Scaling risk for irregularly spaced time series?
There is ia huge literature on calculating daily volatility using irregularly spaced intra-day data. This literature is called "realized variance". Go to Neil Shephard's website at Oxford and you can learn all about it. I have some introductory lectures on this subject on my website http://faculty.washington.edu/ezivot/econ512/econ512.htm Typically, people align intra-day data to a regular time clock (e.g every 5 seconds or every 15 seconds) prior to analysis. This simplifies calculations and using irregularly spaced data does not necessarily better results. To get good results you should use kernel type estimators or use subsampling techniques. My former Phd student Scott Payseur has implemented most of realized variance techniques in his realized package on CRAN. -----Original Message----- From: r-sig-finance-bounces at stat.math.ethz.ch [mailto:r-sig-finance-bounces at stat.math.ethz.ch] On Behalf Of Shane Conway Sent: Friday, October 10, 2008 2:16 PM To: Chiquoine, Ben Cc: r-sig-finance at stat.math.ethz.ch Subject: Re: [R-SIG-Finance] Scaling risk for irregularly spaced time series? Thanks! That's my problem: I can't rely on the observations being evenly spaced (hence my reference to "irregularly spaced"). Do most people interpolate their data so that it's regularly spaced first, and if so, won't that also bias the calculation? Any suggestions for how to produce a daily volatility calculation using an irregularly spaced intraday time series? I'm not married to using the square-root rule if there's a better alternative...
On Fri, Oct 10, 2008 at 5:06 PM, Chiquoine, Ben <BChiquoine at tiff.org> wrote:
I believe Davids suggestion is correct if your 8 hours are consecutive.
This may be pointing out the obvious but if your observations are
unevenly separated throughout the day your return series will not be
hourly and if there is mean reversion or momentum (evidence of both
have been found in fx data depending on the frequency of observation)
your results will be biased. Unfortunately I don't have a better way
for you to approach the problem this is just a heads up
Good luck,
Ben
-----Original Message-----
From: r-sig-finance-bounces at stat.math.ethz.ch
[mailto:r-sig-finance-bounces at stat.math.ethz.ch] On Behalf Of
davidr at rhotrading.com
Sent: Friday, October 10, 2008 4:52 PM
To: Shane Conway; r-sig-finance at stat.math.ethz.ch
Subject: Re: [R-SIG-Finance] Scaling risk for irregularly spaced time
series?
You basically said it: scale by the 'activity'.
If you are measuring activity for 8 hours and that is your day, then
sigma{1 hour} * sqrt(8) is your daily vol, assuming lots of untrue
things, of course ;-)
-- David
-----Original Message-----
From: r-sig-finance-bounces at stat.math.ethz.ch
[mailto:r-sig-finance-bounces at stat.math.ethz.ch] On Behalf Of Shane
Conway
Sent: Friday, October 10, 2008 3:41 PM
To: r-sig-finance at stat.math.ethz.ch
Subject: [R-SIG-Finance] Scaling risk for irregularly spaced time
series?
I'm working with intraday FX price data (primarily hourly bars). I
want to scale my volatility calculations up to the daily level.
Ordinarily I would us the square-root-of-time rule and multiple by the
sqrt(T).
The question is: how do people deal with this scaling factor when the
time series is irregularly spaced? If I apply sqrt(24) for hourly
data but I only have 8 hours of data (for instance), my calculation
will be way off.
Thanks,
Shane
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