  Consider an insurance company exposed to a stochastic economic environment
that contains two kinds of risk. The first kind is the insurance risk caused by
traditional insurance claims, and the second kind is the financial risk
resulting from investments. Its wealth process is described in a standard
discrete-time model in which, during each period, the insurance risk is
quantified as a real-valued random variable $X$ equal to the total amount of
claims less premiums, and the financial risk as a positive random variable $Y$
equal to the reciprocal of the stochastic accumulation factor. This risk model
builds an efficient platform for investigating the interplay of the two kinds
of risk. We focus on the ruin probability and the tail probability of the
aggregate risk amount. Assuming that every convex combination of the
distributions of $X$ and $Y$ is of strongly regular variation, we derive some
precise asymptotic formulas for these probabilities with both finite and
infinite time horizons, all in the form of linear combinations of the tail
probabilities of $X$ and $Y$. Our treatment is unified in the sense that no
dominating relationship between $X$ and $Y$ is required.
