• International Area Studies Review
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• v.13 no.2
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• pp.505-523
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• 2009

We study the cost of capital of Islamic enterprise using the Capital Asset Pricing Model(CAPM). When there exists no risk-free interest rate, the security market line(SML) of Islamic enterprise shows an upward slope starting from the origin. The slope is bigger than that of SML with risk-free interest rate. This is because the cost of capital of Islamic enterprise is higher than that of western firms for the same level of systematic risk. When the effect of zakat is considered, the risk-free interest rate is replaced by minimum required rate of return. The SML of Islamic enterprise reveals an upward slope but it does not pass through the origin. This is because Islamic enterprise cannot invest on risk-free asset. In order to overcome the theoretic limits of CAPM, we propose to use multi-factor approach such as arbitrage pricing model instead of single-factor model for future study.

• Proceedings of the KAIS Fall Conference
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• 2003.06a
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• pp.143-145
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• 2003

본 논문은 벤처기업을 합리적으로 평가할 수 있는 평가모형과 방법을 제시할 목적으로 코스닥 등록기업 중 무작위추출에 의해 선정된 99개 빈처기업을 분석표본으로 삼았으며 기업별 시장주가로 2000년 1월부터 2001년12월까지의 최고, 최저, 평균주가를 추출하였다. 본 논문에서는 벤처기업가치평가 모형으로 실물옵tus 평가 모형 쿵 성장옵션모형을 이용하여 각 기업의 현재가격, 행사가격, 변동성, 행사기간, 무위험이자율의 5개 변수로 벤처기업의 옵션가치를 산출하고 여기에 잔존가치를 현금흐름 할인법으로 한인 산출하여 그 값을 합하여 기업가치를 평가하였고 또한 현금흐름 할인법(DCF)을 이용하여 기업가치를 평가하였다. 여기에 사용된 각종 파라미터 값은 우리나라 벤처기업과 산업의 자료를 중심으로 추출하여 본 모형에 적용, 기업의 가치를 실증적으로 평가하였다.

• The Korean Journal of Financial Studies
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• v.8 no.1
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• pp.99-116
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• 2002

본 연구는 우리나라가 1996년 외환자유화와 자본자유화로 특징지워지는 국가들의 국제조직인 OECD에 가입한 이후 국내주식시장에서 환위험과 세계시장위험에 조건부로 가격보상이 이루어지고 있는가를 가격결정모형의 가장 일반적 형태인 확률적 할인요소를 이용하여 검정하였다. 무조건부 가격결정모형을 이용하여 국내주식시장에서 환위험프리미엄과 세계시장위험 프리미엄의 존재를 검정하고자 하는 연구들의 결론들이 일치되지 않고 있는 상황에서 조건부 가격결정모형을 이용한 검정노력은 자연스러운 접근순서로 볼 수 있다. 무조건부 가격결정모형에서 위험프리미엄이 존재하지 않는 것으로 검정되는 경우에도 조건부 프리미엄은 존재할 수 있다. 확률적 할인요소는 국내시장위험, 환위험, 세계시장위험 등 세 가지 위험요인이 포함되도록 정의하였으며, 조건부 정보를 구성하는 도구변수는 기존의 선행연구를 참조하여 6개를 선정하였다. 여기에는 상수, 전기의 국내시장수익률, 현재기의 무위험이자율, 전기의 세계시장수익률, 전기의 부도위험스프레드, 전기의 유로달러이자율이 포함된다. 월별 시계열 자료를 이용하여 가설을 검정한 결과 환위험 프리미엄과 세계시장프리미엄이 모두 존재하는 것으로 나타났다. 이 결과는 외환위기 기간이었던 1997년을 제외하더라도 차이가 없었다. 따라서 기업의 재무결정에서 환위험헤징활동 등이 기업가치창조와 무관하지 않음을 의미하고 있다. 국내시장위험과 세계시장위험이 모두 프리미엄을 요구하고 있음은 우리 국내시장이 세계시장과 완전 통합된 상태는 아님을 보여준다고도 해석될 수 있다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.4 no.3
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• pp.289-295
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• 2003

In this paper, the option valuation of venture business was calculated by utilizing growth option model, chosen from real option valuation method. The evaluation of venture business was estimated by adding option value to the terminal value discounted from cashflow method. As the result of the empirical analysis, ROV model applied in the Study is more reliable Han the DCF model concerning the pricing of venture business. Unlike the previous study of Schartz & Moon which only analyzed one venture business, this study estimated and analyzed the rational pricing of venture business with comparative methods using various parameters of sampling 99 domestic venture businesses.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.3
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• pp.1091-1098
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• 2012

From the perspective of risk management, financial organization that have issued ELW require an efficient hedging methodology due to recently increased trade volume of ELW. This study presents an ELW hedging methodology using artificial neural network(ANN) to minimize hedging costs. The performance of the presented methodology in this study is examined by analysis utilizing the prices and volatilities of underlying assets, risk free interest rates, and maturities and computational experiments show that the proposed method is superior to existing dynamic delta hedging(DDH) technique in terms of hedging costs ranged from 25% to 250%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.2
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• pp.712-717
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• 2011

Option issuers generally utilize Dynamic Delta Hedging(DDH) technique to avoid the risk resulting from continuously changing option value. DDH duplicates payoff of option position by adjusting hedge position according to the delta value from Black-Scholes(BS) model in order to maintain risk neutral state. DDH, however, is not able to guarantee optimal hedging performance because of the weaknesses caused by impractical assumptions inherent in BS model. Therefore, this study presents a methodology for dynamic option hedge using artificial neural network(ANN) to enhance hedging performance and show the superiority of the proposed method using various computational experiments.

