• Environmental and Resource Economics Review
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• v.22 no.1
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• pp.99-125
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• 2013

Renewable energy industry not only has a promising future but also has more risk than conventional energy industry because of its characteristics. Therefore, in this study, an analysis of domestic renewable energy company risk has been performed. The risk of domestic wind and photovoltaic energy companies has been analyzed by using time varying beta model. The model has been constructed based on risk factors like firm size, firm diversification index, domestic installation, and so on. The principal result of analysis can be summarized as follows. First, risk factors affect domestic renewable energy companies have been discovered. Variables like firm size, growth rate of debt ratio, firm diversification index are statistically significant. I found that large firms are less riskier than small firms. It is also confirmed that companies with high diversification index and high debt ratio have high risk. Second, I got the result that policy factors like domestic renewable energy installation and government R&D expenditure could decrease risk of domestic renewable energy company. Third, relative sensitivity of each risk factor have been discovered. The effect of each variable gets bigger in this order: growth rate of domestic installation, firm size or diversification index, growth rate of debt ratio, growth rate of government R&D expenditure.

• Journal of Korea Port Economic Association
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• v.36 no.3
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• pp.55-74
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• 2020

With globalization and the development of information and communication technology, the supply chain is becoming more widespread and complex, which increases the occurrence and damage caused by supply chain risks. Supply chain risk management has a great impact on corporate performance through the analysis of risk factors and proactive and strategic approaches. This study aims to analyze the effects of supply chain risk factors on risk management strategies and corporate performance empirically. In the research model for empirical analysis, supply chain risk factors were classified into supply, demand, operation, network, and external environment, while the risk management strategies were divided into active and passive strategies, as well as financial and operational performance for corporate performance. The data obtained via the questionnaire were analyzed for the path of the structural equation model. As a result of the analysis, companies are actively pursuing risk management for internal risk factors, rather than external factors, in terms of internal and external risk factors, and it was found that these strategies have a significant effect on corporate performance. Therefore, in the future, companies should conduct risk management strategies more proactively and preemptively through a thorough analysis of various risk factors affecting business operations.

• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.393-406
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• 2015

We examine how to model mortality risk using the adaptation of the mean-reverting processes for the two factor model proposed by Cairns et al. (2006b). Mortality improvements have been recently observed in some countries such as United Kingdom; therefore, we assume long-run mortality converges towards a trend at some unknown time and the mean-reverting processes could therefore be an appropriate stochastic model. We estimate the parameters of the two-factor model incorporated with mean-reverting processes by a Metropolis-Hastings algorithm to fit United Kingdom mortality data from 1991 to 2015. We forecast the evolution of the mortality from 2014 to 2040 based on the estimation results in order to evaluate the issue price of a longevity bond of 25 years maturity. As an application, we propose a method to quantify the speed of mortality improvement by the average mean reverting times of the processes.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.4
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• pp.36-51
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• 2016

This study assessed urban flood risk using a Bayesian probability statistical method and GIS incorporating a climate change scenario. Risk is assessed based on a combination of hazard probability and its consequences, the degree of impact. Flood probability was calculated on the basis of a Bayesian model and future flood occurrence likelihoods were estimated using climate change scenario data. The flood impacts include human and property damage. Focusing on Seocho-gu, Seoul, the findings are as follows. Current flood probability is high in areas near rivers, as well as low lying and impervious areas, such as Seocho-dong and Banpo-dong. Flood risk areas are predicted to increase by a multiple of 1.3 from 2030 to 2050. Risk assessment results generally show that human risk is relatively high in high-rise residential zones, whereas property risk is high in commercial zones. The magnitude of property damage risk for 2050 increased by 6.6% compared to 2030. The proposed flood risk assessment method provides detailed spatial results that will contribute to decision making for disaster mitigation.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.163-170
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• 2014

This study calculates the employee receives severance pay scale are paid from the company in the DC system. In addition, by utilizing the reserve growth model were studied in accordance with shortfall risk levels generated by stochastic asset allocation. For the analysis, from 2004 to 2013 using the KOSPI returns and total bond yields were simulated. Scenario 1 is when compared to the severance reserve is insufficient. Scenario 2 is the same as if toy reserve this severance pay. During one period, depending on the asset allocation of stocks and bonds was confirmed that the probability pension risk does not occur. And we suggest that members of DC pension risk endeavor with the government and companies to avoid.

