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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.

XLK Components 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 XLK.

Components Realized Volatility

Component	Last Value
AAPL	38.77976
ACN	46.42313
ADBE	56.85513
ADI	58.80251
ADP	40.43938
AMAT	72.16706
AMD	94.43710
AVGO	55.69893
CRM	59.85852
CSCO	33.05378
FIS	52.75120
IBM	35.14209
INTC	42.43882
INTU	57.61953
LRCX	78.59868
MA	51.63879
MSFT	45.58899
MU	70.01754
NOW	75.04366
NVDA	90.66829
ORCL	34.11936
PYPL	79.26366
QCOM	72.25404
TXN	51.92491
V	41.64376

All Realized Volatility Visualizations

Component	1 Month Ago	1 Week Ago	Last Value
AAPL	38.77976	38.63915	30.15665
ACN	46.42313	45.73907	38.27502
ADBE	56.85513	53.30568	43.12488
ADI	58.80251	52.87430	34.41449
ADP	40.43938	40.35793	25.41425
AMAT	72.16706	81.04700	69.69853
AMD	94.43710	84.25646	68.55535
AVGO	55.69893	55.14872	40.82248
CRM	59.85852	58.78609	44.13940
CSCO	33.05378	31.59577	24.34058
FIS	52.75120	47.86960	39.78401
IBM	35.14209	35.48488	28.10434
INTC	42.43882	41.80179	32.49699
INTU	57.61953	56.36512	44.04553
LRCX	78.59868	80.70474	61.16246
MA	51.63879	51.16286	38.24134
MSFT	45.58899	48.63842	36.11950
MU	70.01754	63.63834	47.97591
NOW	75.04366	84.14026	67.55908
NVDA	90.66829	84.33221	66.47064
ORCL	34.11936	32.46968	34.94012
PYPL	79.26366	79.64412	47.70435
QCOM	72.25404	70.64576	47.45050
TXN	51.92491	49.38162	30.44168
V	41.64376	41.93227	28.68851

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 Volatility 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 Technology Select Sector SPDR 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 Technology Select Sector SPDR 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 Technology Select Sector SPDR Fund.

Technology Select Sector SPDR Fund

XLK is an exchange traded fund whose portfolio is comprised of US technology sector stocks. This investment seeks to provide the same performance as stocks in the Technology Select Sector Index. The fund typically invests at least 95% of its assets in the securities that comprise this index.

All Realized Volatility Visualizations

Realized Volatility

This chart shows the component realized volatility of XLKover 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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