Quantile and least-absolute deviations (LAD) methods are popular robust statistical methods but have not generally been applied to state filtering and sequential parameter learning. This paper introduces robust state space models whose error structure coincides with quantile estimation...

When a new asset keeps changing its narrative, investors find difficulty in classifying and understanding the new asset. Rational investors therefore face unprecedented uncertainty and learn about the joint dynamics to optimize their portfolio accordingly. Bitcoin's "digital gold" narrative, as...

For optimal asset allocation, mean-variance investors must learn about the joint dynamics of new and existing asset classes, not only their profitability. Bitcoin's digital gold narrative provides a unique laboratory to test this hypothesis. We find that a decrease in investors' estimate on...

We examine the effects of parameter uncertainty and Bayesian learning on equilibrium asset prices when all the structural parameters of the aggregate consumption and dividend growth rate processes are unknown. With realistic calibration of a parsimonious set of prior parameters, the model...

We examine the effects of estimation risk and Bayesian learning on equilibrium asset prices when there is uncertainty about both the first and second moments of consumption and dividend growth rates. For the 1891-2007 period, our model generates a sizable average annual equity premium,...