This paper implements two types of framework to investigate the outperformance, selectivity and market timing skills in hedge funds: uncertainty and probability. Using the uncertainty framework, the paper develops an uncertain fuzzy credibility regression model in the form of a linear and...

In aftermath of the Financial Crisis, some risk management practitioners advocate wider adoption of Bayesian inference to replace Value-at-Risk (VaR) models for minimizing risk failures (Borison & Hamm, 2010). They claim reliance of Bayesian inference on subjective judgment, the key limitation...

We propose a new approach to forecasting stock returns in the presence of structural breaks that simultaneously affect the parameters of multiple portfolios. Exploiting information in the cross-section increases our ability to identify breaks in return prediction models and enables us to detect...

Many consumption-based models succeed in matching long lists of asset price moments. We propose an alternative, full-information Bayesian evaluation that decomposes the price-dividend ratio (p/d) into contributions from long-run risks, habit, and a residual. We find that long-run risks account...

We propose a Markov Switching Graphical Seemingly Unrelated Regression (MS-GSUR) model to investigate time-varying systemic risk based on a range of multi-factor asset pricing models. Methodologically, we develop a Markov Chain Monte Carlo (MCMC) scheme in which latent states are identified on...

Traditional mean variance optimization assumes that future returns and covariances of all the assets in the universe are known exactly. In practice, these input parameters are subject to estimation errors that may render the output of the optimization algorithm essentially useless. Here we...

We investigate the direct connection between the uncertainty related to estimated stable ratios of stock prices and risk and return of two pairs trading strategies: a conditional statistical arbitrage method and an implicit arbitrage one. A simulation-based Bayesian procedure is introduced for...

As common practice, oil studies in the economic literature are carried out by taking the correct specification of a model as given, and ignoring the problem of estimating overly optimistic confidence sets. This means that model uncertainty is pervasive in the empirical results. In this work I...

This paper shows how uncertainty about the type of return distribution (distribution uncertainty) can be incorporated in asset allocation decisions by using a novel, Bayesian semiparametric approach. To evaluate the economic importance of distribution uncertainty, the extent of changes in...