post list
QuantDare
categories
asset management

“Past performance is no guarantee of future results”, but helps a bit

ogonzalez

asset management

Playing with Prophet on Financial Time Series (Again)

rcobo

asset management

Shift or Stick? Should we really ‘sell in May’?

jsanchezalmaraz

asset management

What to expect when you are the SPX

mrivera

asset management

K-Means in investment solutions: fact or fiction

T. Fuertes

asset management

How to… use bootstrapping in Portfolio Management

psanchezcri

asset management

Playing with Prophet on Financial Time Series

rcobo

asset management

Dual Momentum Analysis

J. González

asset management

Random forest: many is better than one

xristica

asset management

Using Multidimensional Scaling on financial time series

rcobo

asset management

Comparing ETF Sector Exposure Using Chord Diagrams

rcobo

asset management

Euro Stoxx Strategy with Machine Learning

fjrodriguez2

asset management

Hierarchical clustering, using it to invest

T. Fuertes

asset management

Lasso applied in Portfolio Management

psanchezcri

asset management

Markov Switching Regimes say… bear or bullish?

mplanaslasa

asset management

Exploring Extreme Asset Returns

rcobo

asset management

Playing around with future contracts

J. González

asset management

BETA: Upside Downside

j3

asset management

Approach to Dividend Adjustment Factor Calculation

J. González

asset management

Are Low-Volatility Stocks Expensive?

jsanchezalmaraz

asset management

Predict returns using historical patterns

fjrodriguez2

asset management

Dream team: Combining classifiers

xristica

asset management

Stock classification with ISOMAP

j3

asset management

Could the Stochastic Oscillator be a good way to earn money?

T. Fuertes

asset management

Correlation and Cointegration

j3

asset management

Momentum premium factor (II): Dual momentum

J. González

asset management

Dynamic Markowitz Efficient Frontier

plopezcasado

asset management

‘Sell in May and go away’…

jsanchezalmaraz

asset management

S&P 500 y Relative Strength Index II

Tech

asset management

Performance and correlated assets

T. Fuertes

asset management

Reproducing the S&P500 by clustering

fuzzyperson

asset management

Size Effect Anomaly

T. Fuertes

asset management

Predicting Gold using Currencies

libesa

asset management

Inverse ETFs versus short selling: a misleading equivalence

J. González

asset management

S&P 500 y Relative Strength Index

Tech

asset management

Seasonality systems

J. González

asset management

Una aproximación Risk Parity

mplanaslasa

asset management

Using Decomposition to Improve Time Series Prediction

libesa

asset management

Las cadenas de Markov

j3

asset management

Momentum premium factor sobre S&P 500

J. González

asset management

Fractales y series financieras II

Tech

asset management

El gestor vago o inteligente…

jsanchezalmaraz

asset management

¿Por qué usar rendimientos logarítmicos?

jsanchezalmaraz

asset management

Fuzzy Logic

fuzzyperson

asset management

El filtro de Kalman

mplanaslasa

asset management

Fractales y series financieras

Tech

asset management

Volatility of volatility. A new premium factor?

4
Volatility of volatility. A new premium factor?

Empirical research has shown that return premium may be captured in equity markets if we base our investments on certain factors, included in which are: equity size, value or momentum. The nowadays-popular low-volatility (low-beta) factor may also be include. This (to a certain point) is the basis of this post.

Following this factor approach, we are going to test a new factor: the volatility of volatility. The use of the volatility of the implied volatility in the option market is well known. However, what I would like to test here is not related with the option market. My interest is in the premium we may achieve, when focusing on the stocks with the lowest volatility of volatility.

There are studies (Baltusen, Bekkum, Grient, 2013) suggesting that the volatility of a stock option’s implied volatility, can be seen as a way to measure the uncertainty of the stock’s future returns: the higher the uncertainty, the less the future returns.

 

Testing the theory

In this post, I am going to capture this idea by testing a universe made up of the S&P 500 components from 1/1/2007 to 31/12/2013, using exclusively the index components of each moment, to avoid any potential survivorship bias.

My tests calculate the volatility of the historical volatility of the daily returns, with 6 months rolling windows. From this point on, the volatility of volatility will be referred to as VoV.

There is no clear pattern if we analyse what happens when selecting weekly deciles from the ranking on VoV. However, we see that the first two deciles have sharpe ratios higher than the ones of the upper deciles.
Sharpe ratio VoV

Likewise, we can see that the maximum draw down grows as we move to the upper deciles.

MDD VoVThese statistics resemble those seen in the studies related with the low volatility anomaly. The same type of graphs based on the simple volatility ranking can be seen below.

Sharpe Ratio from volatility rankings

MDD Volatility

Can this resemblance be attributed to a very similar ranking in volatility and VoV? In the following chart, we can see that the stocks in the first decile of the VoV fall greatly in the first decile of the volatility ranking. In addition, the first decile VoV is also distributed mostly in the first 4-5 volatility deciles.

Ordenacion del primer decil volvol1

The method I used to calculate the VoV explains this result: the calculus of the VoV have not been normalised. For example, dividing by the mean VoV makes it probable that the stocks with lower VoV correspond to some extent with the stocks with lower volatility. Thus,  there’s a high coincidence between the first deciles of the low VoV and the low volatility strategies.

Then, if VoV is apparently taking advantage of the low volatility anomaly, why should we base our strategy in the volatility of volatily? Does VoV contribute anything?

 

Precedence in Volatility Strategies

Previous history shows that low volatility strategies can fall behind with their references under very bullish markets. On the other hand, low volatility strategies show very good behavior in bearish situations. Can we expect this same behavior in a Low VoV strategy?

In the following picture of annual returns, Low VoV clearly surpasses Low Volatility strategy in 2009 and 2013, which are very bullish years. However, during the terrible draw down of 2008, low Volatility beats both the market and the low VoV portfolio (although low VoV also achieves a great spread with the market).

Annual performance

The table of statistics presents very similar annualized returns for both strategies, with lower maximum draw down and volatility in Low Volatility, but higher maximum rolling run up for low VoV.

results abs

 * 1/1/2007 – 31/12/2013

In terms of 1 year rolling spreads with the S&P 500 Index, low VoV is higher above the index and keeps spreads less extreme than the ones of the Low Vol strategy.

results rel

In Summary

The results displayed in this post suggest that a low volatility of volatility strategy could be an alternative to the Low Volatility strategies, keeping the pros of the former and improving the results in “boosting” markets.

To obtain more reliable conclusions, we would need to lengthen the period and to broaden the markets of study.

Tweet about this on TwitterShare on LinkedInShare on FacebookShare on Google+Email this to someone

add a comment

Interesting but the period analyzed is way to short. S&P has history since beginning of 19th century, why not using longer period for the analysis?

Porlones, you are right, it would be required to extend the period, as I say at the end of the post, but at the moment I wrote the post I didn’t have all the historical price series of the S&P 500 components beyond 2007. I consider very important to work with all the compoments of the index at each moment in order to be accurate, otherwise we could be ruling out stocks that might have been allocated if we had them in our stock universe. That’s why I decided to test both strategies only from the point I had enough… Read more »

A well written post with an enlightening conclusion. The results look promising although I am unsure of exactly how the two strategies are implemented (Low Vol and Vov). Could you explain how they work? Thank you!

The way I have implemented the strategies has been quite simple: Every week both strategies select the stocks in the first decile (50 stocks) according to their corresponding ranking (volatility or volatility of volatility) giving them equal weight. I think this implementation is enough for the purpose of the post, but of course there are other possibilities.

wpDiscuz