• The Korean Journal of Financial Management
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• v.9 no.1
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• pp.1-23
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• 1992

CCAPM이 한국증권시장(韓國證券市場)에서 현실적(現實的)으로 성립가능한가에 대한 실증적 검증을 하였으며, 또한 시장(市場)포트폴리오와 대응되는 총소비지출의 성장률이 사전적(事前的)으로 효율적 프론티어(efficient frontier)상에 놓여 있는가에 대한 검증을 실시하였다. 이와 아울러 전통적인 S-L CAPM도 검증함으로써 CCAPM과 S-L CAPM을 비교분석(比較分析)하였다. 통계적 기법은 비선형하(非線型下)의 무관회귀분석과 GLS를 이용하였다. 1980년 1/4분기부터 1990년 4/4분기까지의 분기별 자료를 이용하여 분석한 결과, CCAPM은 전기간(全期間)을 표본으로 한 경우에 기대수익률과 위험과의 선형관계(線型關係)가 기각되었다. 그리고 전기간(全期間)을 전반기(前半期)와 후반기(後半期)로 나눈 하위기간별(下位期間別) 분석(分析)에서도 마찬가지의 결과를 얻었다. 한편 S-L CAPM의 경우에는 전기간(全期間)과 전반기(前半期)에는 선형관계가 기각되었으나 후반기(後半期)에는 채택되었다. 즉 후반기에는 위험프레미엄의 추정치가 0.05834이고 t값이 2.525로 매우 유의적이었으며 절편의 값이 통계적으로 0과 다르지 않아 실질무위험이자율(實質無危險利子率)이 0%로 추정되었다. 그런데 이는 실제로 3개월의 정기예금이자율과 소비자물가지수를 고려하여 구한 것과 일치함을 보였다. 그리고 이에 사용된 한국종합주가지수의 사전적(事前的) 효율성(效率性)은 전기간(全期間)과 하위기간(下位期間) 모두에 있는 것으로 판명되었다. 한편, CCAPM이 한국증권시장에서 기각되는 이유 중의 하나는 경기순환에 따른 생산활동(生産活動)이 즉시적으로 개별증권의 수익률에 영향을 주나 총소비지출은 이보다 비탄력적(非彈力的)인데 따른 것으로 생각되었다.

• Journal of Navigation and Port Research
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• v.39 no.1
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• pp.69-75
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• 2015

The concrete purpose of this study is to suggest actually a debt ratio to optimize the capital structure providing a kind of approach to estimate the proper debt ratio with an analytical model and empirical data in Korean shipping industry. The mathematical and analytical model is started from the first equation about ROE, return of net operating income on equity, with an independent variable, debt ratio. It is constructed with several parameters, ROS(return of operating income on sales), TAT(total assets turnover), and NFCL(net finance cost to liabilities). There could not be a certain relationship between debt ratio and ROS or TAT, while some correlation or causality between debt ratio and NFCL. In other words, most of firms with high debt ratio is likely to burden higher finance cost than others with low one. In this case, there is a linearity relationship between debt ratio and NFCL, so then the second equation considering this relation could be included within the analytical approach of this paper. To be short, if the criteria of adequate debt ratio has to be defined as some level of debt ratio to optimize ROE, the ROE could be illustrated as a quadratic equation to debt ratio from two equations. Next, this research estimated those parameters' numbers through the single regression method with data over 12 years of Korean shipping industry, and identified empirically the fact that optimal debt ratio would be approximately 400%. To conclude, if that industry's sales and operating incomes are stable, the debt ratio could be accepted until twice of 200% had forced in order to guarantee its financial safety in past time.

• 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 the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1423-1431
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• 2022

Volatility is one of the variables that the Black-Scholes model requires for option pricing. It is an unknown variable at the present time, however, since the option price can be observed in the market, implied volatility can be derived from the price of an option at any given point in time and can represent the market's expectation of future volatility. Although volatility in the Black-Scholes model is constant, when calculating implied volatility, it is common to observe a volatility smile which shows that the implied volatility is different depending on the strike prices. We implement supervised learning to target implied volatility by adding V-KOSPI to ease volatility smile. We examine the estimation performance of KOSPI200 index options' implied volatility using various Machine Learning algorithms such as Linear Regression, Tree, Support Vector Machine, KNN and Deep Neural Network. The training accuracy was the highest(99.9%) in Decision Tree model and test accuracy was the highest(96.9%) in Random Forest model.