This paper presents a novel Bayesian method for estimating dynamic stochastic general equilibrium (DSGE) models subject to a constrained posterior distribution of the implied Sharpe ratio. We apply our methodology to a DSGE model with habit formation in consumption and leisure, using an estimate...

This paper presents a novel Bayesian method for estimating dynamic stochastic general equilibrium (DSGE) models subject to a constrained posterior distribution for the implied Sharpe ratio. We apply our methodology to a DSGE model with habit formation in consumption and leisure, and show that...

Financial analysts typically estimate volatilities and correlations from monthly or higher frequency returns when determining the optimal composition of a portfolio. Although it is widely acknowledged that these measures are not necessarily stationary across samples, most analysts assume...

The Sharpe ratio is the most widely used metric for comparing performance across investment managers and strategies, and the information ratio is as commonly used to evaluate performance relative to a benchmark. Although it is widely recognized that non-linearities arising from the inclusion of...

For the popular mean-variance portfolio choice problem in the case without a risk-free asset, we develop a new portfolio strategy to mitigate estimation risk. We show that in both calibrations and real datasets, optimally combining the sample global minimum variance portfolio with a sample...

When a benchmark model is inefficient, including additional assets to the benchmark portfolios can improve its performance. In reality, however, the efficiency of a benchmark model relative to a given set of test assets is ex ante unknown, and the optimal portfolio is constructed based on...

We introduce an ensemble learning method based on Gaussian Process Regression (GPR) for predicting conditional expected stock returns given stock-level and macro-economic information. Our ensemble learning approach significantly reduces the computational complexity inherent in GPR inference and...

High frequency data typically exhibit asynchronous trading and microstructure noise, which can bias the covariances estimated by standard estimators. While a number of specialised estimators have been developed, they have had limited availability in open source software. HighFrequencyCovariance...

Based on intraday data for a large cross-section of individual stocks and Exchange traded funds, we show that short-term as well as long-term fluctuations of realized market and average idiosyncratic higher moments risks are priced in the crosssectionof asset returns. Specifically, we find that...

In this note I bring a new aspect into the so called Island Problem. Given that only cases where there is a suspect reaches the court, what is the consequence for the probability of guilt? I find that it indeed matters for the results that court cases are selected in this way. The analysis...