Apr

11

In the last few days one of the economic talking heads commented on how he has "not seen volatility like this since" sometime in the past. I forget whether the former time was 1998 or 2008, but it doesn't matter, as there are many periods in the past with greater volatility.

My quick look at past volatility consists solely of looking at the height and duration of VIX in earlier periods. I took the standard measure (VIX) because of its relatively universal acceptance. I could use some of my own measures, but not without the risk of being flamed for subjectivity, despite the fact that they compare with VIX on a relative basis.

Question: Is there something I am missing? Is there some measure of vol that I am unaware of? Could the high volatility simply refer to the gentleman's equity balance? Could this simply be an effort to gain a headline, i.e. fake news? Any thoughts?

Gibbons Burke writes: 

The VIX seems skewed to being more sensitive to downside volatility and not so much to upside volatility, and it is based on one instrument: the S&P 500 index calls and puts and their ability to speak to the volatility of the underlying index.

The standard Historical volatility calculation of the same underlying instrument used as the input for option pricing models is somewhat more flexible in that it can be applied to any instrument since all it requires is daily closing prices, and the S&P 500 retroactively before the VIX was created.

The two measures, VIX and SPX historical volatility correlate closely—and most interesting is when they depart from that correlation, which shows that the options market is anticipating something which has not shown up in the movement of the underlying. You know all this of course, and have developed some very interesting work on options and their open interest already as it relates to the underlying, no?

In technical analysis realms, average range, and Wells Wilder's Average True Range (which considers the previous day's close as part of the day's range if it is above or below the high or low of the day, which captures post-close volatility and gap moves) has been used as a volatility measure for input into risk allocation components in trading systems, and as breakout bands for trading systems like one made famous by Larry Williams and others like Steve Notis.

A newer volatility measure which came out of chaos theory ideas when they became popular measures the total range (or true range) over some n-period window of previous market activity, and measures the sum of all the individual period ranges (or true ranges) as a ratio. Two instances of this volatility measure are Adam White's VHF index (vertical-horizontal f-something) and CTA Ed Dreiss' Choppiness Index. Both are solid conceptually, easy to calculate, and are already implemented in many systems.

anonymous writes: 

For the S&P, here is the mean daily High-Low range as a % of the Open, for each year since 1962:

year  /  mean daily H-L as % of Open

2018 -  1.44%
2017 -  0.51%
2016 -  0.95%
2015 -  1.10%
2014 -  0.86%
2013 -  0.85%
2012 -  1.06%
2011 -  1.62%
2010 -  1.36%
2009 -  2.00%
2008 -  2.74%
2007 -  1.17%
2006 -  0.85%
2005 -  0.88%
2004 -  0.95%
2003 -  1.41%
2002 -  2.08%
2001 -  1.75%
2000 -  1.84%
1999 -  1.54%
1998 -  1.58%
1997 -  1.42%
1996 -  1.01%
1995 -  0.72%
1994 -  0.82%
1993 -  0.71%
1992 -  0.82%
1991 -  1.11%
1990 -  1.31%
1989 -  0.95%
1988 -  1.22%
1987 -  1.77%
1986 -  1.12%
1985 -  0.79%
1984 -  1.00%
1983 -  1.01%
1982 -  1.60%
1981 -  2.03%
1980 -  2.21%
1979 -  1.55%
1978 -  1.60%
1977 -  1.37%
1976 -  1.60%
1975 -  2.16%
1974 -  2.58%
1973 -  2.06%
1972 -  1.53%
1971 -  1.54%
1970 -  2.09%
1969 -  1.74%
1968 -  1.78%
1967 -  1.62%
1966 -  1.77%
1965 -  1.26%
1964 -  1.16%
1963 -  1.26%
1962 -  1.73%

Sushil Kedia writes:

​VIX measures the price of volatility all are wagering on. Price is the weighted mean/vector sum of all individual values of volatility the various have for themselves. 
Combining a few well accepted ideas, here & everywhere else: 
Depending on where one is in the market food chain there are different versions of what is noise and what is tradeable information content. 
So a simple and effective & consistent to calculate the value of volatility for oneself is to objectively write down what is the minimum movement size below which you dont act. For a HFT robot it could be every tick & for "markets cannot be timed behemoths collecting only other people's money, a.k.a. long only passive funds" it could be 5%. Whatever it be define your sensitivity and lets call it your sensitivity unit move. 
Then each occurence of a move of a unit size is counted — as in counting by toes or a computer programme over any observed length of data. Count the absolute vaues of the Unit sensitivity. Divide the net change over the same length of data with the sum of absolute values of unit sensitivities observed. 
A straight line move would thus give you zero volatility or noise and a perfectly tradeable information content. If however over the observed length of data, on the other hand, net change is zero then there is only noise. 
I remember, many years ago Bill & few others had discussed here how Point & Figure method from the university of mumbo jumbo is an approach that is very similar to this thinking and a fantastic way to separate signal and noise relevant to each as per their forebearance within the food chain. 

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