Functional time series and high-dimensional scalar predictors frequently arise ina wide range of modern economic and business applications, which require statisticalmodels that can simultaneously handle the temporal and causal dependence that areprevalent in large sets of mixed-type data. We...

About 23% of the German energy demand is supplied by natural gas. Additionally, for about the same amount Germany serves as a transit country. Thereby, the German network represents a central hub in the European natural gas transport network. The transport infrastructure is operated by...

We develop a novel large-scale Network Autoregressive model with balance Constraint (NAC) to predict hour-ahead gas flows in the gas transmission network, where the total in- and out-flows of the network are balanced over time. By integrating recent advances in optimization and statistical...

We propose a reinforcement learning (RL) framework to solve the HJB equations of optimal market making with the presence of rebate. As a numerical solution, the RL algorithm successfully mirrors the analytical solutions under the scheme of no rebate and constant rebate. Under the time-dependent...

Hidden liquidity is attracting significant volume share in modern order-driven markets, providing exposure risk reduction and mitigating adverse selection risk. In a continuous-time framework, we show there is a switching in the optimal liquidation strategy for a risk-neutral agent who uses both...