Dynamics

Dynamics.

class osc_physrisk_financial.dynamics.ConstantGrowth(growth_rate: float, value0: float, name: str | None = None)

Bases: Dynamic

Class representing a constant growth model: \(V_t = V_0 \\times (1 + \mu)^t.\).

Parameters:

• growth_rate (float) – Constant growth rate \(\mu.\)

• name (string, optional) – Name for identification.

• value0 (float) – \(V_0\) in [Methodology]

Examples

>>> cg = ConstantGrowth(growth_rate=0.02, name='RealAsset')

References

Methodology, Chapter 4 of Methodology survey (Overleaf).

compute_value(dates: DatetimeIndex | list)

Compute the asset value at future dates.

dates

Dates for which the value wants to be computed. Note that in this model we are only interested in the years, so we only extract that part. The initial date is also included here ( \(t_{0}\) such that \(V_{t_0} = V_0\) of [Methodology].

Type:

pandas.DatetimeIndex, list of strings, pandas.Timestamp, or string

Returns:

\(V_t\) in [Methodology] for the different dates. It includes the value \(V_0\). Note that the dates have been sorted and the output is returned with the dates sorted.

Return type:

np.ndarray

References

Methodology, Chapter 4 of Methodology survey (Overleaf).

class osc_physrisk_financial.dynamics.Dynamic(name: str | None = None)

Bases: ABC

A base class for simulating asset value dynamics.

Notes

This base class is based on Underlying from pypricing.

abstract compute_value(dates: DatetimeIndex | list)

Abstract method for computing the asset value at future dates.

dates

Future dates for which the asset value wants to be computed.

Type:

pandas.DatetimeIndex, list of strings, pandas.Timestamp, or string

Notes

This base class is based on Underlying from pypricing.