• Environmental and Resource Economics Review
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• v.21 no.3
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• pp.465-491
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• 2012

Climate change, a result of increasing global warming, has been receiving more public attention due to its serious impact upon many industries. In this study we consider sustainable- (Green-) Growth and Green-Finance, and in particular temperature derivatives, as appropriately active responses to the world's significant climate change trends. We characterize the daily average temperatures in Seoul, South Korea with their seasonal properties and cycles of error terms. We form forecasting models and perform Monte Carlo simulations, and find that the risk-neutral values for CDD call-options and HDD put-options have risen since 1960s, which implies that the trend of temperature increase can be quantified in the financial markets. Contrary to the existing models, the Vasicek model with the explicit consideration of cycles in the error terms suggests that the significant option-values for the CDD call -options above certain exercise prices, implying that there is the possibility of explicit hedging against the considerable and stable increase in temperature.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1507-1520
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• 2014

In this paper, we show that there is a positive correlation between the level and the volatility of interest rate and thus suggest that a proper interest rate volatility coefficient (IRVC), a factor used in evaluating the interest rate risk that insurers are exposed to, should be chosen in accordance with the level of interest rate. To this end, we calculate the historical volatility of interest rate using data on government bond yields and show a proportionate relationship between interest rate and historical volatility. The review of exponential Vasicek (EV) and Cox-Ingersoll-Ross (CIR) models for interest rate also confirms the positive correlation between them. The estimation of IRVC by EV and CIR models are 0.9 and 1.1, respectively, which are much smaller than the one under the current risk-based capital (RBC) requirement. We provide modified IRVCs reflecting the level of interest by the two interest rate models. Using modified IRVCs can be a more reasonable method to evaluate the interest rate risk that insurers face.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.8
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• pp.147-156
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• 2016

This study analyzed the impact of changes in the construction business on construction company insolvency according to their size using the vector error correction model. First, this study applied EDF (Expected Default Frequency), which was calculated by KMV (Kealhofer, McQuown and Vasicek) model, as a variable to indicate the insolvency of construction companies. This study set 30 construction companies listed to KOSPI/KOSDAQ for estimating the EDF by size and construction companies were divided into two groups according to their size. To examine the construction business cycles, the amount of construction orders according to the type-residential, non-residential, and civil work- was used as a variable. The serial data was retrieved from TS2000 established by the Korea Listed Companies Association (KLCA), Statistics Korea. The analysis period was between the second quarter of 2001 and fourth quarter of 2015. As a result of calculating the EDF of construction companies by size, as it is generally known, the large-sized construction companies showed lower levels of insolvency than relatively smaller-sized construction companies. On the other hand, impulse response analysis based on VECM confirmed that the level of insolvency of large-scaled companies is more sensitive to business fluctuations than relatively smaller-sized construction companies, particularly changes in the residential construction market. Hence it is a major factor affecting the changes in insolvency of large-sized construction companies.

• Journal of Korea Society of Industrial Information Systems
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• v.9 no.2
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• pp.1-8
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• 2004

In this paper, we propose three estimators of Kullback-Leibler Information functions using the data from accelerated life tests. This acceleration model is assumed to be a cumulative exposure model. Some asymptotic properties of proposed estimators are proved. Simulations are performed for comparing the small sample properties of the proposed estimators under use condition of accelerated life test.

• Korean Journal of Construction Engineering and Management
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• v.17 no.5
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• pp.13-21
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• 2016

Insolvency of construction companies that play the role of main contractors can lead to clients' losses due to non-fulfillment of construction contracts, and it can have negative effects on the financial soundness of construction companies and suppliers. The construction industry has the cash flow financial characteristic of receiving a project and getting payment based on the progress of the construction. As such, insolvency during project progress can lead to financial losses, which is why the prediction of construction companies is so important. The prediction of insolvency of Korean construction companies are often made through the KMV model from the KMV (Kealhofer McQuown and Vasicek) Company developed in the U.S. during the early 90s, but this model is insufficient in predicting construction companies because it was developed based on credit risk assessment of general companies and banks. In addition, the predictive performance of KMV value's insolvency probability is continuously being questioned due to lack of number of analyzed companies and data. Therefore, in order to resolve such issues, the Bayesian Probabilistic Approach is to be combined with the existing insolvency predictive probability model. This is because if the Prior Probability of Bayesian statistics can be appropriately predicted, reliable Posterior Probability can be predicted through ensured conditionality on the evidence despite the lack of data. Thus, this study is to measure the Expected Default Frequency (EDF) by utilizing the Bayesian Probabilistic Approach with the existing insolvency predictive probability model and predict the accuracy by comparing the result with the EDF of the existing model.

• The Korean Journal of Applied Statistics
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• v.36 no.4
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• pp.349-360
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• 2023

Dynamic Nelson-Siegel model is widely used in modeling term structure of interest rates for financial products. In this study, we explain dynamic Nelson-Siegel model from the perspective of the state space model and explore Bayesian approaches that can be applied to that model. By applying SOFR term rate data to the Bayesian dynamic Nelson-Siegel model, we confirm the performance and compare it with other competing models such as Vasicek model, dynamic Nelson-Siegel model based on the frequentist approach, and the two-factor Bayesian dynamic Nelson-Siegel model. We also confirm that the Bayesian dynamic Nelson-Siegel model outperformed its competitors on SOFR term rate data based on RMSE.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1007-1020
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• 2011

Since the research of Black and Scholes (1973), modeling methods using diffusion processes have performed principal roles in financial engineering. In modern financial theories, various types of diffusion processes were suggested and applied in real situations. An estimation of the model parameters is an indispensible step to analyze financial data using diffusion process models. Many estimation methods were suggested and their properties were investigated. This paper reviews the statistical properties of the, Euler approximation method, New Local Linearization(NLL) method, and Generalized Methods of Moment(GMM) that are known as the most practical methods. From the simulation study, we found the NLL and Euler methods performed better than GMM. GMM is frequently used to estimate the parameters because of its simplicity; however this paper shows the performance of GMM is poorer than the Euler approximation method or the NLL method that are even simpler than GMM. This paper shows the performance of the GMM is extremely poor especially when the parameters in diffusion coefficient are to be estimated.

