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diff --git a/utils/path.py b/utils/path.py
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+# author : S. Mandalia
+#          shivesh.mandalia@outlook.com
+#
+# date   : March 19, 2020
+
+"""
+Path generator for underlying.
+"""
+
+import math
+import random
+from copy import deepcopy
+from dataclasses import dataclass
+from typing import List, Tuple
+
+
+__all__ = ['PathGenerator']
+
+
+@dataclass
+class PathGenerator:
+    """
+    Class for generating underlying prices using MC techniques.
+
+    Attributes
+    ----------
+    S : Spot price.
+    r : Risk-free interest rate.
+    div : Dividend yield.
+    vol : Volatility.
+    net_r : Net risk free rate.
+
+    Methods
+    ----------
+    generate(T)
+        Generate a random path {S_t1, S_t2, ..., S_tn}.
+    generate_antithetic(T)
+        Generate a random plus antithetic path
+        [{S_t1, S_t2, ..., S_tn}, {S'_t1, S'_t2, ..., S'_tn}].
+
+    Examples
+    ----------
+    >>> from utils.path import PathGenerator
+    >>> path = PathGenerator(S=100., r=0.1, div=0.01, vol=0.3)
+    >>> print(path.generate(T=range(4)))
+    [100.0, 91.11981160354563, 94.87596210593794, 117.44132223235353]
+    >>> print(path.generate(T=range(4)))
+    [100.0, 68.73668230722738, 71.43490333826567, 70.70833180133955]
+
+    """
+    S: float
+    r: float
+    div: float
+    vol: float
+
+    @property
+    def net_r(self) -> float:
+        """Net risk free rate."""
+        return self.r - self.div
+
+    def generate(self, T: List[float]) -> List[float]:
+        """
+        Generate a random path {S_t1, S_t2, ..., S_tn}.
+
+        Parameters
+        ----------
+        T : Set of times {t1, t2, ..., tn} in years.
+
+        Returns
+        ----------
+        spot_prices : Set of prices for the underlying {S_t1, S_t2, ..., S_tn}.
+
+        """
+        # Calculate dt time differences
+        dts = [T[idx + 1] - T[idx] for idx in range(len(T) - 1)]
+
+        spot_prices = [0] * len(T)
+        spot_prices[0] = self.S
+        for idx, dt in enumerate(dts):
+            # Calculate the drift e^{(r - (1/2) σ²) Δt}
+            drift = math.exp((self.net_r - (1/2) * self.vol**2) * dt)
+
+            # Calculate the volatility term e^{σ √{Δt} N(0, 1)}
+            rdm_gauss = random.gauss(0, 1)
+            vol_term = math.exp(self.vol * math.sqrt(dt) * rdm_gauss)
+
+            # Calculate next spot price
+            S_t = spot_prices[idx] * drift * vol_term
+            spot_prices[idx + 1] = S_t
+        return spot_prices
+
+    def generate_antithetic(self, T: List[float]) -> Tuple[List[float],
+                                                           List[float]]:
+        """
+        Generate a random plus antithetic path
+        [{S_t1, S_t2, ..., S_tn}, {S'_t1, S'_t2, ..., S'_tn}].
+
+        Parameters
+        ----------
+        T : Set of times {t1, t2, ..., tn} in years.
+
+        Returns
+        ----------
+        prices_tuple : Set of prices for the underlying
+                       [{S_t1, S_t2, ..., S_tn}, {S'_t1, S'_t2, ..., S'_tn}].
+
+        """
+        # Calculate dt time differences
+        dts = [T[idx + 1] - T[idx] for idx in range(len(T) - 1)]
+
+        # Create data structures
+        spot_prices = [0] * len(T)
+        spot_prices[0] = self.S
+
+        a_spot_prices = deepcopy(spot_prices)
+
+        for idx, dt in enumerate(dts):
+            # Calculate the drift e^{(r - (1/2) σ²) Δt}
+            drift = math.exp((self.net_r - (1/2) * self.vol**2) * dt)
+
+            # Calculate the volatility term e^{σ √{Δt} N(0, 1)}
+            rdm_gauss = random.gauss(0, 1)
+            a_gauss = -rdm_gauss
+            vol_term = math.exp(self.vol * math.sqrt(dt) * rdm_gauss)
+            a_vol_term = math.exp(self.vol * math.sqrt(dt) * a_gauss)
+
+            # Calculate next spot price
+            S_t = spot_prices[idx] * drift * vol_term
+            a_S_t = a_spot_prices[idx] * drift * a_vol_term
+
+            # Add to data structure
+            spot_prices[idx + 1] = S_t
+            a_spot_prices[idx + 1] = a_S_t
+
+        return (spot_prices, a_spot_prices)