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Nasdaq-100 Components Realized Volatility Streamgraph

Emerging Markets Realized Volatility Streamgraph

International Developed Markets Realized Volatility Streamgraph

US Equity Sectors Realized Volatility Streamgraph

XLU Components Realized Volatility Streamgraph

XLP Components Realized Volatility Streamgraph

The datasets from which these PYPL forecasts are drawn originate from FactSet. They represent the aggregated estimates made available to academics or practitioners via the Institutional Brokers’ Estimate System (IBES). Although this seems like a fair way of predicting future profits given that they have some level expertise in investment banking, studies show there's still an optimism bias present among these professionals.

Regression-based models suffer from the use of past earnings in a linear or exponential framework. This can lead to bias because these models assume that future performance will mirror historical trends exactly, whereas business cycle dynamics and seasonality may introduce randomness over time periods.

While there is a clear consensus that a factor-based approach to investment is rewarded over time, it goes without saying that the implementation of factor investing strategies, especially in the world of long-only money-management, is rarely subject to the same consensus. Index providers who offer funds that generally contain a small number of stocks in relation to the size and risk level they are designed for, often do so by selecting certain conditions or factors within each company.

For example, some commercial indexes aim at proportionality between price movements and dividends paid out over time while others look exclusively on liquidity considerations alone; yet still more restrict their selection criteria based around corporate governance issues like transparency reports rating various aspects such as soundness levels among others relevant metrics available about any given firm when deciding whether it should be included into an investor’s portfolio.

Commodities Realized Volatility Streamgraph

Components

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 DBC.

Commodities Realized Volatility

Component	Last Value
BAL	22.44682
CORN	24.57560
COW	21.15962
GLD	13.46702
JJC	23.91142
JJG	22.83275
JJN	30.17755
JJU	27.31660
JO	23.72540
PALL	47.60521
SGG	18.02965
SLV	24.63904
SLX	39.57886
SOYB	23.98948
UNG	73.50311
USO	30.15284
WEAT	35.86362
WOOD	26.93375

All Realized Volatility Visualizations

Component	1 Month Ago	1 Week Ago	Last Value
BAL	22.44682	23.26551	20.88189
CORN	24.57560	21.61132	20.84265
COW	21.15962	19.52285	17.27174
GLD	13.46702	11.40125	11.45020
JJC	23.91142	23.04344	19.55042
JJG	22.83275	20.37151	16.71573
JJN	30.17755	31.48853	25.79948
JJU	27.31660	25.56842	19.65682
JO	23.72540	25.20720	28.66052
PALL	47.60521	39.92112	38.45596
SGG	18.02965	19.10125	15.46619
SLV	24.63904	22.38632	19.73645
SLX	39.57886	39.68853	27.82531
SOYB	23.98948	20.89265	21.41148
UNG	73.50311	81.49297	70.38870
USO	30.15284	27.49217	30.37263
WEAT	35.86362	33.46330	28.94956
WOOD	26.93375	27.81454	20.60739

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.

How Realized Volatilty is Measured

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.

How to Read the Streamgraph

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 Invesco DB Commodity Index Tracking Fund. 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 Invesco DB Commodity Index Tracking Fund.

About the Streamgraph Coordinates

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 Invesco DB Commodity Index Tracking Fund.

Invesco DB Commodity Index Tracking Fund

DBC seeks to track changes in the level of DBIQ Optimum Yield's Commodity Index. The fund's portfolio includes: exchange-traded futures on Light Sweet Crude Oil (WTI), Heating Oil, RBOB Gasoline Natural Gas Brent Crude Oil; Gold , Silver, Aluminum, Zinc, Copper (Grade A), Corn, Wheat, Soybeans and Sugar.

All Realized Volatility Visualizations

Realized Volatility

This chart shows the component realized volatility of DBCover the past year of trading.

Components

Historical price and realized volatility data

AAPL

Guest Commentary

Robson Chow is a hedge fund manager

Thank you for reading... and hope you are enjoying the holidays. The full post can be found here https://t.co/uLKVnqB5ZS
— QuartersPower (@HalfersPower) December 30, 2021

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