• Proceedings of the Technology Innovation Conference
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• 2005.02a
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• pp.268-286
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• 2005

본 연구에서는 OECD 주요 10개국을 대상으로 국가별 정보통신산업의 성장 추이를 각각 분석하고 국별 특성을 비교하는데 목적이 있다. 이를 바탕으로 각국의 정보통신산업이 경기순환 또는 단계별 발전 속성을 지니고 있는지를 파악하고 국가별 공통점과 특이점을 분석하고자 하였다. 방법론적으로 OECD 국가들의 정보통신산업 GDP 추이 및 성장률의 움직임을 국면전환 (regime change) 확산과정으로 묘사함으로써 각 국가별 정보통신산업 발전 양상의 특징 및 국면전환 시점 등을 포착해 내고자 하였다 추세를 갖는 대표적 확산과정인 GBM 모형과 평균회귀 성향을 갖는 대표적 확산과정인 Vasicek 모형에 각각 마코프 국면전환을 도입하여 국가별 정보통신산업 GDP 및 GDP 성장률의 추이에 있어 국면 전환 여부와 독특한 발전 특성을 비교 분석하였다. 실증분석 결과 정보통신산업 GDP의 성장률과 변동성 사이에는 높은 상관관계가 있었으며, 한국, 멕시코 등은 고성장, 고변동성을, 미국, 프랑스, 일본 등은 저성장, 저변동성의 특성을 보이는 것으로 나타났다 또한 한국의 경우 유일하게 성장률과 변동성 모두 국면전환이 일어나는 국가로 나타났다. 장기평균 성장률의 특성에 따라 분류한 결과, 한국, 일본, 미국, 멕시코, 뉴질랜드는 고성장에서 저성장으로의 국면전환, 핀란드와 덴마크는 경기 순환적 국면전환, 노르웨이, 프랑스, 캐나다는 단일 국면으로 분류할 수 있었다. 특히 한국의 경우 평균회귀 속도와 변동성이 타 국가에 비해 높은 특성을 보여주었다. 본 연구는 정보통신산업을 미시적 분석이나 세부 항목별 정량적 분석을 통해서가 아니라 산업의 발전 속성 및 경기 순환 등의 관점에서 분석함으로써 정보통신산업 정책의 수립 및 집행을 거시적 안목 하에 정립할 수 있게 한다는 데 의의를 가진다. 또한 경제변수를 묘사하는데 있어 국면전환 확산과정을 사용함으로써 향후 실물옵션 등을 통한 기술 및 무형자산의 가치평가에 있어 기초자산의 움직임을 보다 정확히 포착해 낼 수 있는 프로세스를 제공하였다는데 또 다른 의의를 갖는다고 하겠다.

• The Korean Journal of Financial Studies
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• v.14 no.1
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• pp.119-158
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• 2008

본 연구에서는 복합금융그룹의 부실위험을 그룹전체기반 측도로 측정하는 방법론을 비교하고 국내 복합금융그룹의 자료를 이용하여 실증분석한다. Joint Forum(2001a) 방법은 연결기준을 사용하여 그룹내 자본의 중복요소들을 상계한 후 필요자본 대비 자기자본비율을 구한다. 신BIS 규제자본 방법은 Vasicek(1987)의 점근적 단일위험 모형을 가정하여 자산의 전체기반 위험을 측정하고 연결기준을 사용하여 자본의 중복계상을 배제하여 측정한다. 개별 경제적 자본 방법은 개별 경제적 위험을 수준별로 합산하여 전체기반 경제적 자본을 빌딩블록 방식으로 합산한다. 경제적 자본 방법은 위험 측정시 겪게 되는 극단적 손실 문제와 결합분포의 비대칭성을 반영할 수 있는 방법을 측정시 포함시킬 수 있다. 국내 복합금융그룹의 자료를 이용하여 실증분석을 한 결과, 첫째, 개별 재무지표에서 복합금융그룹 소속회사들의 ROA, ROA 변동성 그리고 총자산 대비 자기자본비율이 우량한 것으로 나타났다. 특히 가장 비중이 큰 은행산업에서 위 개별 재무지표는 복합금융그룹 소속회사에서 우량하게 나타난다. 둘째, 그룹전체기반 위험자본 측도로서 필요자본 대비 자기자본 비율과 연결기준 BIS비율을 살펴본 결과 은행계열 금융그룹의 부실위험이 낮은 것으로 판단된다. 전체적으로 국내 복합금융그룹의 부실위험은 높지 않은 것으로 판단된다. 이상의 결과를 바탕으로 복합금융그룹에 대한 리스크상시감시방안에의 시사점을 살펴보면, 첫째, 복합금융그룹 소속 금융회사에 대한 리스크 평가시 그룹전체기반 부실위험평가를 반영하여 이를 측정할 필요가 있다. 둘째, 권역별로 통일된 리스크감시를 위해 권역별 자기자본규제의 형평성을 제고할 필요가 있다.