liquidity, discreteness, bad ticks, sparse price data issues..

["Bilo Selhi" <biloselhi@xxxxxxxxxxx>]

i am exploring some typical price-time series data  
problems ( departure from normality features )
 and how those may affect typical 
normality  when modeling financial time series.  

if somebody has knowledge of issues of:
- price discreteness 
- liquidity / illiquidity
- sparse data 
- bad ticks

and how those affect typical price-time data normality 
assumptions, i would appreciate if you could either 
point to papers on those issues or e-mail back with 
your experience.

for example,
we can observe the following data features on 1 min 30 year US bond futures:
- discreteness - yes, data is highly discrete
- liquidity - it is pretty high 
- sparse data - front month has little sparse data, only towards rollover or on low 
volume days.
- bad ticks ( outside trades ) - almost none, however many pop and drop 
long range bars resemble bad ticks ( outlier bars ) 

or we can observe the following data features on 1 min DIA:
- discreteness - yes, to a degree
- liquidity - low 
- sparse data - yes, a lot of it
- bad ticks ( outside trades ) - frequent

if we however look at the daily or 30 min data of those two symbols 
we can observe that those features are pretty much ALL gone.

if modeling risk for instance if those features are not accounted for
( on high resolution data for example ) the model may fall apart
if data is not within the "normal" bounds... the trading system will bomb,
unless specifically adjusted for those features.

reason i am interested is that those is 
i have a pretty good understanding of those but need to check 
myself against the consensus to make sure i am not missing anything.

so, experience, papers, books??? anyone?
bilo.
ps. i would also be interested in real time implementation 
of bad tick ( quote ) checking  vs automated trade execution.