• Journal of Korea Port Economic Association
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• v.32 no.2
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• pp.59-71
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• 2016

This study empirically examines the dynamic specification of the ship price model based on a vector autoregressive model and data covering from January 2000 to October 2014. Our results are summarized as follows: first, the relationship between ship price and interest rate shows significantly negative and the relationship between ship price and freight rate shows positive. It provides consistent implication that ship price depends on interest rate and freight rate under the dynamic Gordon model. Second, we apply an impulse response analysis to ship price and find the responses of the ship price from both factors, interest rate and freight rate, which affect during seven periods approximately. Finally, the results of a variance decomposition indicate that freight rate is more important than interest rate on the ship price.

• Journal of Navigation and Port Research
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• v.41 no.2
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• pp.71-78
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• 2017

This paper aims to investigate the time-varying systematic risk of the stocks of Korean logistics firms. For this purpose, the period from January 1991 to October 2016 was examined with respect to 21 logistics companies that are listed on the Korea Exchange. The systematic risk of the logistics stocks is measured in terms of the Capital Asset Pricing Model (CAPM) beta for which the sensitivity of a stock is compared to the return changes of the whole market. Overall, the betas of the stocks of the Korean logistics companies are significantly lower than those of the market unity; however, it was revealed that the logistics betas are not constant, but are actually time-varying according to different economic regimes, which is consistent with the previous empirical findings. This finding is robust across different measurements of the logistics betas. In addition, the impact of macroeconomic factors on the logistics betas was examined. The present study shows that the logistics betas are positively associated with foreign exchange-rate changes.

• KDI Journal of Economic Policy
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• v.40 no.1
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• pp.45-66
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• 2018

This paper measures the extent of comovements in stock returns between Korea and three major countries (China, Japan and the U.S.) using industry-level data for Korea from 2003 to 2016 in the spirit of the international capital asset pricing model. It also examines what drives the comovements between Korea and the three countries. We find that the comovements of Korean stock returns with those of the U.S. and Japan became smaller after the global financial crisis. In contrast, the comovement in stock returns between Korea and China became larger after the crisis. After an additional analysis, we conclude that trade linkage is the main driver of the comovements between Korea and the three countries.

• The Pure and Applied Mathematics
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• v.27 no.1
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• pp.43-50
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• 2020

We investigate the domain of influence of the local volatility function on the solutions of the general Black-Scholes model. First, we generate the sample paths of underlying asset using the Monte Carlo simulation. Next, we define the inner and outer domains to find the effective volatility region. To confirm the effect of the inner domain, we use the root mean square error for the European call option prices, and then change the values of volatility in the proposed domain. The computational experiments confirm that there is an effective region which dominates the option pricing.

• The Korean Journal of Financial Management
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• v.8 no.2
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• pp.1-29
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• 1991

본 논문에서는 우리나라의 부동산시장(不動産市場)의 불완전성(不完全性)이 금융시장보다도 강한 현실을 반영하여 그것이 자본자산가격결정모형(資本資産價格決定模型) (the capital asset pricing model)에 미치는 영향을 분석하였다. 부동산시장의 불완전성으로서는 특히 부동산의 경우 기본적인 최저거래단위가 일반 금융자산보다도 높아 거래에 제약이 따른다는 점, 또한 최근 정부의 부동산 가격의 안정화 시책에 따라 개인의 부동산 보유한도가 엄격하게 제한되고 있으며 부동산 투자 이득에 대한 과세도 대폭 강화되고 있다는 점 등이 고려되었다. 이와같은 가정하에서 본고에서는 전통적인 자본자산가격모형을 수정 검토한 뒤, 그 모형의 틀속에서 부동산보유제한(不動産保有制限) 및 투자이득(投資利得)의 과세강화(課稅强化)같은 부동산가격 안정화 시책의 효과를 살펴보고, 그 외 자산담보대출의 담보비율 조정이 자산의 가격형성에 미치게되는 효과와 부동산 투자신탁제도의 도입효과, 부동산 기대수익률과 주식의 기대수익률의 관계 등을 검토하였다. 이러한 분석의 결과, 특히 본고에서는 현재 정부가 추진중인 불동산보유제한(不動産保有制限) 및 투자이득(投資利得)의 과세강화(課稅强化)와 같은 정책들이 경우에 따라서 부동산 가격을 안정화시키기 보다는 오히려 부동산 가격의 상승을 유발할 수도 있는 것으로 나타나 정책의 시행시 상당히 신중을 기하지 않으면 안되는 것으로 분석되었다.

• The Journal of Information Technology
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• v.1 no.2
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• pp.25-37
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• 1998

The paper discusses several means for estimating appropriating discount rates to value non-traded assets. That Is, this study discusses the relationship between market equity beta and observable finance information. The relationship can in principle be used to determine betas for non-traded entity for which conventional market model or pure-play techniques are impractical. In addition, the paper shows on model researched by Patterson in 1993. Patterson's research investigates the cross-sectional relationship market beta and accounting beta in Canadian capital market.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.419-430
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• 2023

Currently, NFTs have no dominant application except for the proof of ownership for digital content, and it also have small liquidity problem, which makes their price difficult to predict. Real estate usually has very high barriers to investment due to its high pricing. Real estate can be converted into NFTs and also divided into small value fungible tokens (FTs), and it can increase the the volume of the investor community due to more liquidity and better accessibility. In this document, we implement and design a system that allows ordinary users can invest on high priced real estate utilizing Black Litterman (BL) model-based Portfolio investment interface. To this end, we target a set of real estates pegged as collateral and issue NFT for the collateral using blockchain. We use oracle to get the current real estate information and to monitor varying real estate prices. After tokenizing real estate into NFTs, we divide the NFTs into easily accessible price FTs, thereby, we can lower prices and provide large liquidity with price volatility limited. In addition, we also implemented BL based asset portfolio interface for effective portfolio composition for investing in multiple of real estates with small investments. Using BL model, investors can fix the asset portfolio. We implemented the whole system using Solidity smart contracts on Flask web framework with public data portals as oracle interfaces.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.39-55
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• 2019

