Minseok Shin Econometric Theory

[논문] ROBUST HIGH-DIMENSIONAL TIME-VARYING COEFFICIENT ESTIMATION

Abstract Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button. In this article, we develop a novel high-dimensional coefficient estimation procedure based on high-frequency data. Unlike usual high-dimensional regression procedures such as LASSO, we additionally handle the heavy-tailedness of high-frequency observations as well as time variations of coefficient processes. Specifically, we employ the Huber loss and a truncation scheme to handle heavy-tailed observations, while ℓ1-regularization is adopted to overcome the curse of dimensionality. To account for the time-varying coefficient, we estimate local coefficients which are biased due to the ℓ1-regularization. Thus, when estimating integrated coefficients, we propose a debiasing scheme to enjoy the law of large numbers property and employ a thresholding scheme to further accommodate the sparsity of the coefficients. We call this robust thresholding debiased LASSO (RED-LASSO) estimator. We show that the RED-LASSO estimator can achieve a near-optimal convergence rate. In the empirical study, we apply the RED-LASSO procedure to the high-dimensional integrated coefficient estimation using high-frequency trading data. https://www.cambridge.org/core/journals/econometric-theory/article/robust-highdimensional-timevarying-coefficient-estimation/508DF3AFB068F35BBB401C854CE34A92

Minseok Shin Journal of Econometrics

[논문] Factor and idiosyncratic VAR volatility matrix models for heavy-tailed high-frequency financial observations

Abstract This paper introduces a novel process for both factor and idiosyncratic volatility matrices whose eigenvalues follow the vector auto-regressive (VAR) model. We call it the factor and idiosyncratic VAR (FIVAR) model. The FIVAR model accounts for the dynamics of the factor and idiosyncratic volatilities and includes many parameters. In addition, many empirical studies have shown that high-frequency stock returns and volatilities often exhibit heavy tails. To handle these two problems simultaneously, we propose a penalized optimization procedure with a truncation scheme for parameter estimation. We apply the proposed parameter estimation procedure to predicting large volatility matrices and establish its asymptotic properties. https://www.sciencedirect.com/science/article/abs/pii/S0304407625001836?dgcid=coauthor

Kwangmin Jung North American Actuarial Journal

[논문] Spatial Cyber Loss Clusters at County Level and Socioeconomic Determinants of Cyber Risks

Abstract This study investigates whether cyber loss events occurring in the United States are spatially correlated and if so, which socioeconomic factors are associated with the spatial correlation. We analyze 3132 counties of the 50 U.S. states from 2005 to 2020 using the largest existing dataset of cyber risks and socioeconomic data. While previous literature found no or little spatial correlation at the state level, we are the first to document that such correlation exists at the county level; positive Moran’s I indicates that more exposed (i.e., a relatively large number of cyber events and losses) and less exposed counties are clustered. Spatial regressions show positive direct and negative indirect effects of county-level population and average income on loss frequency and severity. Large and wealthy counties thus tend to be more exposed to cyber risk events, but their geographically neighboring counties are less affected. We further investigate relatively exposed regions (California and the Northeast Coast) and three risk types (malicious, unintended, and privacy risks) and show consistent spatial effects for the key variables of population size and average income. Our findings can aid risk managers, cyber insurers, and policymakers to geographically differentiate cyber risk, recognize relatively more exposed regions, and develop more effective risk management strategies. https://doi.org/10.1080/10920277.2024.2408263

Kwangmin Jung Risk Sciences

[논문] Optimism bias and its impact on cyber risk management decisions

Abstract This study explores how optimism bias influences decision-making in cyber risk management by developing a novel model that reflects utility loss aversion, a factor previously unexplored in this context. We find that decision-makers with self-protection as reference point are less likely to invest in other cyber risk management measures, providing support for optimism bias observed in the cyber-insurance market. We also show that decision-makers with higher loss aversion tend to not invest in other cyber risk management measures. Our results help to explain the lack of demand for cyber-insurance and have important implications for corporate risk management and public policy on cyber risk. They also help better understand cyber risk events which can trigger huge systemic consequences for economies and societies. https://doi.org/10.1016/j.risk.2024.100001

Young Myoung Ko IEEE Access

[논문] Learning-Driven Berth Allocation Optimization With Port Authority Behavior

Abstract This study presents a two-phase framework that integrates machine learning with optimization modeling considering port authority behavior. First, a machine learning model predicts port authority allocation behavior based on historical data. These predictions capture the port authority’s scheduling behavior which reflects implicit operational patterns by predicting vessel waiting times. Second, these predicted behaviors are embedded into an optimization model through explicit constraints that ensure berth assignments closely align with berth authority tendencies. Using real operational data, we demonstrate the effectiveness of the proposed approach against a conventional optimization approach that does not take port authority behavior into account. Sensitivity analysis indicates that the proposed model effectively incorporates port authority behavior by varying the port authority behavior rate parameter.

