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One commonly used sample estimator of volatility is the standard deviation of the log returns.

It is indeed a very good estimator (unbiased, ...) when the sample is large.

But I don't like it for small sample as it tends to overweight outliers in log returns.

Do you know if any other statistical dispersion measure that can be use to estimate the volatility of a stock? (I don't care about statistical properties; I just want it to estimate differently / better the daily risk of this stock.)

PS: I have already tried to use the norm 1 instead of the Euclidean norm. Any other idea / remark?

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en.wikipedia.org/wiki/Median_absolute_deviation seams interesting. – RockScience Mar 31 '11 at 8:13
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anyone has used the concept of entropy for financial time series? – RockScience Mar 31 '11 at 8:21
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"As we all know, the best known sample estimator of volatility is the standard deviation of the log returns.". Really? Evidence? – Shane Mar 31 '11 at 10:02
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Concerning entropy: This seems to be an interesting question in its own right - why don't you ask it as a separate question?! – vonjd Mar 31 '11 at 10:57
@Shane: Agreed! – RockScience Apr 1 '11 at 1:51

3 Answers

up vote 5 down vote accepted

You could use something like the interquartile or semi-interquartile range, which is somewhat more insensitive to extremes. This is a better measure to use if your data is skewed, however if your data is normally distributed it is still better to use the standard deviation.

http://en.wikipedia.org/wiki/Interquartile_range

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if which data is skewed? The sample of the entire population? – RockScience Apr 1 '11 at 2:07
I am referring to the sample data – Ralph Winters Apr 1 '11 at 2:29
It is an interesting point, because a small sample is skewed by nature – RockScience Apr 1 '11 at 2:53
up vote 7 down vote

You mention "daily" risk, so I'm assuming you're looking at a daily frequency. Yang-Zhang Volatility (Drift-independent Volatility Estimation Based on High, Low, Open and Close Prices) fits the bill for what you're asking, it takes into account intraday fluctuations as well.

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I guess it is anyway applicable for intraday or monthly data if you have a OHLC time serie. – RockScience Apr 1 '11 at 1:53
It will indeed dilute the effect of an outlier as we use more data than the close to close returns. But still we may have this "hat shape" of length the number of data in your rolling Yang-Zhang vol – RockScience Apr 1 '11 at 1:57
up vote 2 down vote

In order to suppress the effect of outliers, you can use indicator transform or rank transform. Once you convert your data in those form then you can find the volatility. Personally, I like indicator transform as it is easy, more popular in many applications and gives good results.

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"more popular in many applications and gives good results." Do you have a reference for this? – RockScience Apr 1 '11 at 1:58
I am speaking from perspective in my application of reservoir engineering modeling. I am also aware of its usability and popularity from taking a course on Stochastic Modeling. Moreoever, I was comparing indicator transform with rank transform. Nevertheless, if you want a solution to your problem you might want to explore what a sequential indicator transform does. – S_H Apr 1 '11 at 2:22
Where can I find the definition of a "sequential indicator transform"? Sorry for my lack of knowledge, it is the first time I hear about it. – RockScience Apr 1 '11 at 2:56
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webs1.uidaho.edu/geoe428/files/GeostatSec7.pdf: You dont need to read all of it. Just get the idea of making a cut-off limit and using binary numbers 0 and 1 to populate your distribution. This way you can subdue the effect of outliers. – S_H Apr 1 '11 at 4:20

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