• Journal of Korean Society of Rural Planning
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• v.20 no.4
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• pp.221-232
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• 2014

초고령화된 한국 농촌에서 다양한 교육 및 직업 배경을 가진 중장년층 귀농인구의 유입은 고령화 문제에 대한 대안의 하나로 부각되고 있다. 본 연구는 농가 고령화에 대한 귀농인구의 잠재적인 기여도 측면에서 귀농인구의 지역별 유입 유출 패턴을 분석하였다. 분석에는 로그선형모형과 총합레퍼런스코딩을 사용하였고, 분석자료는 통계청의 2013년 귀농 통계이다. 분석결과에 의하면, 귀농인의 절반 정도가 수도권에서 비수도권 지역으로 이주한 인구이고, 이들을 제외하면 귀농인의 대부분은 원 거주지가 있던 도 내에서 이동하고 있다. 귀농인의 귀농 전 대비 귀농 후 지역 내 오즈비(odds ratio)는 지역별로 차이가 있으며, 귀농인의 성별과 연령에 따라서도 지역별 유입 유출패턴이 다르다. 이는 귀농인 유입의 긍정적 효과를 높이기 위해서는 지자체별 차이를 반영한 특화된 정책이 필요하다는 것을 의미한다.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.3
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• pp.725-735
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• 2015

The aim of this study is to specifically understand the officetel market by empirical analysis for the determining factors that affect determining the price of the officetel in Busan. In my opinion, it can help officetel providers to select the appropriate size and location that analysis for the factors determining officetel price with market price, and also it can help customers officetel to choice depending on the purpose. So I was conducting this study. In this study, I analyzes the factors determining the price of Officetel using a OLS linear regression, semi-log model, and a robust regression-Busan area Officetel Real Transaction Price as the dependent variable and factors representing the physical characteristics, locational characteristics and regional characteristics as independent variables.

• Journal of Korean Society of Transportation
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• v.19 no.4
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• pp.19-33
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• 2001

In the developed countries, traffic noise is one of most serious problems faced by people's lives. So the importance of the traffic noise is quite well recognized by the infrastructure planners as well as the people. The traffic noise is valued in monetary terms in some countries and it is reflected in estimating the net present value or benefit/cost ratio. On the contrary, the effects of traffic noise are not reflected in the assessment of infrastructure in most cases in Korea. However, as the income level has been increasing, more people have been becoming to put more importance on their living conditions. The purpose of this paper is to estimate the value of traffic noise in the Seoul metropolitan area. The housing price were surveyed to use the quasi-hedonic price technique. By this way, two housing prices at the same floor level in different 128 complexes in the Seoul metropolitan area were surveyed. the actual traffic noise level was also measured. The differences of housing prices and noise levels were analyzed using the various types of regression models. The value is quite different by size of house. The value of large house is higher than that of small house. Since the income level of people in large house is higher than that in small house. it might be said that value of traffic noise for high income people is higher than that for low income people. Moreover, the increase of 1dB(A) noise affects the house price by about 0.3% in Seoul metropolitan area.

• Journal of Korea Technology Innovation Society
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• v.11 no.2
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• pp.241-263
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• 2008

Within any income approach, a discount rate is used to convert some projected free cash flow to its presented value. In case of valuing companies, the most frequently used discount rate is the weighted average cost of capital(WACC) at the aggregate level. But technology valuation is different to discounting aggregate corporate cash flow since it is concerned about individual Intellectual property. Therefore, blindly applying standard discount rate such as WACC in technology valuation is unlikely to lead to the right result. The primary focus of this paper is to establish the structure of discount rate for technology valuation and to suggest the method of estimation. To determine an appropriate discount rate for technology valuation, the level of technology risk, market risk and competitive risk should be included in the structure of discount rate. This paper suggests the build-up model which consists of three components as a expansion of the CAPM. It includes (1) a risk-free rate of return, (2) general market risk premium and beta and (3) intellectual property risk premium related to technology risk and specific target market risk. However, there is no specific check list for examining the intellectual property risk until now and no specific method for quantifying its risk into risk premium. This paper developed the 10 element to determine the level of the intellectual property risk and applied estimation function such as linear function, natural log function and exponential function to transform the level of risk into risk premium. The limitation of this paper is that the range of intellectual property risk premium is inferred based on the information of foreign and domestic valuation agency. Finally, this paper explored the development of an intellectual property discount rate for technology valuation and presented the method in order to quantify the intellectual property risk premium.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.79-98
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• 2009

Web traffic is becoming an important indicator to make inferences about internet companies' future prospects so that traditional firm valuation methods need to be modified to integrate the ideas of web traffic information as a major asset of internet companies. It is because web traffic is a measure of attracting visitors to firm's web site and is the basis for internet companies' marketing expenditure and customer acquisition and retention. Also the web traffic represents the internet companies' technological advances and marketability. The major purpose of this study is to show the relevance of web traffic for valuation of internet companies. For this, we test hypothesis with the firm's web traffic and financial data using the analysis model of Hand(2000a) derived from the log-linear model introduced by Ye and Finn(1999). Test results show that the web traffic, more specifically the number of unique visitors, visits, and page views are all positively related to the firm's value. This implies that the web traffic information should be considered as one of the important non-financial indicator for the internet firm valuation.

