• The Korean Journal of Financial Management
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• v.23 no.2
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• pp.209-237
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• 2006

Since previous default forecasting models for the firms evaluate the probability of default based upon the accounting data from book values, they cannot reflect the changes in markets sensitively and they seem to lack theoretical background. The market-information based models, however, not only make use of market data for the default prediction, but also have strong theoretical background like Black-Scholes (1973) option theory. So, many firms recently use such market based model as KMV to forecast their default probabilities and to manage their credit risks. Korean firms also widely use the KMV model in which default point is defined by liquid debt plus 50% of fixed debt. Since the debt structures between Korean and American firms are significantly different, Korean firms should carefully use KMV model. In this study, we empirically investigate the importance of debt structure. In particular, we find the following facts: First, in Korea, fixed debts are more important than liquid debts in accurate prediction of default. Second, the percentage of fixed debt must be less than 20% when default point is calculated for Korean firms, which is different from the KMV. These facts give Korean firms some valuable implication about default forecasting and management of credit risk.

• Environmental and Resource Economics Review
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• v.31 no.4
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• pp.645-668
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• 2022

Hydrogen energy is emerging as an important means of carbon neutrality in the various sectors including power, transportation, storage, and industrial processes. Fuel cell power plants are the fastest spreading in the hydrogen ecosystem and are one of the key power sources among means of implementing carbon neutrality in 2050. However, high volatility in system marginal price (SMP) and renewable energy certificate (REC) prices, which affect the profits of fuel cell power plants, delay the investment timing and deployment. This study applied the real option methodology to analyze how the dual uncertainties in both SMP and REC prices affect the investment trigger price level in the irreversible investment decision of fuel cell power plants. The analysis is summarized into the following three. First, under the current Renewable Portfolio Standard (RPS), dual price uncertainties passed on to plant owners has significantly increased the investment trigger price relative to one under the deterministic price case. Second, reducing the volatility of REC price by half of the current level caused a significant drop in investment trigger prices and its investment trigger price is similar to one caused by offering one additional REC multiplier. Third, investment trigger price based on gray hydrogen and green hydrogen were analyzed along with the existing byproduct hydrogen-based fuel cells, and in the case of gray hydrogen, economic feasibility were narrowed significantly with green hydrogen when carbon costs were applied. The results of this study suggest that the current RPS system works as an obstacle to the deployment of fuel cell power plants, and policy that provides more stable revenue to plants is needed to build a more cost-effective and stable hydrogen ecosystem.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.4
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• pp.163-174
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• 2017

This study analyzed how Korean firms' recapitalization affects their capital structure decision and firm value. Recapitalization was categorized into three groups according to the influence of the debt to equity ratio: debt ratio-increasing-recapitalization(capital reduction with refund, cash dividend), debt ratio-unchanging-recapitalization (capital reduction without refund, retirement of repurchased stocks), and debt ratio-decreasing-recapitalization(exercise the rights for convertible bonds, bond with stock warrants, exchangeable bonds and stock options). This article highlights how the relationship between the firms' recapitalization and the capital structure decision driven by the change in debt to equity ratio through the recapitalization should affect the firm value. The whole recapitalization sample used for this analysis comprised 22,814 enterprises listed on the Korea Exchange that were analyzed over the 16-year period from 2000 to 2015. To summarize the results of this Panel Data Analysis, firstly, when a firm executes debt ratio-increasing-recapitalization and debt ratio-decreasing-recapitalization at the period of t-1, the debt to equity ratio, which is increased or decreased, should affect the firm's debt capacity in the same period, then, at the period of t, the firm establishes a leverage policy to readjust the debt to equity ratio the other way around. These adjustments of debt-paying-ability from the leverage policy, including the capital structure decision, finally affect the firm value. Secondly, when a firm implements the debt ratio-unchanging-recapitalization in the period of t-1, the debt to equity ratio, which is neutral, should not affect the firm's capital structure decision. But, the firm value is positively affected by the influence of that recapitalization. Conclusively, we acknowledge a firm which carries out the recapitalization balances its capital structure to the optimal level of leverage and that the capital structure decision positively affects the corporate value.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.12
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• pp.5671-5676
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• 2011

Volatility Index is the index that represents future volatility of underlying asset implied in option price and expected value of market that measures the possibility of stock price's change expected by investors. The Korea Exchange announces a volatility Index, VKOSPI, since April, 13, 2009. This paper used daily data from January, 2002 through December, 2008 and tested power of Volatility index for future returns of portfolios sorted by size, book-to-market equity and beta. As a result, VKOSPI has the predictive power to future returns and then VKOSPI may be determinants of returns. Also if beta is included when sorting portfolio, the predictive power of VKOSPI is stronger for future portfolio returns.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.1 no.1
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• pp.91-100
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• 2000

