Increasingly sophisticated methods are available for analyzing financial data and helping decision makers. But in practice the data that is used by these methods may be full of errors; it is dirty data. And it is often the more sophisticated methods that are most affected by dirty data: time series and variance models, such as GARCH, seem to be particularly sensitive to the presence of bad values in the data.
While it is sometimes possible to use robust techniques that are less sensitive to bad observations, for example using a median instead of a mean, it makes sense to deal with the bad data before the modeling takes place. Improve the quality of the data and you are very likely to improve the quality of the results. At PaceMetrics one of our focuses is on producing reliable data through the cleansing of market data cleansing. Read about our Assure Solution to see how we can provide you with better data too.
The following links explain what market data is and what data quality specialists like PaceMetrics can do to cleanse it.