US Equity Sectors Realized Volatility Streamgraph
Historical price and realized volatility data
Seasonality
This multi-factor forecast for Paypal Holdings (PYPL) is based on a weighted average of five factor-dervied forecasts.
Backtest PYPL Left-hand side y-axis orders liquidity rank of individuals chart components.
Right-hand side y-axis coordinates measure the price of SPY.
Components Realized Volatility
| Component |
Last Value |
| XLB |
30.50894 |
| XLC |
36.55132 |
| XLE |
43.43998 |
| XLF |
31.89355 |
| XLI |
29.15365 |
| XLK |
41.87575 |
| XLP |
20.14011 |
| XLU |
25.97423 |
| XLV |
24.01097 |
| XLY |
44.21922 |
| Component |
1 Month Ago |
1 Week Ago |
Last Value |
| XLB |
30.50894 |
29.26093 |
19.36034 |
| XLC |
36.55132 |
35.91835 |
21.95847 |
| XLE |
43.43998 |
43.18834 |
32.57439 |
| XLF |
31.89355 |
31.32191 |
22.18040 |
| XLI |
29.15365 |
27.63456 |
18.03313 |
| XLK |
41.87575 |
41.36464 |
28.97508 |
| XLP |
20.14011 |
19.16441 |
13.32872 |
| XLU |
25.97423 |
24.38045 |
17.79055 |
| XLV |
24.01097 |
23.26911 |
17.28124 |
| XLY |
44.21922 |
43.08101 |
28.14984 |
About Realized Volatility
Realized volatility (as derived from the square root of variance) is a measurement of the standard deviation of returns of an asset over a given time period, typically annualized.
Realized volatility can be measured many ways. The classical way of calculating realized volatility is by taking the log returns of close to close prices.
Per Euan Sinclair, “there is no uncertainty due to measurement. But there is uncertainty over whether the measure is truly representative of the underlying reality.”
The
streamgraph visualization above displays realized volatility over the previous 21 days by applying the
Yang-Zhang method of calculating realized volatility. This measurement utilizes more data points than the typical close-to-close estimator, which results in a measurement that is considered more accurate.
We measure realized volatility for each component of a particular index or ETFs in order to help understand volatility dynamics, and anomalies underneath the surface.
The streamgraph is a data visualization that enables the representation of many timeseries in an efficient manner. The
Tradewell realized volatility streamgraph shows the change in realized volatility through time across multiple datasets, displaced around a central axis (the 0-line).
The streamgraph highlights three main attributes of realized volatility:
1. The overall level of realized volatility at the index or etf level relative to history.
If you notice the streamgraph expanding and then contracting, that behavior is representative of individual component volatility expanding and contracting. The widest part of the streamgraph represents the period with the most volatility across components, while the narrowest part of the streamgraph represents the period with the least volatility across components.
2. Anomalies in individual components realized through time.
When companies have large moves, ie volatility increases significantly due to earnings, unexpected events or otherwise, the streamgraph will immediately highlight those anomalies visually — the width of an individual securities contribution to the streamgraph will widen considerably and the individual component ticker will be displayed on the streamgraph on the Date where realized volatility was highest for S&P 500. As volatility clusters, it is common to see the width persist after an anomalous move in a particular security.
3. The level of realized volatility of individual components relative to other components that comprise S&P 500.
The X-axis displays trading days by date, and the Y-axis contains the component realized volatility. The absolute distance between each line on the chart is the 21-day realized volatility for S&P 500.
The S&P 500 is an index that follows the movement of 500 large companies from stock exchanges in the United States. It is the most referenced benchmark for US equities, and a common indicator of economic health in the United States.
