The theory of financial markets is well developed, but before any
of it can be applied there are statistical questions to be answered: Are
the hypotheses of proposed models reasonably consistent with what
data shows? If so, how should we infer parameter values from data?
How do we quantify the error in our conclusions? This paper examines
these questions in the context of the two main areas of quantitative
finance, portfolio selection and derivative pricing. By looking at these
two contexts, we get a very clear understanding of the viability of the
two main statistical paradigms, classical (frequentist) statistics, and
Bayesian statistics
