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Quantifying randomness: variance ratio
- Thread starter stephencrowley
- Start date
I trade exclusively intraday.. overnight returns are problematic due to the low sample size.. would be nice if the markets traded 24/7...but even then there would be seasonal activity.
The intraday trend analysis probably has no relation to daily trend analysis.. but that is just a guess, I don't have any daily data to test...
No extension of the VR is necessary.. just prepare your data properly.
The intraday trend analysis probably has no relation to daily trend analysis.. but that is just a guess, I don't have any daily data to test...
No extension of the VR is necessary.. just prepare your data properly.
Quote from squeeze:
Have you managed to extend it to trend analysis using intra-day interval data?
This will be different from a daily trend analysis due to the over-night intervals and the intra-day volatility structure.
What do you mean by "daily version"? The calculation is the same no matter what your frequency of choice is.
Quote from squeeze:
The concept should be extendable to trend analysis over the active trading session.
For the daily version I modified the variance calculation (ln(C/C[n]))^2 which is correct for log normally distributed returns.
The calculation in the paper is a close approximation.
So do you guys think this 1.67 number can be extended intraday? For example a 10 min chart would have its own HV right. Can you take the open to close 10 min bars for the past 30 10M periods lets say and run this test and decide that anything > 1.67 shows mean reversion so fade the spikes vs. the 10M bars that show < 1.67 which implies , at the least, don't fade the spikes? THanks
I can't see using the daily data as a judge on how to approach intra-day trading. Woudn't that be disastrous?
I can't see using the daily data as a judge on how to approach intra-day trading. Woudn't that be disastrous?
To be clear, the variance ratio doesnt depend on any magic numbers and doesnt use highs, lows, etc and can be used on any time frame. I cant speak for these other measures.
Quote from GATrader:
So do you guys think this 1.67 number can be extended intraday? For example a 10 min chart would have its own HV right. Can you take the open to close 10 min bars for the past 30 10M periods lets say and run this test and decide that anything > 1.67 shows mean reversion so fade the spikes vs. the 10M bars that show < 1.67 which implies , at the least, don't fade the spikes? THanks
I can't see using the daily data as a judge on how to approach intra-day trading. Woudn't that be disastrous?
Quote from stephencrowley:
What do you mean by "daily version"? The calculation is the same no matter what your frequency of choice is.
If the calculation in the paper is applied to intra-day data without modification I would expect the results to be skewed by the over-night volatility. Markets with high over-night volatility would appear more trending as a result.
I see what you are saying.. that is only true if you were to concatenate intra-day returns together and ignore overnight changes.. this is not a good idea.
Basically, I calculate a variance ratio profile for each day using intraday data.. and then overlay them.. if they are all very similiar then they are "consistently random" across the days analyzed.
However, I've found some symbols that are trending many days and then pure random walks on others. I guess it basically quantifies what traders call intuitively "choppy" and "trendy" markets.
Basically, I calculate a variance ratio profile for each day using intraday data.. and then overlay them.. if they are all very similiar then they are "consistently random" across the days analyzed.
However, I've found some symbols that are trending many days and then pure random walks on others. I guess it basically quantifies what traders call intuitively "choppy" and "trendy" markets.
Quote from squeeze:
If the calculation in the paper is applied to intra-day data without modification I would expect the results to be skewed by the over-night volatility. Markets with high over-night volatility would appear more trending as a result.
Quote from stephencrowley:
I see what you are saying.. that is only true if you were to concatenate intra-day returns together and ignore overnight changes.. this is not a good idea.
Basically, I calculate a variance ratio profile for each day using intraday data.. and then overlay them.. if they are all very similiar then they are "consistently random" across the days analyzed.
However, I've found some symbols that are trending many days and then pure random walks on others. I guess it basically quantifies what traders call intuitively "choppy" and "trendy" markets.
I can see how you could calculate a single variance ratio number for each day but how do you calculate a variance ratio profile?
Since the defintion of the variance ratio (or modified variance ratio) is the relative difference between 1-period changes vs N-period changes then you just calculate all the variance ratios from 1 to M and plot them.
Note: you must use overlapping returns when calculating the N-period differences
e.g.
[sum(1:3) sum(2:4) sum(3:5)] etc
vr(1,1) is always = to 1 by definition
vrProfile=[var(1,1) var(1,2) var(1,3) ...var(1,M)];
You can also fit a polynomial to the VR profile and it'll define 99% of the profile with only the first two coeffecients.
Note: you must use overlapping returns when calculating the N-period differences
e.g.
[sum(1:3) sum(2:4) sum(3:5)] etc
vr(1,1) is always = to 1 by definition
vrProfile=[var(1,1) var(1,2) var(1,3) ...var(1,M)];
You can also fit a polynomial to the VR profile and it'll define 99% of the profile with only the first two coeffecients.
Quote from squeeze:
I can see how you could calculate a single variance ratio number for each day but how do you calculate a variance ratio profile?