[R-SIG-Finance] Scaling risk for irregularly spaced time series?

[Chiquoine, Ben]

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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