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MAINT: Fix default params in cir and Heston stochastic process #401

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Nov 23, 2021
Merged

MAINT: Fix default params in cir and Heston stochastic process #401

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cf4c185
fix default params in cir and Heston stochastic process
masanorihirano Nov 16, 2021
ba3e6bf
Update pfhedge/stochastic/cir.py
masanorihirano Nov 16, 2021
5ff59bc
Update pfhedge/stochastic/heston.py
masanorihirano Nov 16, 2021
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8 changes: 6 additions & 2 deletions pfhedge/stochastic/cir.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,7 @@
def generate_cir(
n_paths: int,
n_steps: int,
init_state: Tuple[TensorOrScalar, ...] = (0.04,),
init_state: Optional[Tuple[TensorOrScalar, ...]] = None,
kappa: TensorOrScalar = 1.0,
theta: TensorOrScalar = 0.04,
sigma: TensorOrScalar = 0.2,
Expand All @@ -37,10 +37,11 @@ def generate_cir(
Args:
n_paths (int): The number of simulated paths.
n_steps (int): The number of time steps.
init_state (tuple[torch.Tensor | float], default=(0.04,)): The initial state of
init_state (tuple[torch.Tensor | float], optional): The initial state of
the time series.
This is specified by a tuple :math:`(X(0),)`.
It also accepts a :class:`torch.Tensor` or a :class:`float`.
If ``None`` (default), it uses :math:`(\theta, )`.
kappa (torch.Tensor or float, default=1.0): The parameter :math:`\kappa`.
theta (torch.Tensor or float, default=0.04): The parameter :math:`\theta`.
sigma (torch.Tensor or float, default=2.0): The parameter :math:`\sigma`.
Expand Down Expand Up @@ -71,6 +72,9 @@ def generate_cir(
tensor([[0.0400, 0.0408, 0.0411, 0.0417, 0.0422],
[0.0400, 0.0395, 0.0452, 0.0434, 0.0446]])
"""
if init_state is None:
init_state = (theta,)

# Accept Union[float, Tensor] as well because making a tuple with a single element
# is troublesome
if isinstance(init_state, (float, Tensor)):
Expand Down
7 changes: 5 additions & 2 deletions pfhedge/stochastic/heston.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,7 +32,7 @@ def volatility(self) -> Tensor:
def generate_heston(
n_paths: int,
n_steps: int,
init_state: Tuple[TensorOrScalar, ...] = (1.0, 0.04),
init_state: Optional[Tuple[TensorOrScalar, ...]] = None,
kappa: float = 1.0,
theta: float = 0.04,
sigma: float = 0.2,
Expand Down Expand Up @@ -62,9 +62,10 @@ def generate_heston(
Args:
n_paths (int): The number of simulated paths.
n_steps (int): The number of time steps.
init_state (tuple[torch.Tensor | float], default=(1.0,)): The initial state of
init_state (tuple[torch.Tensor | float], optional): The initial state of
the time series.
This is specified by a tuple :math:`(S(0), V(0))`.
If ``None`` (default), it uses :math:`(1.0, \\theta)`.
kappa (float, default=1.0): The parameter :math:`\\kappa`.
theta (float, default=0.04): The parameter :math:`\\theta`.
sigma (float, default=2.0): The parameter :math:`\\sigma`.
Expand Down Expand Up @@ -101,6 +102,8 @@ def generate_heston(
tensor([[0.0400, 0.0408, 0.0411, 0.0417, 0.0422],
[0.0400, 0.0395, 0.0452, 0.0434, 0.0446]])
"""
if init_state is None:
init_state = (1.0, theta)
# Cast to init_state: Tuple[Tensor, ...] with desired dtype and device
init_state = cast(Tuple[Tensor, ...], tuple(map(torch.as_tensor, init_state)))
init_state = tuple(map(lambda t: t.to(dtype=dtype, device=device), init_state))
Expand Down