Volatility Attribution¶

perfana.monte_carlo.risk.vol_attr(cov_or_data, weights, freq)[source]¶

Derives the volatility attribution given a data cube and weights.

Notes

The return values are defined as follows:

marginal: The absolute marginal contribution of the asset class towards the portfolio volatility. It is essentially the percentage attribution multiplied by the portfolio volatility.
percentage: The percentage contribution of the asset class towards the portfolio volatility. This number though named in percentage is actually in decimals. Thus 0.01 represents a 1% contribution.

Parameters

cov_or_data (ndarray) – Covariance matrix or simulated returns data cube.
weights (Union[Iterable[Union[int, float]], ndarray, Series]) – Weights of the portfolio. This must be 1 dimensional and must match the dimension of the data’s last axis.
freq (Union[str, int]) – Frequency of the data. Can either be a string (‘week’, ‘month’, ‘quarter’, ‘semi-annual’, ‘year’) or an integer specifying the number of units per year. Week: 52, Month: 12, Quarter: 4, Semi-annual: 6, Year: 1.

Returns

A named tuple of relative and absolute volatility attribution respectively. The absolute attribution is the volatility of the simulated data over time multiplied by the percentage attribution.

Return type

Attribution

Examples

>>> from perfana.datasets import load_cube
>>> from perfana.monte_carlo import vol_attr
>>> data = load_cube()[..., :3]  # first 3 asset classes only
>>> weights = [0.33, 0.34, 0.33]
>>> freq = 'quarterly'
>>> attr = vol_attr(data, weights, freq)
>>> attr.marginal.round(4)
array([0.2352, 0.5006, 0.2643])
>>> attr.percentage.round(4)
array([0.0445, 0.0947, 0.05  ])
>>> attr.marginal is attr[0]
True
>>> attr.percentage is attr[1]
True