A flexible forecast density combination approach is introduced that can deal with large data sets. It extends the mixture of experts approach by allowing for model set incompleteness and dynamic learning of combination weights. A dimension reduction step is introduced using a sequential...

We present new methodology and a case study in use of a class of Bayesian predictive synthesis (BPS) models for multivariate time series forecasting. This extends the foundational BPS framework to the multivariate setting, with detailed application in the topical and challenging context of...

A flexible predictive density combination model is introduced for large financial data sets which allows for dynamic weight learning and model set incompleteness. Dimension reduction procedures allocate the large sets of predictive densities and combination weights to relatively small sets....

A novel dynamic asset-allocation approach is proposed where portfolios as well as portfolio strategies are updated at every decision period based on their past performance. For modeling, a general class of models is specified that combines a dynamic factor and a vector autoregressive model and...

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...

The study is empirically motivated to analyze the performance of new class of Bayesian shrinkage priors that are powerful in reducing time-varying parameters to static ones to avoid over-fitting problem in time-varying parameter models. We utilized newly improved shrinkage priors in a...

We propose a new methodology for predicting international stock returns and evaluating international asset pricing models. Our Bayesian framework performs probabilistic selection of predictors and factors that can shift at multiple unknown structural break dates. The approach generates...

The price volatility of energy assets such as natural gas, crude oil, and coal among others do influence electricity prices, which altogether directly have significant economic impacts on different sectors of the economy. From this viewpoint, accurate energy price volatility predictions are very...

This paper presents the Matlab package DeCo (Density Combination) which is based on the paper by Billio et al. (2013) where a constructive Bayesian approach is presented for combining predictive densities originating from different models or other sources of information. The combination weights...