Is there any research on applying state-space or dynamic linear models to forecasting equity risk premia on a security-by-security basis with a medium term horizon (say 3 month to 12 months horizon)?
Markov Models are a special case of state-space models where the states are discrete. There's an avalanche of research on these MM's and HMM's (beginning with Ryden 1998) but it seems to me there are some advantages to taking an approach where the state is a continuous variable. I have seen papers by Johannes, Lopes, and Carvalho that focus on shorter-horizons and demonstrate the superiority of PL over MCMC methods.
Seems to me that this approach could capture the time-series dependence and cross-sectional dependencies in a way that traditional panel models cannot. Also, it seems that since history rhymes but does not repeat, a long-memory state-space model would be better than an HMM.
Before I go down this windy road I'm curious if anyone out there has already attempted this or if there is a weakness with this approach. For example, perhaps state-space or particle filtering models only work best when the forecast horizon is very short.