Conviction Function ? A New Decision Paradigm for Personal inancial Risk Management in the Face of Large Exogenous Shocks

This paper contributes to the limited-information literature on savings in a stochastic
environment. In particular, it contributes techniques and concepts to the question of
state verification (or filtering), by including learning about aggregate income shocks,
based on signals. As a seminal contribution to the extant literature, a “conviction
function” is introduced, which takes into account histories of past prediction errors in
determining how rational agents internalize such information in taking personal investment
decisions. For purpose of a more transparent illustration, a numerical rendition
of the posited model is provided for five consecutive time periods. We also perform
a series of Monte Carlo simulations to demonstrate how the posited approach could
potentially outperform traditional forward-looking models in the presence of sudden
large extraneous shocks reminiscent of the recent Global Financial Crisis.