We develop methodology and theory for a general Bayesian approach towards dynamic variable selection in high-dimensional regression models with time-varying parameters. Specifically, we propose a variational inference scheme which features dynamic sparsity-inducing properties so that different...

We pit individual theoretical predictors of the equity premium against a variety of data-driven statistical methods. Theoretically motivated predictive regressions outperform conventional penalised regressions but have similar out-of-sample R2 and lower economic gains relative to more agnostic...

We investigate the correlation between rarity and returns on investments in digital art in the form of non-fungible tokens (NFT) across three major comparable collections. Methodologically, we first quantify the “shadow” price of both individual rarity scores and aggregate market activity...

We propose an alternative approach towards cost mitigation in volatility-managed portfolios based on smoothing the predictive density of an otherwise standard stochastic volatility model. Specifically, we develop a novel variational Bayes estimation method that flexibly encompasses different...

This paper proposes a Bayesian estimation framework for a typical multi-factor model with time-varying risk exposures to macroeconomic risk factors and corresponding premia to price U.S. stocks and bonds. The model assumes that risk exposures and idiosynchratic volatility follow a break-point...

We use Bayesian methods to estimate a multi-factor linear asset pricing model characterized by structural instability in factor loadings, idiosyncratic variances, and factor risk premia. We use such a framework to investigate the key differences in the pricing mechanism that applies to...