Volatility Metrics

Volatility metrics measure the stability and smoothness of the index. We will demonstrate how to calculate and visualize volatility metrics.

Basic Setup

First, we will import the necessary modules, load some sales data, and create an index:

>>> from hpipy.datasets import load_seattle_sales
>>> from hpipy.price_index import RepeatTransactionIndex

>>> df = load_seattle_sales()

>>> hpi = RepeatTransactionIndex.create_index(
...     trans_data=df,
...     prop_id="pinx",
...     trans_id="sale_id",
...     price="sale_price",
...     date="sale_date",
...     periodicity="M",
...     estimator="robust",
...     log_dep=True,
...     smooth=True,
... )

Calculating Volatility

To calculate the volatility of the index, you can use the volatility function:

>>> from hpipy.utils.metrics import volatility
>>> from hpipy.utils.plotting import plot_index_volatility

>>> vol = volatility(hpi)
>>> vol.round(5).head()
      roll     mean   median
1  0.02474  0.01721  0.01652
2  0.02751  0.01721  0.01652
3  0.02336  0.01721  0.01652
4  0.01585  0.01721  0.01652
5  0.00476  0.01721  0.01652

>>> plot_index_volatility(vol).properties(title="Volatility Metrics")
alt.LayerChart(...)

Rolling Window Analysis

You can also analyze volatility over different time windows:

>>> rolling_vol = volatility(hpi, window=12)
>>> rolling_vol.round(5).head()
       roll     mean   median
6   0.01655  0.01799  0.01739
7   0.02159  0.01799  0.01739
8   0.02078  0.01799  0.01739
9   0.01927  0.01799  0.01739
10  0.01837  0.01799  0.01739

The window parameter specifies the number of periods to use for the rolling calculation.