  In the econometrics of financial time series, it is customary to take some
parametric model for the data, and then estimate the parameters from historical
data. This approach suffers from several problems. Firstly, how is estimation
error to be quantified, and then taken into account when making statements
about the future behaviour of the observed time series? Secondly, decisions may
be taken today committing to future actions over some quite long horizon, as in
the trading of derivatives; if the model is re-estimated at some intermediate
time, our earlier decisions would need to be revised - but the derivative has
already been traded at the earlier price. Thirdly, the exact form of the
parametric model to be used is generally taken as given at the outset; other
competitor models might possibly work better in some circumstances, but the
methodology does not allow them to be factored into the inference. What we
propose here is a very simple (Bayesian) alternative approach to inference and
action in financial econometrics which deals decisively with all these issues.
The key feature is that nothing is being estimated.
