• 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.

• Journal of Navigation and Port Research
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• v.30 no.7
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• pp.599-605
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• 2006

The Port Development has been achieved by the Government because it needs large scale of funds. However, since 1994, the Govenment has been implemeting private investments for constructing and operating the ports and so on. Although the Government had high expectation that it could expedite the expansion of the port facilities, there were many problems in view of construction, management, financial and social environment. This study figure out that most of the important reasons are the uncertainty of risk allocation between private investors and the Government, using with Analytic Hierarchy Process. It is expected that the results of this study will encourage more private investors to participate in port private investments in the future.

• Proceedings of the Korea Concrete Institute Conference
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• 2005.05a
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• pp.351-354
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• 2005

In this study ship collision risk analysis is performed to determine the design vessel for collision impact analysis of the maritime bridge. Method II which is a more complicated probability based analysis procedure is used to select the design vessel for collision impact. The AF allocation by weights seems to be more reasonable than the pylon concentration allocation method because this AF allocation takes the design parameter characteristics quantitatively into consideration although the pylon concentration allocation method brings more economical results when the overestimated design collision strength of piers compared to the strength of pylon is moderately modified.

• Journal of the Korea institute for structural maintenance and inspection
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• v.10 no.3
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• pp.123-134
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• 2006

In this study ship collision risk analysis is performed to determine the design vessel for collision impact analysis of the maritime bridge. Method II which is a probability based analysis procedure is used to select the design vessel for collision impact from the risk analysis results. The analysis procedure, an iterative process in which a computed annual frequency of collapse(AF) is compared to the acceptance criterion, includes allocation method of acceptance criterion of annual frequency of bridge component collapse. The AF allocation by weights seems to be more reasonable than the pylon concentration allocation method because this AF allocation takes the design parameter characteristics quantitatively into consideration although the pylon concentration allocation method brings more economical results when the overestimated design collision strength of piers compared to the strength of pylon is moderately modified. From the assessment of ship collision risk for each bridge pier exposed to ship collision, a representative design vessel for all bridge components is selected. The design vessel size varies much from each other in the same bridge structure depending upon the vessel traffic characteristics.

• 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.

• East Asian Economic Review
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• v.25 no.3
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• pp.310-336
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• 2021

We use iterative numerical procedures combined with analytical methods due to Rapach and Wohar (2009) to solve for the dynamic asset allocation strategy for optimal portfolio demand. We compare different optimal portfolio demands when investors in each country have different access to overseas and domestic investment opportunities. The optimal dynamic asset allocation strategy without foreign investment opportunities leads domestic investors in Korea, Hong Kong, and Singapore to allocate more funds to domestic bonds than to domestic stocks. However, the U.S. investors allocate more wealth to domestic stocks than to domestic bonds. Investors in all countries short bills at a low level of risk aversion. Next, we investigate dynamic asset allocation strategy when domestic investors in Korea have access to foreign markets. The optimal portfolio demand leads investors in Korea to allocate most resources to domestic bonds and foreign stocks. On the other hand, the portfolio weights on foreign bonds and domestic stocks are relatively low. We also analyze dynamic asset allocation strategy for the investors in the U.S., Hong Kong, and Singapore when they have access to the Korean markets as overseas investment opportunities. Compared to the results when the investors only have access to domestic markets, the investors in the U.S. and Singapore increase the portfolio weights on domestic stocks in spite of the overseas investment opportunities in the Korean markets. The investors in the U.S., Hong Kong, and Singapore short domestic bills to invest more than initial funds in risky assets with a varying degree of relative risk aversion coefficients without exception.

• Journal of the Korea Society for Simulation
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• v.33 no.1
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• pp.27-34
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• 2024

This study aims to propose an optimal asset allocation that minimizes the risk of insufficient realized replacement rates compared to the OECD average replacement rate. To do this, we set the shortfall risk of replacement rates and calculates an asset allocation plan to minimize this risk based on the period of enrollment, the income level and additional contribution. We consider stocks and deposits as investment assets, using Monte Carlo simulation with a GBM model to generate return distributions for stocks. Our result show that, for individuals with a enrollment period of less than 30 years, participants should invest a minimum of 70-80% of their funds in risky assets to minimize the shortfall risk. However, the proportion of funds that need to be invested in risky assets declines significantly when participants contribute an additional premiums. This effect is particularly pronounced among low-income individuals. Therefore, to achieve OECD average replacement rates, the government needs to incentivize participants to invest more in risky assets, while also providing policies to encourage additional contributions, especially for the low-income population.

• Journal of the Korean housing association
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• v.17 no.6
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• pp.83-90
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• 2006

The purpose of this study is to apply efficiently a successful Project Finance in BTL. To use Project Finance, it is necessary to Risk Analysis of each work step. This Risk Analysis have to repeatedly progress in overall Project Process. The differences between Corporate Finance and Project Finance are remarkably appeared in Risk Allocation and Limited Resource. To use successful Project Finance in construction industry, first, raising project confidence, second, technical : economical : lawful evaluation by Finance Expert, third, Income Guarantee for Lender or Consortium of Lending Banks, forth, Leverage Effect of Project Sponsor must be preceded.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.2
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• pp.151-164
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• 2016

In this paper, we deal with a risk analysis for an IPS (Integrated power system) and propose a simulation model combining the fault tree and event tree in order to estimate the system availability and risk level, together. Firstly, the basic information such as operational scenarios, physical structure, safety systems is explained in order to make the fault tree and event tree of the IPS. Next, we propose a discrete-event simulation model using a next-event time advance technique to advance the simulation time. Also the state transition and activity diagrams are explained to represent the relationship between the objects. By numerical examples, the redundancy allocation is considered in order to decrease the risk level of the IPS.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1A
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• pp.11-19
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• 2006

In this study ship collision risk analysis is performed to determine the design vessel for collision impact analysis of suspension bridge. Method II in AASHTO LRFD bridge design specifications which is a more complicated probability based analysis procedure is used to select the design vessel for collision impact. From the assessment of ship collision risk for each bridge pier exposed to ship collision, the design impact lateral strength of bridge pier is determined. The analysis procedure is an iterative process in which a trial impact resistance is selected for a bridge component and a computed annual frequency of collapse(AF) is compared to the acceptance criterion, and revisions to the analysis variables are made as necessary to achieve compliance. The acceptance criterion is allocated to each pier using allocation weights based on the previous predictions. This AF allocation method is compared to the pylon concentration allocation method to obtain safety and economy in results. This method seems to be more reasonable than the pylon concentration allocation method because AF allocation by weights takes the design parameter characteristics quantitatively into consideration although the pylon concentration allocation method brings more economical results when the overestimated design collision strength of piers compared to the strength of pylon is moderately modified. The design vessel for each pier corresponding with the design impact lateral strength obtained from the ship collision risk assessment is then selected. The design impact lateral strength can vary greatly among the components of the same bridge, depending upon the waterway geometry, available water depth, bridge geometry, and vessel traffic characteristics. Therefore more researches on the allocation model of AF and the selection of design vessel are required.