Release/0.7.4

pfhedge/instruments/primary/brownian.py

@@ -12,6 +12,9 @@ class BrownianStock(Primary):
 
     The drift of the spot prices is assumed to be vanishing.
 
+    See :func:`pfhedge.stochastic.generate_geometric_brownian`
+    for details of the process.
+
     Args:
         volatility (float, default=0.2): The volatility of the price.
         cost (float, default=0.0): The transaction cost rate.

pfhedge/instruments/primary/heston.py

@@ -10,7 +10,7 @@
 class HestonStock(Primary):
     """A stock of which spot price and variance follow Heston process.
 
-    See :func:`pfhedge.stochastic.generate_heston` for details of the Heston process.
+    See :func:`pfhedge.stochastic.generate_heston` for details of the process.
 
     Args:
         kappa (float, default=1.0): The parameter :math:`\\kappa`.

pfhedge/nn/modules/bs/american_binary.py

@@ -43,6 +43,10 @@ class BSAmericanBinaryOption(BSModuleMixin):
         tensor([[1.1285],
                 [0.0000],
                 [0.0000]])
+
+    References:
+        Dai, M., 2000. A closed-form solution for perpetual American floating strike
+        lookback options. Journal of Computational Finance, 4(2), pp.63-68.
     """
 
     def __init__(self, call: bool = True, strike: float = 1.0):

pfhedge/nn/modules/bs/european.py

@@ -41,6 +41,9 @@ class BSEuropeanOption(BSModuleMixin):
         tensor([[0.4497],
                 [0.5126],
                 [0.5752]])
+
+    References:
+        John C. Hull, 2003. Options futures and other derivatives. Pearson.
     """
 
     def __init__(self, call: bool = True, strike: float = 1.0):
@@ -142,7 +145,6 @@ def price(
 
         Args:
             log_moneyness (torch.Tensor): Log moneyness of the underlying asset.
-            max_log_moneyness (torch.Tensor): Cumulative maximum of the log moneyness.
             expiry_time (torch.Tensor): Time to expiry of the option.
             volatility (torch.Tensor): Volatility of the underlying asset.
 

pfhedge/nn/modules/bs/european_binary.py

@@ -42,6 +42,9 @@ class BSEuropeanBinaryOption(BSModuleMixin):
         tensor([[6.2576],
                 [6.3047],
                 [6.1953]])
+
+    References:
+        John C. Hull, 2003. Options futures and other derivatives. Pearson.
     """
 
     def __init__(self, call: bool = True, strike: float = 1.0):

pfhedge/nn/modules/bs/lookback.py

@@ -44,6 +44,10 @@ class BSLookbackOption(BSModuleMixin):
         tensor([[0.9208],
                 [1.0515],
                 [1.0515]])
+
+    References:
+        Conze, A., 1991. Path dependent options: The case of lookback options.
+        The Journal of Finance, 46(5), pp.1893-1907.
     """
 
     def __init__(self, call: bool = True, strike: float = 1.0):

pfhedge/stochastic/brownian.py

@@ -17,6 +17,12 @@ def generate_brownian(
 
     The drift of the time series is assumed to be vanishing.
 
+    The time evolution of the process is given by:
+
+    .. math ::
+
+        dS(t) = \\sigma dW(t) \\,.
+
     Args:
         n_paths (int): The number of simulated paths.
         n_steps (int): The number of time steps.
@@ -66,6 +72,12 @@ def generate_geometric_brownian(
 
     The drift of the time series is assumed to be vanishing.
 
+    The time evolution of the process is given by:
+
+    .. math ::
+
+        dS(t) = \\sigma S(t) dW(t) \\,.
+
     Args:
         n_paths (int): The number of simulated paths.
         n_steps (int): The number of time steps.

pfhedge/stochastic/cir.py

@@ -15,12 +15,14 @@ def generate_cir(
 ) -> Tensor:
     """Returns time series following Cox-Ingersoll-Ross process.
 
-    The time evolution of CIR process is given by:
+    The time evolution of the process is given by:
 
     .. math ::
 
         dX(t) = \\kappa (\\theta - X(t)) + \\sigma \\sqrt{X(t)} dW(t) \\,.
 
+    Time-series is generated by Andersen's QE-M method (See Reference for details).
+
     Args:
         n_paths (int): The number of simulated paths.
         n_steps (int): The number of time steps.

pfhedge/stochastic/heston.py

@@ -21,15 +21,16 @@ def generate_heston(
 ) -> Tuple[Tensor, Tensor]:
     """Returns time series following Heston model.
 
-    The time evolution of Heston process is given by:
+    The time evolution of the process is given by:
 
     .. math ::
 
         dS(t) = S(t) \\sqrt{V(t)} dW_1(t) \\,, \\\\
         dV(t) = \\kappa (\\theta - V(t)) + \\sigma \\sqrt{V(t)} dW_2(t) \\,.
 
     The correlation between :math:`dW_1` and :math:`dW_2` is :math:`\\rho`.
-    The correlation between :math:`dW_1` and :math:`dW_2` is `\\rho`.
+
+    Time-series is generated by Andersen's QE-M method (See Reference for details).
 
     Args:
         n_paths (int): The number of simulated paths.

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "pfhedge"
-version = "0.7.3"
+version = "0.7.4"
 description = "Deep Hedging in PyTorch"
 authors = ["Shota Imaki <shota.imaki.0801@gmail.com>"]
 license = "MIT"