Param ct

CT parameter class.

CentTendParam

Bases: GenericParam

Parameter storage for CT model.

Attributes:

Name	Type	Description
riskfree	float	Risk-free rate of return
mean_v	float	Mean of the volatility process
kappa_s	float	Mean reversion speed of volatility
kappa_y	float	Mean reversion speed of central tendency
eta_s	float	Instantaneous standard deviation of volatility
eta_y	float	Instantaneous standard deviation of central tendency
lmbd	float	Equity risk premium
rho	float	Correlation
measure	Measure	Under which measure (P or Q)

__init__(*, riskfree=0.0, lmbd=0.1, lmbd_s=0.0, lmbd_y=0.0, mean_v=0.5, kappa_s=1.5, kappa_y=0.5, eta_s=0.1, eta_y=0.01, rho=-0.5, measure=Measure.P)

Initialize class.

Parameters:

Name	Type	Description	Default
riskfree	float	Risk-free rate of return	0.0
mean_v	float	Mean of the volatility process	0.5
kappa_s	float	Mean reversion speed of volatility	1.5
kappa_y	float	Mean reversion speed of central tendency	0.5
eta_s	float	Instantaneous standard deviation of volatility	0.1
eta_y	float	Instantaneous standard deviation of central tendency	0.01
lmbd	float	Equity risk price	0.1
lmbd_s	float	Volatility risk price	0.0
lmbd_y	float	Central tendency risk price	0.0
rho	float	Correlation	-0.5
measure	Measure	Under which measure: - Measure.P : physical measure - Measure.Q : risk-neutral	P

convert_to_q()

Convert parameters to risk-neutral version.

feller()

Check Feller condition.

Returns:

Type	Description
bool	True for valid parameters, False for invalid

from_theta(*, theta, measure=Measure.P) classmethod

Initialize parameters from parameter vector.

Parameters:

Name	Type	Description	Default
theta	(nparams, ) array	Parameter vector	required
measure	Measure		P

get_bounds(*, subset=Subset.all, measure=Measure.PQ)

Bounds on parameters.

Parameters:

Name	Type	Description	Default
subset	Subset	Which parameters to update Belongs to - 'all' : all parameters, including those related to returns - 'vol' : only those related to volatility	all
measure	Measure	Under which measure: - 'P' : physical measure - 'Q' : risk-neutral - 'PQ' : both	PQ

Returns:

Type	Description
sequence of (min, max) tuples

get_constraints()

Get parameter constraints.

Returns:

Type	Description
dict or sequence of dict	Equality and inequality constraints. See scipy.optimize.minimize

get_model_name() staticmethod

Return model name.

Returns:

Type	Description
str	Parameter vector

get_names(*, subset=Subset.all, measure=Measure.PQ) staticmethod

Return parameter names.

Parameters:

Name	Type	Description	Default
subset	Subset	Which parameters to return. Belongs to - 'all' : all parameters, including those related to returns - 'vol' : only those related to volatility	all
measure	Measure	Under which measure: - 'P' : physical measure - 'Q' : risk-neutral - 'PQ' : both	PQ

Returns:

Type	Description
list of str	Parameter names

get_theta(*, subset=Subset.all, measure=Measure.PQ)

Return vector of model parameters.

Parameters:

Name	Type	Description	Default
subset	Subset	Which parameters to return Belongs to - 'all' : all parameters, including those related to returns - 'vol' : only those related to volatility	all
measure	Measure	Under which measure: - 'P' : physical measure - 'Q' : risk-neutral - 'PQ' : both	PQ

Returns:

Type	Description
(nparams, ) array	Parameter vector

is_valid()

Check validity of parameters.

Returns:

Type	Description
bool	True for valid parameters, False for invalid

update(*, theta, subset=Subset.all, measure=Measure.PQ)

Update attributes from parameter vector.

Parameters:

Name	Type	Description	Default
theta	(nparams, ) array	Parameter vector	required
subset	Subset	Which parameters to update Belongs to - 'all' : all parameters, including those related to returns - 'vol' : only those related to volatility	all
measure	Measure	Under which measure: - 'P' : physical measure - 'Q' : risk-neutral - 'PQ' : both	PQ

update_ajd()

Update AJD representation.