "\n",
    "\n",
    "for scheme in schemes:\n",
    "    prices = [compute_call_bs(X, scheme, N, M) for N in N_list]\n",
    "    plt.plot(N_list, [p[0] for p in prices], label=scheme)\n",
    "    plt.fill_between(N_list, [p[0] - p[1] for p in prices], [p[0] + p[1] for p in prices], alpha=0.2, label=scheme)\n",
    "\n",
    "result = pd.DataFrame({scheme: [compute_call_bs(X, scheme, N, M) for N in N_list] for scheme in schemes}, index=N_list)\n",
    "result.columns = [f'{scheme} price ± stdev' for scheme in schemes]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "004e7d75-9b38-4a78-9b0e-438420bf7fab",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "question"
    ]
   },
   "source": [
    "### Question: visualisation\n",
    "\n",
    "Tracer les erreurs et les zones de confiances associées en fonction de $h = \\frac{1}{N}$ pour les 2 schémas de discrétisation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8de95b01-a8d3-47bc-9ef0-1ca7b7fee621",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8023b442-1310-42df-8057-b912d5a94196",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Extrapolation de Richardson\n",
    "\n",
    "L'extrapolation de Richardson est une technique permettant de gagner un ordre de convergence. En effet, en considérant une combinaison linéaire entre un schéma de pas $\\frac{T}{N}$ et un autre de pas $\\frac{T}{2N}$ on peut améliorer l'ordre de convergence en éliminant le premier terme $\\frac{c_1}{N}$ qui apparait dans le développement de l'erreur faible:\n",
    "\\begin{equation*}\n",
    "    \\mathbb{E} \\bigl[ \\varphi(x_T) \\bigr]\n",
    "    - \\mathbb{E} \\bigl[ 2 \\varphi(X^{2N}_{2N}) -  \\varphi(X^{N}_{N}) \\bigr] = -\\frac{c_2}{2 N^2} + \\mathcal{O}\\bigl( \\frac{1}{N^2} \\bigr)\n",
    "\\end{equation*}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f88fcd4f-15a4-44ab-9d7d-b0f72018e37a",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "question"
    ]
   },
   "source": [
    "### Question: estimation avec extrapolation\n",
    "\n",
    "Ecrire une fonction `compute_call_bs_richardson` similaire à `compute_call_bs` qui est un estimateur Monte Carlo de \n",
    "\\begin{equation*}\n",
    "\\mathbb{E} \\bigl[ 2 \\varphi(X^{2N}_{2N}) -  \\varphi(X^{N}_{N}) \\bigr]\n",
    "\\end{equation*}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c18c5eb5-edd6-4848-af4e-4c376ac2b476",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "aremplir"
    ]
   },
   "outputs": [],
   "source": [
    "def compute_call_bs_richardson(sde : BlackScholes, scheme, N, M):\n",
    "    dW_fine = np.sqrt(sde.T / N) * rng.standard_normal((N, M))\n",
    "    dW_coarse = dW_fine[::2, :] + dW_fine[1::2, :]\n",
    "    if scheme == 'exact':\n",
    "        paths_fine = sde.paths_exact(dW_fine)\n",
    "        paths_coarse = sde.paths_exact(dW_coarse)\n",
    "    elif scheme == 'euler':\n",
    "        paths_fine = sde.paths_euler(dW_fine)\n",
    "        paths_coarse = sde.paths_euler(dW_coarse)\n",
    "    elif scheme == 'milstein':\n",
    "        paths_fine = sde.paths_milstein(dW_fine)\n",
    "        paths_coarse = sde.paths_milstein(dW_coarse)\n",
    "    else:\n",
    "        raise ValueError(\"Scheme must be 'exact', 'euler' or 'milstein'\")\n",
    "    payoff_fine = np.maximum(paths_fine[-1] - 100, 0)\n",
    "    payoff_coarse = np.maximum(paths_coarse[-1] - 100, 0)\n",
    "    price = np.exp(-sde.r * sde.T) * (2 * np.mean(payoff_fine) - np.mean(payoff_coarse))\n",
    "    stdev = np.exp(-sde.r * sde.T) * np.std(payoff_fine - payoff_coarse) / np.sqrt(M)\n",
    "    return price, stdev"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7784bde-6171-4eba-b92e-bb7f00dca724",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "question"
    ]
   },
   "source": [
    "### Question: estimation et visualisation\n",
    "\n",
    "Reprendre l'étude précédente pour comparer les méthodes avec et sans extrapolation de Richardson-Romberg."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8ca35041-444c-49cc-b906-3b8ec1c7f223",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "aremplir"
    ]
   },
   "outputs": [],
   "source": [
    "Ns = 2**np.arange(1, 8)\n",
