Using a Bayesian time‐varying beta model, we explore how the systematic risk exposures of hedge funds vary over time conditional on some exogenous variables that managers are assumed to use in changing their trading strategies. In such a setting, we impose a structure on fund returns, betas...

All the financial practitioners are working in incomplete markets full of unhedgeable risk-factors. Making the situation worse, they are only equipped with the imperfect information on the relevant processes. In addition to the market risk, fund and insurance managers have to be prepared for...

We solve two optimal stopping problems whose payoff functions are the maximum and the minimum of two state variables driven by the Ornstein-Uhlenbeck processes. We consider a class of problems where we obtain analytical solutions. Furthermore, by making use of the analytical results we study...

Among the 5,000 equity mutual funds in the world, more than 80 percent belong to some fund family. A fund family is a group of mutual funds supervised by the same investment group. Despite the prevalence of the family organization, previous literature, when evaluating mutual fund performance,...

Risk measurement and pricing of financial positions are based on modeling assumptions, which are common assumptions on the probability distribution of the position's outcomes. We associate a model with a probability measure and investigate model risk by considering a model space. First, we...

This paper investigates dynamic correlations both across commodities and between commodities and traditional assets, such as equities and government bonds, using the Regime Switching Dynamic Correlation (RSDC) model. There are three major findings. First, results from correlations both across...

The intuitiveness and practicability of mean-variance portfolios largely depends on the accuracy of moment estimates, which are subject to large estimation errors and conditional on time. We propose a model accounting for factor dynamics in a Bayesian setting, in which the impact of estimation...

We introduce a simulation-free method to model and forecast multiple asset returns and employ it to investigate the optimal ensemble of features to include when jointly predicting monthly stock and bond excess returns. Our approach builds on the Bayesian Dynamic Linear Models of West and...

We develop a new variational Bayes estimation method for large-dimensional sparse vector autoregressive models with exogenous predictors. Unlike existing Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms, our approach is not based on a structural form representation of the...

Determining the optimal mix of assets in the context of a portfolio construction involves “smart” forecasts of asset returns as well as good estimates of the asset return variances and covariances. Typically, sample moments are used as best estimates of the population moments. Several...