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Walter:
Can you please define/explain "TLF processes"? Is this a mathmatical
"fitting" of a non-Gaussian distribution into a Normal (ie. bell curve)
distribution or is it a "natural" normalization as more data is added?
Thanks.
----- Original Message -----
From: Walter Lake <wlake@xxxxxxxxx>
To: Metastock bulletin board <metastock@xxxxxxxxxxxxx>
Sent: June 30, 1999 05:33
Subject: Analysis of high speed data
> The following from "Financial Markets Tick By Tick" page 248 suggests an
> answer to the confusion surrounding Gaussian Vs non-Gaussian analysis in
> backtesting:
>
> "... this means that investors with horizons of one month or longer face
> Gaussian risks and that conventional risk management and asset pricing is
> applicable. On the other hand, investors at shorter horizons will face
> non-Gaussian fat-tailed distribution and must therefore use high-frequency
> data {defined as 30 minute bars} and non-Gaussian probability tools (e.g.,
> fat-tail estimators, rare event analysis) to quantify their risks. ..."
>
> As the time periods become longer for the data {i.e., end of day, weekly,
> monthly} TLF processes converge Non-Gaussian distributions to Gaussian
> distributions {i.e., from fat tails to higher peaked central
distributions}.
>
> "... These conclusions agree with conventional wisdom and practice ... and
> suggest that high-frequency data analysis is of little value {in system
> testing} to long-term investors. ..."
>
> They go on to describe the clear seasonality that exists within the
> different trading time periods.
>
> Therefore, the analysis and the seasonality and the trading rules are time
> period specific to the individual trader. You can pick your time frame and
> use the appropriate analysis without being confused by the requirements of
> other time periods.
>
> Best regards
>
> Walter
>
>
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