    "res_euler = [compute_call_bs_richardson(X, 'euler', N, M) for N in Ns]\n",
    "res_euler_df = pd.DataFrame({'N': Ns, 'Euler price ± stdev': res_euler})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "63459642-20f1-4601-97d4-f0231ff1d9bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>price</th>\n",
       "      <th>stdev</th>\n",
       "      <th>lower_1sigma</th>\n",
       "      <th>upper_1sigma</th>\n",
       "      <th>lower_95</th>\n",
       "      <th>upper_95</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>22.622014</td>\n",
       "      <td>0.040019</td>\n",
       "      <td>22.581995</td>\n",
       "      <td>22.662033</td>\n",
       "      <td>22.543576</td>\n",
       "      <td>22.700452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>22.450015</td>\n",
       "      <td>0.029403</td>\n",
       "      <td>22.420612</td>\n",
       "      <td>22.479417</td>\n",
       "      <td>22.392385</td>\n",
       "      <td>22.507644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>22.066634</td>\n",
       "      <td>0.021375</td>\n",
       "      <td>22.045259</td>\n",
       "      <td>22.088009</td>\n",
       "      <td>22.024738</td>\n",
       "      <td>22.108529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>22.619715</td>\n",
       "      <td>0.015216</td>\n",
       "      <td>22.604499</td>\n",
       "      <td>22.634931</td>\n",
       "      <td>22.589892</td>\n",
       "      <td>22.649539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>22.136467</td>\n",
       "      <td>0.011026</td>\n",
       "      <td>22.125441</td>\n",
       "      <td>22.147492</td>\n",
       "      <td>22.114856</td>\n",
       "      <td>22.158077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>64</td>\n",
       "      <td>22.668339</td>\n",
       "      <td>0.007760</td>\n",
       "      <td>22.660580</td>\n",
       "      <td>22.676099</td>\n",
       "      <td>22.653130</td>\n",
       "      <td>22.683549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>128</td>\n",
       "      <td>22.465535</td>\n",
       "      <td>0.005447</td>\n",
       "      <td>22.460088</td>\n",
       "      <td>22.470982</td>\n",
       "      <td>22.454859</td>\n",
       "      <td>22.476211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     N      price     stdev  lower_1sigma  upper_1sigma   lower_95   upper_95\n",
       "0    2  22.622014  0.040019     22.581995     22.662033  22.543576  22.700452\n",
       "1    4  22.450015  0.029403     22.420612     22.479417  22.392385  22.507644\n",
       "2    8  22.066634  0.021375     22.045259     22.088009  22.024738  22.108529\n",
       "3   16  22.619715  0.015216     22.604499     22.634931  22.589892  22.649539\n",
       "4   32  22.136467  0.011026     22.125441     22.147492  22.114856  22.158077\n",
       "5   64  22.668339  0.007760     22.660580     22.676099  22.653130  22.683549\n",
       "6  128  22.465535  0.005447     22.460088     22.470982  22.454859  22.476211"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import ast\n",
    "\n",
    "col = \"Euler price ± stdev\"\n",
    "\n",
    "# If the tuples are stored as strings (sometimes happens), convert safely to real tuples\n",
    "res_euler_df[col] = res_euler_df[col].apply(\n",
    "    lambda x: ast.literal_eval(x) if isinstance(x, str) else x\n",
    ")\n",
    "\n",
    "# Split (price, stdev) into two columns\n",
    "tmp = res_euler_df[col].apply(pd.Series)\n",
    "tmp.columns = [\"price\", \"stdev\"]\n",
    "\n",
    "# Build output dataframe\n",
    "df_out = pd.concat([res_euler_df[[\"N\"]], tmp], axis=1)\n",
    "\n",
    "# Bounds: choose either 1-sigma or 95% CI (z=1.96)\n",
    "df_out[\"lower_1sigma\"] = df_out[\"price\"] - df_out[\"stdev\"]\n",
    "df_out[\"upper_1sigma\"] = df_out[\"price\"] + df_out[\"stdev\"]\n",
    "\n",
    "z = 1.96\n",
    "df_out[\"lower_95\"] = df_out[\"price\"] - z * df_out[\"stdev\"]\n",
    "df_out[\"upper_95\"] = df_out[\"price\"] + z * df_out[\"stdev\"]\n",
    "\n",
    "df_out"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  },
  "toc-autonumbering": true
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
