The untapped potential of wave energy offers another alternative to diversifying renewable energy sources and addressing climate change by reducing CO2 emissions. However, development costs to mature the technology remain significant hurdles to adoption at scale and the technology often must...

The literature on derivative pricing in illiquid markets has mostly focused on computing optimal hedging controls, but empirical microstructure studies show that large order flow generates persistent and predictable price effects. Therefore, these controls can themselves induce endogenous market...

In this paper, we study an intertemporal utility maximization problem in which an investor chooses consumption and portfolio strategies in the presence of a stochastic factor and a no-borrowing constraint. In the spirit of the Kim-Omberg model, the stochastic factor represents the excess return...

Statistical inference-both estimation and testing-for stochastic volatility (SV) models is known to be challenging and computationally demanding. We propose simple and efficient estimators for SV models with conditionally heavy-tailed error distributions, particularly the Student's t and...

Background: Transportation delays pose significant challenges for perishable products by reducing freshness, shortening selling duration, and causing lost sales during the delay. Methods: Motivated by the growing importance of transportation delays on perishable products, this study develops a...

Background: The vehicle routing problem (VRP) is of great importance in the Industry 4.0 era because enabling technologies such as the internet of things (IoT), artificial intelligence (AI), big data, and geographic information systems (GISs) allows for real-time solutions to versions of the...

Purpose - We propose a method to build confidence sets for the solutions of stochastic discrete optimization problems solved through the sample average approximation method. Design/methodology/approach - By combining the concept of Model Confidence Set (MCS) with shrinkage estimation of large...

Achieving a net-zero energy system in Europe by 2050 will likely require large-scale deployment of hydrogen and seasonal energy storage to manage variability in renewable supply and demand. This study addresses two key objectives: (1) to develop a modeling framework that integrates seasonal...

In ergodic singular stochastic control problems, a decision-maker can instantaneously adjust the evolution of a state variable using a control of bounded variation, with the goal of minimizing a long-term average cost functional. The cost of control is proportional to the magnitude of...

We study mean-field games of optimal stopping (OS-MFGs) and introduce an entropy-regularized framework to enable learning-based solution methods. By utilizing randomized stopping times, we reformulate the OS-MFG as a mean-field game of singular stochastic controls (SC-MFG) with entropy...

Technical efficiency measures the performance of an observation unit in generating outputs effectively. The stochastic production frontier (SPF), which is a commonly used method for this purpose, determines how close a unit is to achieving maximum output based on its inputs. However, the...

This study examines a scenario where a manufacturer owns one retailer and collaborates with an independent retailer to sell a single product with Poisson demand over a multi-period selling horizon. The manufacturer protects the independent retailer's profitability through price protection and...

Multinational enterprises (MNEs) often collaborate with local agents to establish initial distribution channels due to their need for market-specific knowledge and experience. As the market matures and upstream suppliers and production plans are solidified, MNEs may transition to developing...

This paper proposes a mixed-frequency stochastic volatility model for intraday returns that captures fast and slow level shifts in the volatility level induced by news from both low-frequency variables and scheduled announcements. A MIDAS component describes slow-moving changes in volatility...

This study introduces and evaluates stochastic models to describe Bitcoin price dynamics at different time scales, using daily data from January 2019 to December 2024 and intraday data from 20 January 2025. In the daily analysis, models based on are introduced to capture long memory, paired with...