• Atmosphere
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• v.34 no.2
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• pp.107-121
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• 2024

In this study, we empirically analyzed the impact of physical risks due to climate change on the soundness and operational performance of the financial industry by combining economics and climatology. Particularly, unlike previous studies, we employed the Seasonal-Trend decomposition using LOESS (STL) method to extract trends of climate-related risk variables and economic-financial variables, conducting a two-stage empirical analysis. In the first stage estimation, we found that the delinquency rate and the Bank for International Settlement (BIS) ratio of commercial banks have significant negative effects on the damage caused by natural disasters, frequency of heavy rainfall, average temperature, and number of typhoons. On the other hand, for insurance companies, the damage from natural disasters, frequency of heavy rainfall, frequency of heavy snowfall, and annual average temperature have significant negative effects on return on assets (ROA) and the risk-based capital ratio (RBC). In the second stage estimation, based on the first stage results, we predicted the soundness and operational performance indicators of commercial banks and insurance companies until 2035. According to the forecast results, the delinquency rate of commercial banks is expected to increase steadily until 2035 under assumption that recent years' trend continues until 2035. It indicates that banks' managerial risk can be seriously worsened from climate change. Also the BIS ratio is expected to decrease which also indicates weakening safety buffer against climate risks over time. Additionally, the ROA of insurance companies is expected to decrease, followed by an increase in the RBC, and then a subsequent decrease.

• Journal of Korean Institute of Industrial Engineers
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• v.35 no.3
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• pp.203-212
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• 2009

The bullwhip effect refers to the phenomenon where demand variability is amplified when one moves upward a supply chain. In this paper, we exactly quantify the bullwhip effect for cases of seasonal demand processes in a two-echelon supply chain with a single retailer and a single supplier. In most of the previous research, some measures of performance for the bullwhip effect are developed for cases of non-seasonal demand processes. The retailer performs demand forecast with a multiplicative seasonal mixed model by using the minimum mean square error forecasting technique and employs a base stock policy. With the developed bullwhip effect measure, we investigate the impact of seasonal factor on the bullwhip effect. Then, we prove that seasonal factor plays an important role on the occurrence of the bullwhip effect.

• Proceedings of the Korean Statistical Society Conference
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• 2002.11a
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• pp.265-272
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• 2002

An Integer-valued autoregressive integrated (INARI) model is introduced to eliminate stochastic trend and seasonality from time series of count data. This INARI extends the previous integer-valued ARMA model. We show that it is stationary and ergodic to establish asymptotic normality for conditional least squares estimator. Optimal estimating equations are used to reflect categorical and serial correlations arising from panel count data and variations arising from three random processes for obtaining observation into estimation. Under regularity conditions for martingale sequence, we show asymptotic normality for estimators from the estimating equations. Using cancer mortality data provided by the U.S. National Center for Health Statistics (NCHS), we apply our results to estimate the probability of cells classified by 4 causes of death and 6 age groups and to forecast death count of each cell. We also investigate impact of three random processes on estimation.

• Water Engineering Research
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• v.5 no.2
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• pp.81-99
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• 2004

Long term streamflow regime under virtual climate change scenario was examined. Rainfall forecast simulation of the Canadian Global Coupled Model (CGCM2) of the Canadian Climate Center for modeling and analysis for the IPCC SRES B2 scenario was used for analysis. The B2 scenario envisions slower population growth (10.4 billion by 2010) with a more rapidly evolving economy and more emphasis on environmental protection. The relatively large scale of GCM hinders the accurate computation of the important streamflow characteristics such as the peak flow rate and lag time, etc. The GCM rainfall with more than 100km scale was downscaled to 2km-scale using the space-time stochastic random cascade model. The HEC-HMS was used for distributed hydrologic model which can take the grid rainfall as input data. The result illustrates that the annual variation of the total runoff and the peak flow can be much greater than rainfall variation, which means actual impact of rainfall variation for the available water resources can be much greater than the extent of the rainfall variation.

