In a data-rich environment, few studies use many predictors at different frequencies to forecast crude oil returns. This paper proposes a new model, LASSO-MIDAS, which combines LASSO (the least absolute shrinkage and selection operator) and MIDAS (mixed data sampling model) to predict crude oil...

This paper mainly investigates the performances of Chinese categorial economic policy uncertainty (EPU) indices and categorial Twitter-based uncertainty for predicting Chinese stock market volatility. Results show both types of uncertainty indices can predict Chinese stock market volatility,...

In dieser Arbeit handelt es um die OP-Planung im Krankenhausmanagement. Mithilfe der Theorie des Distributed-Decision-Makings wird der OP-Planungsprozess beziehungsweise der Kommunikationsprozess zwischen relevanten Parteien bei der OP-Planung mit dem Ziel einer möglichst effizienten...

As economic growth and energy consumption are strongly correlated, this research proposes a novel set of energy consumption indices (ECI) that leverages the mixing-frequency method to predict GDP growth rates. The findings demonstrate that the newly developed indices have strong predictive...

We propose an aggregate climate change concern index based on several indices from individual sources. In this paper, we define concern as attention to the risk of climate change and the negative consequences associated with that risk. Our results indicate that the aggregate climate change...

This study develops a prevailing shrinkage method, LASSO with Markov regime-switching model (MRS-LASSO), to predict the US stock market volatility. Totally 17 famous macroeconomic and financial factors are used in this research. The out-of-sample results reveal the MRS-LASSO model can...

Academic research relies heavily on exogenous drivers to improve the forecasting accuracy of Bitcoin volatility. The present study provides additional insight into the role of macroeconomic and technical indicators in forecasting the realized volatility of Bitcoin. Using 17 famous macroeconomic...

This paper comprehensively investigates the connection between oil futures volatility and the financial market based on a data-rich and model-rich environment, which contains traditional prediction models, machine learning models, and combination models. The results highlight the efficiency of...