[R-SIG-Finance] statistical features of equity time series
edd at debian.org
Sun Oct 28 13:41:17 CET 2012
On 28 October 2012 at 13:21, Alex Grund wrote:
| I would like to explore some basic investment "behaviors" (not real
| quant "strategies"), such like the cost average effect.
| Therefore, I would like to create artificial time series with similar
| statistical features as real stock price time series.
| 1) How could I create them? What is a common distribution function to
| get returns from? (Without having reference data)
There are libraries full of papers and dissertations on this.
You first need to establish _which properties_ you actually want to model /
recreate. And at which time frame. Eg for daily data you may use a normal
mixture, maybe add a jump, overlay some sort of Garch or SV... but those are
I'd (carefully) resample as per 4).
| 2) How can I create a time series with similar features as a given time series?
See 1). Which features?
| 3) How can I create a time series with statistical features that are
| similar to most of the data from a set of given time series?
See 1) and 2). Seriously :) The last paper presentation I saw was Diebold who
showed how to regenerate trade duration data, as well as high frequency vol,
from a "simple" four parameter model. And simple is a relative term -- he
recaptured the features of his (SP100 equity TAQ) data set, but its not a
model you can code up in just a few lines.
| 4) Is there anything valuable which could make given data more
| exhaustible? Something like bootstrapping?
Block bootstrap for time series is pretty well established, and the tseries
package even had a tsbootstrap() function for over a decade. You can (fairly
easily) extend similar schemes.
Dirk Eddelbuettel | edd at debian.org | http://dirk.eddelbuettel.com
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