• Proceedings of the Technology Innovation Conference
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• 1999.12a
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• pp.328-347
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• 1999

The substitution of Electronic Commerce(EC) for the traditional transactions triggers the changes of the industry structures and promotes the cost reductions of the firms in the areas of distributions and other administrative operations associated with purchase via EC. Our study clarifies the changes of the environments attributable to EC which are faced inter-and-externally by firms and try to exhibit the trend of EC market growth through such descriptions. Regardless of the rapid spread of EC, recent studies do not show appropriately its impact on the relevant industries and our domestic economy. Therefore, our study focuses on the forecasting of the impacts of EC on the domestic productions and imports. To this end, we develop an analytic framework using the existing data in Input/Output Analysis and the estimations of the EC market growth in the future. We, finally, identify the industrial sectors whose productions and imports are estimated to be accelerated by the extension of EC and forecast the whole effects of EC on domestic productions and imports.

• Proceedings of the Korean DIstribution Association Conference
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• 2000.10a
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• pp.91-105
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• 2000

Results of a study of the coordination effect in stocking and promotional markdown policies for a supply chain consisting of a retailer and a discount outlet (DCO) are reported here. We assume that the products are sold in two consecutive periods: Normal Sales Period (NSP) and subsequent Promotional Sales Period (PSP). When managers in the two periods coordinate, they share information on the demand forecast and jointly decide the stocking quantity, markdown time schedule, and markdown price to maximize mutual profit. In the absence of coordination, decisions are decentralized to optimize the individual party's objective function. Optimal coordination policy for the retailer/DCO problem setting is analyzed, and the coordination policy is compared with the uncoordinated policy to explore factors that make coordination an effective approach.

• Journal of applied mathematics & informatics
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• v.12 no.1_2
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• pp.233-241
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• 2003

The rolling schedule procedure has been an important part of the Enterprise Resource Planning (ERP) systems. The performance of production planning in an ERP system depends on the selection of the three parameters in rolling schedule procedure: frozen interval, replanning interval, and planning horizon (forecast window). This research investigated, in a theoretical approach, the combined impact of selections of those three parameters. The proven mathematical theorems provided guidance to re-duction of instability (nervousness) and to seek the optimal balance between stability and responsiveness of ERP systems. Further the theorems are extended to incorporate the cost structure.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2012.05a
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• pp.133-135
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• 2012

The Fire growth rate(kW/s²) is significant impact on initial fire behavior in fire safety design of buildings. As a result of domestic existing combustibles, this study analyzed considering matters in techniques for calculating caloric values, and then made an investigation sheet. By utilizing written combustion sheets, the study could suggest a standard model at common houses and dense ones after getting caloric value information in dense ones. As a result, fire growth rate is experiment 1(0.01), experiment 2(0.0048), FDS(0.0072), MATSUYAMA equation(0.0144).

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1910-1914
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• 2007

This paper employs a relatively new technique of Artificial Neural Network (ANN) to forecast water demand of Daegu city. The ANN model used in this study is a single hidden layer hierarchy model. About seventeen sets of historical water demand records and the values of their socioeconomic impact factors are used to train the model. Also other regression and time serious models are investigated for comparison purpose. The results present the ANN model can better perform the issue of urban water demand forecasting, and obtain the correlation coefficient of $R^2$ with a value of 0.987 and the relative difference less than 4.4% for this study.

• Proceedings of the KIEE Conference
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• 2003.11a
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• pp.327-330
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• 2003

This paper proposes an estimation algorithm for the generating power of distributed generations(DG) interconnected with distribution networks. These days, DG are rapidly increasing and most of them are interconnected with distribution networks. The DG can supply power into the distribution network, which may make significant impact on fault current and the protection scheme of the interconnected distribution networks. Generally these influences of DG is proportioned as the distributed generator's power. Therefore, it is important to forecast the output power of distributed generator in PCC(point of common coupling). This paper presents the prediction method of DG's power by monitoring the current and phase difference.