In 1997 the recession of Korean economy brought about the bankruptcy of large corporations and the large size of non-Performing financial assets which led to IMF financial crisis. One of the major reasons for IMF financial crisis was poor loan management of domestic financial institutions . During the restructuring process of financial institutions since the IMF financial crisis, the importance of the loan management has been recognized. Especially. financial institutions' credit allocation had been concentrated on a few big conglomerates and their subsidies as well as some specific business areas. Hence, risk-diversifying portfolio effects were not reflected in any loan portfolios. The IMF financial crisis in 1997 has clearly showed that credit-risk management is essential not only for individuals' loan but also for portfolios consisting of various loans The main objective of this paper is to provide some suggestions on the direction for financial institutions in Korea to improve their loan portfolio management. Particularly, for the effective management of loan portfolios, this paper introduces quantitative credit-risk management schemes such as KMV models and CreditMetrics which are commonly used in financial institutions in advanced countries. Financial institutions in Korea should make their best efforts to establish a more scientific as well as quantitative loan portfolio management.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.2
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• pp.1118-1125
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• 2014

The past IMF foreign exchange crisis and subprime financial crisis had a big influence on variability of macroeconomics, even if the origin of its occurrence might be different. This not only had a significant infrequence on the overall industries, but also produced many insolvent companies by being closely linked with a management environment of an individual construction company leading the construction industry. The purpose of this research is to investigate characteristics of management condition of construction company according to the size of construction company using KMV model developed on the basis of the Black & Scholes option pricing theory. This research has set 28 construction companies listed to KOSPI/KOSDAQ for applying the KMV model and measuring the level of the default risk of construction companies. The data was retrieved from TS2000 established by Korea Listed Companies Association (KLCA), Statistics Korea. The analysis period is between first quarter of 2004 and fourth quarter of 2010. This research examine characteristics of the level and fluctuation process of the management condition of construction company according to the size of construction company.

• Proceedings of the Korean System Dynamics Society
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• 2005.10a
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• pp.23-42
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• 2005

세계 각 국은 자국의 이익을 보장하기 위해 방위산업의 기반을 강화하고 있고 갈수록 첨단무기체계를 도입 및 개발하고 있는 추세이다. 왜냐하면 방위산업은 평화와 군비경쟁이 공존하는 '균형속의 대결' 양상을 보이는 환경에서 자국의 생존을 위한 중요한 변수이기 때문이다. 한국의 방위산업도 1970년대 이후 괄목할 만한 성장을 이루었으나 구조적인 문제점을 탈피하지 못하였고 이제는 한계점에 도달하였다. 많은 방위산업 분야 전문가들은 현 시점에서 한국 방위산업의 발전과 활성화를 위한 방안들을 제시하였고, 정부에서도 자주국방의 기치 아래 다양한 개혁과 정책을 추구하고 있다. 그러나 대부분의 전문가 제시 내용과 연구 논문들은 방위산업의 대해 제한적으로 정성적인 분석과 대안제시에 국한되어 있는 수준이고 시스템 사고를 통해 인과관계를 분석하여 정책적인 대안을 제시하는 부분이 미흡한 실정이다. 따라서, 본 연구 논문에서는 방위산업의 전반적ㅇ.ㄴ 핵심요인을 식별하고 각 요인들간의 인과관계를 분석하여 한국 방위산업의 인과지도를 제시하였고 이를 통해 구조적인 문제점들을 해결하여 21세기 협력적 자주국방이 가능하도록 정책적인 대안을 제시하였다. 본 논문은 향후 복잡성이 가속화되는 방위산업에서 시스템적 사고를 이해하는데 기여가 될 것이고, 정책을 결정하고 추진하는 의사결정자와 무기체계 획득업무를 담당하는 실무자들에게 반드시 시스템적인 사고에 바탕을 둔 피드백 로프를 고려해야 한다는 것을 인식시킬 수 있을 것이다. 아울러 한국 방위산업의 활성화와 발전에 기여하리라 믿는다.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. 수 있다. 따라서 성장 ${\cdot}$ 고용 ${\cdot}$ 분배의 조화는 바로 노동효율 증가형 기순혁신이며, 이를 위한 인적자본에의 투자라고 말할 수 있다. 본 연구가 기술경제 패러다임(techno-economic paradigm)의 시각에서 제시하는 한국경제의 성장 ${\cdot}$ 고용 ${\cdot}$ 분배를 위한 정책방향은 다음과 같은 동태적발전과정으로 요약할 수 있다 : 기초과학연구능력 확충 ${\rightarrow}$ 소화 ${\cdot}$ 흡수 ${\cdot}$ 개량 ${\rightarrow}$ 토착화 능력의 배양 ${\rightarrow}$ 자체기술개발, 선진기술 도입, 산업간 및 산업내 기술확산, 국제기술협력 ${\rightarrow}$ 기술혁신의 촉진 ${\rightarro

• 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.