Motivated by the short-lived arbitrage model, we jointly imply volatilities and virtual interest rates of options to develop minimum variance hedge ratios. We use artificial neural networks to capture the dynamics between the underlying asset price and correlated state variables, which improves...

The main objective is to examine the complex relationship between financial development and economic growth in Syria for the period of 1980-2018, using the Artificial Neural Network VAR model, the Non-Linear Causality test using Artificial Neural Networks, the Nonlinear Autoregressive...

Deep neural networks (DNNs) have been applied to predict the cycle times of jobs in manufacturing accurately. However, the prediction mechanism of a DNN is complex and difficult to communicate. This limits its acceptability (or practicability) in real-world applications. An explainable...

Classic artificial intelligence (Q-learning) algorithms have been capable of consistently learning supra-competitive pricing strategies in infinitely repeated Nash-Bertrand pricing games without human communication. Such algorithms have been able to converge due to the temporal correlation of...

We present a hierarchical architecture based on recurrent neural networks for predicting disaggregated inflation components of the Consumer Price Index (CPI). While the majority of existing research is focused on predicting headline inflation, many economic and financial institutions are...