• Environmental and Resource Economics Review
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• v.21 no.1
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• pp.129-156
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• 2012

This study analyzes the contribution to the national economy of the stable water supply through managing multi-purpose dam. For the analysis, we consider 17 major multi-purpose dams and build a CGE model with summer water and winter water being the production factors as the base year of 2007. We analyze the economic impact of meeting water demand due to the dam management and estimate the risk premium of reducing the uncertainty of water supply. The analysis results show a significant production decrease in the industries of agriculture, forestry and fisheries and tap water as well as the food and beverage industry using the former industries' output as intermediates in the production and show an production increase largely in steel industry and electronic and electrical industries. Being compared to the benchmark solution, GNP is analyzed as being reduced by 0.22~0.68%. Meanwhile, the risk premium is estimated to be about 4 billion to 24 billion won for the value 01 the measure of relative risk aversion in the range 01 0.5 to 3.0.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.1-12
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• 2013

In this paper, we introduce a continuous-time risk model where the surplus follows a diffusion process with positive drift while being subject to two types of claims. We assume that the sizes of both types of claims are exponentially distributed and that type I claims occur more frequently, however, their sizes are smaller than type II claims. We obtain the ruin probability that the level of the surplus becomes negative, by establishing an integro-differential equation for the ruin probability. We also obtain the ruin probabilities caused by each type of claim and the probability that the level of the surplus becomes negative naturally due to the diffusion process. Finally, we illustrate a numerical example to compare the impacts of two types of claim on the ruin probability of the surplus with that of the diffusion process in the risk model.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.271-286
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• 2017

The objective of this paper is to evaluate whether longevity risk is properly managed in Korean life insurance industry by measuring longevity risk in the viewpoint of natural hedge. According to analysis, the sum of the reserve of annuity and that of whole life insurance appears to decrease in the case both reserve of annuity and whole life insurance are shocked by same degree and also the mortality rate of the aged policyholders is improved faster than that of the less aged policyholders. Although the sum of the reserves increases only when the mortality improvement of annuity policyholders is higher than that of whole life insurance policyholders by two times, more than 60% of reserve increase of annuity is found to be offset by natural hedge. Thus, it is judged that the longevity risk of Korea life insurance industry is properly managed by natural hedge.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.50-50
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• 2017

도시홍수의 잠재적 위험은 홍수경감계획이 발전됨에 따라 감소하지만, 침수피해 가능성은 도시화와 도시 확장에 따라 증가한다. 침수피해 가능성에 대한 사전 파악 및 위험도 분석은 대규모 침수재해 발생 시의 위기관리에도 도움을 준다. 또한, 경제적 피해에 대한 예측은 재해발생 후 복구 및 복원 작업에 필요한 자원 할당에 매우 유용하며, 잠재적 홍수 피해에 대한 예측은 장기적인 홍수경감계획과 재해관리에 필요하다. 본 연구에서는 다차원 침수해석 모형의 결과로 산정 가능한 침수심, 유속 등의 지표들을 복합적으로 고려하여 침수위험도를 산정하고, 침수 발생 위험이 있는 지역의 인문 사회 경제적 지표를 통해 피해 저감 및 복구성을 반영하기 위한 재해 취약인자를 선정하여 해당 지역에 대한 취약도를 산정하였다. 또한, 분석된 위험도와 취약도의 연산으로 통합리스크 분석을 실시하여 침수 발생 시 해당지역에 대한 피해 예상과 지역별 상대평가가 가능하도록 하였다. 위험도와 취약도 및 리스크 분석은 다양한 인자를 동시에 고려하기 위해 여러 개의 기준에 대한 선호도를 결정하거나 최적 대안을 선택하는 다기준의사결정(MCDM)기법을 적용하였으며, MCDM기법 중 보편적으로 많이 이용되는 TOPSIS기법을 적용하였다. 이러한 리스크 분석은 우리나라 전체, 특정 시도, 시군구, 읍면동 간의 침수피해와 관련한 상대적 비교 평가가 가능하며, 대응 및 대비의 관점에서 저감 대책 수립의 우선 지역을 도출하는 데 활용될 수 있을 것이며, 침수피해 발생 후, 리스크가 큰 지역에 대해 우선적으로 복구 조치가 이뤄질 수 있을 것이다. 또한, 한정된 지자체 예산 안에서 도시홍수 피해 경감 대책 수립을 위한 의사결정에 활용될 수 있을 것으로 판단된다.