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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.

Emerging Markets 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 EEM.

Components Realized Volatility

Component	Last Value
AGT	21.96147
ECH	39.52756
EIDO	16.08370
EPHE	16.08971
EPOL	28.40218
EPU	35.77770
ERUS	45.20100
EWM	12.38285
EWT	22.25362
EWW	29.00662
EWY	22.55735
EWZ	26.94492
EZA	29.84813
FXI	28.01354
ICOL	18.43018
INDA	18.68762
KSA	14.43165
KWT	10.51393
QAT	18.89931
THD	15.53786
TUR	37.77481
UAE	19.49211

All Realized Volatility Visualizations

Component	1 Month Ago	1 Week Ago	Last Value
AGT	21.96147	24.44867	21.72157
ECH	39.52756	39.17884	37.36596
EIDO	16.08370	15.98853	11.70157
EPHE	16.08971	14.82744	14.43249
EPOL	28.40218	25.39397	19.18546
EPU	35.77770	36.20812	27.24215
ERUS	45.20100	41.93408	34.67010
EWM	12.38285	12.73732	10.97713
EWT	22.25362	20.87818	12.12180
EWW	29.00662	26.79824	17.59920
EWY	22.55735	22.95051	16.51427
EWZ	26.94492	29.65864	28.47149
EZA	29.84813	30.68247	24.03887
FXI	28.01354	29.12985	26.99051
ICOL	18.43018	21.82083	21.67249
INDA	18.68762	16.64305	11.62214
KSA	14.43165	13.94615	11.86487
KWT	10.51393	11.12984	8.45529
QAT	18.89931	14.84311	12.10198
THD	15.53786	15.41431	13.39449
TUR	37.77481	41.57841	131.64655
UAE	19.49211	18.26369	21.59998

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 iShares MSCI Emerging Markets ETF. 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 iShares MSCI Emerging Markets ETF.

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 iShares MSCI Emerging Markets ETF.

iShares MSCI Emerging Markets ETF

iShares MSCI Emerging Markets ETF captures large and mid cap representation across 25 emerging market countries. The index covers 85% of the free float-adjusted market capitalization in each country.

All Realized Volatility Visualizations

Realized Volatility

This chart shows the component realized volatility of EEMover 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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