Dong Gu Choi Energy and Climate Change

[논문] A multi-model assessment of carbon neutrality pathways for Korea’s power sector

Abstract In October 2021, Korea announced its mid-century carbon mitigation target of achieving carbon neutrality by 2050, reaffirming its commitment by enhancing its 2030 Nationally Determined Contribution (NDC). This study employs six energy-economic and integrated assessment models to explore net-zero emission pathways and strategies for Korea’s power sector, while assessing the associated costs and challenges. The findings underscore the complexity and urgency of this transition, with the power sector playing a pivotal role in balancing the dual challenges of rapidly growing electricity demand and full decarbonization. A shift toward a renewable-dominated power sector emerges as a robust strategy, though it poses unprecedented technological and economic challenges. Large-scale low-carbon technologies, such as carbon capture and storage (CCS) and nuclear power, are identified as crucial solutions to reduce reliance on variable renewable energy sources and mitigate associated costs. Additionally, the study finds that current energy and climate policies are insufficient to meet the mid-century mitigation target, highlighting the urgent need for policy enhancements to bridge the gap and ensure the feasibility of Korea’s carbon neutrality goal

Kwang-Jae Kim Reliability Engineering and System Safety

[논문] An integrated maintenance-upgrade policy for stochastically degrading products

Abstract Providing appropriate maintenance policies for degrading products can help sustain good performance within the specified operating time without incurring overwhelming cost burdens. In practice, many manufacturers perceive upgrade as a strategic alternative to enhance product reliability and minimize maintenance costs. In this paper, we propose a maintenance strategy framework that integrates upgrade for stochastically degrading products. We consider two different scenarios that adapt to various manufacturer’s decision-making preferences. In the first scenario, the preventive maintenance (PM) target is fixed as a constant. In the second, the PM target is variable, which allows manufacturer to find the optimal policy via more flexible repair operations. In both scenarios, we establish maintenance models under a finite time horizon using a Markov decision process and study the structural properties of the optimal policy. We conclude that the optimal decision lies in employing a control limit policy among upgrade, PM and doing nothing. Numerical examples are then provided to illustrate the proposed methods. We find that with a variable PM target, the manufacturer is afforded greater flexibility, significantly reducing the expected cost. Additionally, when the target shifts from constant to variable, the trade-off among the three decisions of upgrade, PM, and doing nothing changes considerably

Minwoo Chae Statistics and Probability Letters

[논문] On reverse inequalities for Besov integral probability metrics between smooth densities

Abstract For smooth probability densities, we prove certain reverse inequalities between Besov integral probability metrics with different orders of smoothness. Our results provide a substantial generalization and improvement of the existing results in the literature

Young Myoung Ko Computers and Industrial Engineering

[논문] The benefits of a dual-lane zone picking system with buffer-free conveyor lanes: An analytical approach

Abstract The increasing prevalence of small-footprint warehouses in urban areas necessitates a system that accommodates both fast order processing and space-saving requirements. This study introduces a Dual-lane Zone Picking system (DZP) featuring dual buffer free conveyor lanes to improve picking performance in such warehouses. We develop an analytical model for a two-zone DZP, incorporating zone passing, a notable feature in sequential zone picking systems. We fit phase-type distributions for tote interarrival times and tote picking times, model the system as a Continuous-Time Markov Chain with a block-form transition generator, and apply the matrix geometric method to obtain the system’s stationary distribution and performance metrics. Numerical experiments demonstrate that DZP considerably reduces mean flow time and yields higher maximum throughput compared to traditional single-lane zone picking systems, especially in two-zone systems with high zone-passing probabilities. The results highlight the potential of the DZP to improve order picking efficiency and provide valuable insights into operational control

Byung-In Kim Applied Mathematical Modelling

[논문] Integrated cutting stock and multi-period inventory optimization considering raw material–product eligibility for steel

Abstract This study addresses the complex cutting stock problem encountered by steel-pipe manufacturers. Key features of this problem include raw material–product eligibility, multi-period constraints, and inventory level restrictions. The primary objective is to allocate product orders to eligible raw materials while optimizing the production plan over multiple time periods. To tackle this challenge, we propose a comprehensive mathematical model with an objective function that minimizes raw-material costs, product inventory levels, the use of over-specified raw materials, unfulfilled orders, and excess inventory while adhering to eligibility constraints and yield limitations. To solve the model, we introduce a large neighborhood search algorithm integrated with a heuristic initial solution construction, destroy operators, and repair operators. A series of experiments on randomly generated instances and real-world data demonstrate the algorithm's effectiveness, outperforming conventional mathematical model-based approaches. For large-scale real-world problems, our method achieved a 24.8 % reduction in cost and a 14.0 % improvement in inventory optimization compared to current company practices