Recently banks and large financial institutions have introduced lots of Robo-Advisor products. Robo-Advisor is a Robot to produce the optimal asset allocation portfolio for investors by using the financial engineering algorithms without any human intervention. Since the first introduction in Wall Street in 2008, the market size has grown to 60 billion dollars and is expected to expand to 2,000 billion dollars by 2020. Since Robo-Advisor algorithms suggest asset allocation output to investors, mathematical or statistical asset allocation strategies are applied. Mean variance optimization model developed by Markowitz is the typical asset allocation model. The model is a simple but quite intuitive portfolio strategy. For example, assets are allocated in order to minimize the risk on the portfolio while maximizing the expected return on the portfolio using optimization techniques. Despite its theoretical background, both academics and practitioners find that the standard mean variance optimization portfolio is very sensitive to the expected returns calculated by past price data. Corner solutions are often found to be allocated only to a few assets. The Black-Litterman Optimization model overcomes these problems by choosing a neutral Capital Asset Pricing Model equilibrium point. Implied equilibrium returns of each asset are derived from equilibrium market portfolio through reverse optimization. The Black-Litterman model uses a Bayesian approach to combine the subjective views on the price forecast of one or more assets with implied equilibrium returns, resulting a new estimates of risk and expected returns. These new estimates can produce optimal portfolio by the well-known Markowitz mean-variance optimization algorithm. If the investor does not have any views on his asset classes, the Black-Litterman optimization model produce the same portfolio as the market portfolio. What if the subjective views are incorrect? A survey on reports of stocks performance recommended by securities analysts show very poor results. Therefore the incorrect views combined with implied equilibrium returns may produce very poor portfolio output to the Black-Litterman model users. This paper suggests an objective investor views model based on Support Vector Machines(SVM), which have showed good performance results in stock price forecasting. SVM is a discriminative classifier defined by a separating hyper plane. The linear, radial basis and polynomial kernel functions are used to learn the hyper planes. Input variables for the SVM are returns, standard deviations, Stochastics %K and price parity degree for each asset class. SVM output returns expected stock price movements and their probabilities, which are used as input variables in the intelligent views model. The stock price movements are categorized by three phases; down, neutral and up. The expected stock returns make P matrix and their probability results are used in Q matrix. Implied equilibrium returns vector is combined with the intelligent views matrix, resulting the Black-Litterman optimal portfolio. For comparisons, Markowitz mean-variance optimization model and risk parity model are used. The value weighted market portfolio and equal weighted market portfolio are used as benchmark indexes. We collect the 8 KOSPI 200 sector indexes from January 2008 to December 2018 including 132 monthly index values. Training period is from 2008 to 2015 and testing period is from 2016 to 2018. Our suggested intelligent view model combined with implied equilibrium returns produced the optimal Black-Litterman portfolio. The out of sample period portfolio showed better performance compared with the well-known Markowitz mean-variance optimization portfolio, risk parity portfolio and market portfolio. The total return from 3 year-period Black-Litterman portfolio records 6.4%, which is the highest value. The maximum draw down is -20.8%, which is also the lowest value. Sharpe Ratio shows the highest value, 0.17. It measures the return to risk ratio. Overall, our suggested view model shows the possibility of replacing subjective analysts's views with objective view model for practitioners to apply the Robo-Advisor asset allocation algorithms in the real trading fields.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.98-105
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• 2021

Using the frequency-based decomposition, I decompose the consumption growth to explain well-known patterns of stock returns in the Korean market. To be more specific, the consumption growth is decomposed by its half-life of shocks. The component over four years of half-life is called the business-cycle consumption component, and the components with half-lives under four years are short-run components. I compute the long-run and short-run components of stock excess returns as well and use component-by-component sensitivities to price stock portfolios. As a result, the business-cycle consumption risk with half-life of over four years is useful in explaining the cross-section of size-book-to-market portfolios and size-momentum portfolios in the Korean stock market. The short-run components have their own pricing abilities with mixed direction, so that the restricted one short-term factor model is rejected. The explanatory power with short- and long-run components is comparable to that of the Fama-French three-factor model. The components with one- to four-year half-lives are also helpful in explaining the returns. The results about the long-run components emphasize the importance of long-run component in consumption growth to explain the asset returns.

• Journal of Korea Technology Innovation Society
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• v.14 no.3
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• pp.578-598
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• 2011

This study estimates the value of photovoltaic core material technology, which is getting attention as a clean energy source. The estimation is based on the real option pricing model (ROPM). This study has two main contributions. The first is in the methodology. The process of modeling volatility, which is the most complicated stage in ROPM, is greatly simplified by using the stock price as a covariate representing the volatility of the real option's basic asset. The second contribution is the application of technology. In this study, the economic value of poly-silicon, a core material in the photovoltaic industry and recently surging in demand, is evaluated as a manufacturing technology. In a case study of a company in the photovoltaic industry, the stochastic process of a basic asset follows geometric Brownian motion (GBM), and the option value of firm A's poly-silicon manufacturing technology is estimated at 3.4 trillion won.