Vol Model Calibration

class quantflow.options.calibration.VolModelCalibration(*, pricer: ~quantflow.options.pricer.OptionPricer[TypeVar], vol_surface: ~quantflow.options.surface.VolSurface[~typing.Any], minimize_method: str | None = None, moneyness_weight: ~typing.Annotated[float, ~annotated_types.Ge(ge=0)] = 0.0, ttm_weight: ~typing.Annotated[float, ~annotated_types.Ge(ge=0.0), ~annotated_types.Le(le=1.0)] = 0.0, options: dict[~quantflow.options.calibration.ModelCalibrationEntryKey, ~quantflow.options.calibration.OptionEntry] = <factory>)

Abstract class for calibration of a stochastic volatility model

Methods:

cost_function	Calculate the cost function from the model prices
cost_weight	Calculate the weight for the cost function for a given time to maturity and moneyness
fit	Fit the model
get_bounds	Get the parameter bounds for the calibration
get_constraints	Get the constraints for the calibration
get_params	Get the parameters of the model
implied_vol_range	Get the range of implied volatilities
model_post_init	Override this method to perform additional initialization after __init__ and model_construct.
penalize	Penalize the cost function
plot	Plot the implied volatility for market and model prices
set_params	Set the parameters of the model

Attributes:

minimize_method	The optimization method to use - if None, the default is used
model	Get the model
model_config	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
moneyness_weight	The weight for penalize options with moneyness as it moves away from 0
options	The options to calibrate
pricer	The OptionPricer for the model
ref_date	Get the reference date
ttm_weight	The weight for penalize options with ttm as it approaches 0
vol_surface	The VolSurface to calibrate the model with

cost_function(params: ndarray) → float

Calculate the cost function from the model prices

cost_weight(ttm: float, moneyness: float) → float

Calculate the weight for the cost function for a given time to maturity and moneyness

fit() → OptimizeResult

Fit the model

get_bounds() → Bounds | None

Get the parameter bounds for the calibration

get_constraints() → Sequence[dict[str, Any]] | None

Get the constraints for the calibration

abstract get_params() → ndarray

Get the parameters of the model

Must be implemented by the subclass

implied_vol_range() → Bounds

Get the range of implied volatilities

model_post_init(_ctx: Any) → None

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

penalize() → float

Penalize the cost function

plot(index: int = 0, *, max_moneyness_ttm: float | None = 1.0, support: int = 51, **kwargs: Any) → Any

Plot the implied volatility for market and model prices

abstract set_params(params: ndarray) → None

Set the parameters of the model

Must be implemented by the subclass

minimize_method: str | None

The optimization method to use - if None, the default is used

property model: M

Get the model

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

moneyness_weight: float

The weight for penalize options with moneyness as it moves away from 0

The weight is applied as exp(-moneyness_weight * moneyness), therefore a value of 0 won’t penalize moneyness at all

options: dict[ModelCalibrationEntryKey, OptionEntry]

The options to calibrate

pricer: OptionPricer[M]

The OptionPricer for the model

property ref_date: datetime

Get the reference date

ttm_weight: float

The weight for penalize options with ttm as it approaches 0

The weight is applied as 1 - ttm_weight*exp(-ttm), therefore a value of 0 won’t penalize ttm at all, a value of 1 will penalize options with ttm->0 the most

vol_surface: VolSurface[Any]

The VolSurface to calibrate the model with

class quantflow.options.calibration.HestonCalibration(*, pricer: ~quantflow.options.pricer.OptionPricer[Heston], vol_surface: ~quantflow.options.surface.VolSurface[~typing.Any], minimize_method: str | None = None, moneyness_weight: ~typing.Annotated[float, ~annotated_types.Ge(ge=0)] = 0.0, ttm_weight: ~typing.Annotated[float, ~annotated_types.Ge(ge=0.0), ~annotated_types.Le(le=1.0)] = 0.0, options: dict[~quantflow.options.calibration.ModelCalibrationEntryKey, ~quantflow.options.calibration.OptionEntry] = <factory>, feller_penalize: float = 0.0)

A VolModelCalibration for the Heston stochastic volatility model

Methods:

get_bounds	Get the parameter bounds for the calibration
get_params	Get the parameters of the model
penalize	Penalize the cost function
set_params	Set the parameters of the model

Attributes:

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

get_bounds() → Sequence[Bounds] | None

Get the parameter bounds for the calibration

get_params() → ndarray

Get the parameters of the model

Must be implemented by the subclass

penalize() → float

Penalize the cost function

set_params(params: ndarray) → None

Set the parameters of the model

Must be implemented by the subclass

minimize_method: str | None

The optimization method to use - if None, the default is used

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

moneyness_weight: float

The weight for penalize options with moneyness as it moves away from 0

The weight is applied as exp(-moneyness_weight * moneyness), therefore a value of 0 won’t penalize moneyness at all

options: dict[ModelCalibrationEntryKey, OptionEntry]

The options to calibrate

pricer: OptionPricer[M]

The OptionPricer for the model

ttm_weight: float

The weight for penalize options with ttm as it approaches 0

The weight is applied as 1 - ttm_weight*exp(-ttm), therefore a value of 0 won’t penalize ttm at all, a value of 1 will penalize options with ttm->0 the most

vol_surface: VolSurface[Any]

The VolSurface to calibrate the model with