  Large and moderate deviation probabilities play an important role in many
applied areas, such as insurance and risk analysis. This paper studies the
exact moderate and large deviation asymptotics in non-logarithmic form for
linear processes with independent innovations. The linear processes we analyze
are general and therefore they include the long memory case. We give an
asymptotic representation for probability of the tail of the normalized sums
and specify the zones in which it can be approximated either by a standard
normal distribution or by the marginal distribution of the innovation process.
The results are then applied to regression estimates, moving averages,
fractionally integrated processes, linear processes with regularly varying
exponents and functions of linear processes. We also consider the computation
of value at risk and expected shortfall, fundamental quantities in risk theory
and finance.