• Journal of Preventive Medicine and Public Health
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• v.30 no.4 s.59
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• pp.719-728
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• 1997

To guarantee the inter-reviewer reliability is very important in evaluating the quality of large number of clinical research papers by multiple reviewers. We cannot find reports on statistical methods for evaluating reliability for multiple raters in clinical research field. The purpose of this paper is to introduce the statistical methods focused on kappa statistic and five kinds of loglinear models for, which can be applied when evaluating the reliability of multiple raters. We have applied these methods to the result of a project, in which seven reviewers have evaluated the quality of 33 papers with regard to four aspects of paper contents including study hypothesis, study design, study population, study method, data analysis and interpretation. Among the five loglinear models including Symmetry model, Conditional symmetry model, Quasi-symmetry model, Independence model, and Quasi-independence model, Quasi-symmetry model shows the best model of fitting. And the level of reliability among seven reviewers revealed to be acceptable as meaningful.

• Journal of Korean Society of Environmental Engineers
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• v.31 no.11
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• pp.947-956
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• 2009

Residual petroleum hydrocarbons after an oil spill may accumulate in the marine benthic ecosystem due to their high hydrophobicity. A lot of monitoring data are required for the estimation of ecosystem exposure to residual petrochemicals in an ecological risk assessment in the affected region. To save time and cost, the environmental exposure to them in the affected ecosystem can also be assessed using a simple food-web bioaccumulation model. In this study, we evaluated residual concentrations of four selected polycyclic aromatic hydrocarbons (phenanthrene, anthracene, pyrene, and benzo[a]pyrene) in a hypothetic benthic ecosystem composed of six species under two exposure scenarios. Body-residue concentration ranged 5~250 mg/kg body depending on trophic positions in an extreme scenario in which the aqueous concentrations of PAHs were assumed to be one-tenth of their aqueous solubility. In addition, bioconcentration factors (BCFs) and bioaccumulation factors (BAFs) were evaluated for model species. The logarithm of bioconcentration factor (log BCF) linearly increased with increasing the logarithm of 1-octanol-water partition coefficient (log $K_{OW}$) until log $K_{OW}$ of 7.0, followed by a gradual decrease with further increase in log $K_{OW}$ without metabolic degradation. Biomagnification became significant when log $K_{OW}$ of a pollutant exceeded 5.0 in the model ecosystem, indicating that investigation of food-web structure should be critical to predict biomagnifications in the affected ecosystem because log $K_{OW}$ values of many petrochemicals are higher than 5.0. Although further research is required for better site-specific evaluation of exposure, the model simulation can be used to estimate the level of the ecosystem exposure to residual oil contaminants at the screening level.

• International Journal of Highway Engineering
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• v.10 no.4
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• pp.151-159
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• 2008

Performances of retroreflectivity vary place to place, according to traffic volumes and time lengths after striping, depending on pavement marking materials and colors. The present paper uses the nation wide data of retroreflectivity, which has been collected from freeways and then tries to develop the regression curve setting traffic volume and service life as independent variables and retroreflectivities as dependent variables. The DB system includes two year's measurement in $2005{\sim}2006$ over Korean freeway pavement marking at an interval of three months for the period. The mobile measurement system, a laserlux, was employed for the purpose. The DB has provided a lot of information about materials and performance of the specific pavement marking such as geometric features, traffic volumes, material characteristics and the installation date. This study provides the comparison of pavement marking performances under diversified conditions. Based on accumulated pavement marking performances, this study provides performance curves based on the diversified factors. The goal of the retroreflectivity modeling is to develop equations that can be used to estimate an average retroreflectivity of pavement markings as a function time since application and traffic volume. After representing the variation of retroreflectivities and estimating regression curves by linear, exponential, logarithmic and power function, the regression curve which had the highest coefficient of determination and the value similar to the last field measurement was regarded as the retroreflectivity decay model. As a result of verification, the decay model showed the signification within the 90% confidence level and especially showed the clear relation with field data according to increase of cumulative vehicle exposure. Accordingly, these models can be used to determine service lives, retroreflectivity degradation rates, and retroreflectivity of new markings.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.490-497
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• 2020

Asphalt pavement accounts for more than 90% of the total pavement in Korea. Pavement is most widely constructed among construction structures. The heat transfer characteristics (Thermophysical Properties) of the asphalt pavement cause the heat island effect in downtown areas. An increasing asphalt surface temperature is one of the major causes of damage to asphalt pavement. This study examined the heat transfer characteristic factors according to solar energy accumulation in an asphalt mixture. The specimens (WC-2 & PA-13, Recycled aggregate used WC-2) used in the experiment were compacted with a Gyratory Compactor. The thermo-physical properties (thermal conductivity, specific heat capacity, thermal diffusivity, and thermal emissivity) and solar energy accumulation were evaluated. The thermal accumulation and HFM tests revealed a 1.2- to 2.0-fold difference. This indicates that the thermal conductivity of the asphalt mixture pavement changes with the accumulation of solar energy. An analysis of the correlation of thermal conductivity according to the surface temperature of the asphalt mixture showed that WC-2 was logarithmic, and PA-13 was linear. Experiments on the heat transfer characteristics of asphalt pavement that can be used for thermal failure modeling of asphalt were conducted.