• The Korean Journal of Financial Management
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• v.26 no.3
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• pp.139-169
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• 2009

This paper introduces the modified VaR which takes into account the asymmetry and fat-tails of financial asset distribution, and then compares its out-of-sample forecast performance with traditional VaR model such as historical simulation model and Riskmetrics. The empirical tests using stock indices of 6 countries showed that the modified VaR has the best forecast accuracy. At the test of independence, Riskmetrics and GARCH model showed best performances, but the independence was not rejected for the modified VaR. The Monte Carlo simulation using skew t distribution again proved the best forecast performance of the modified VaR. One of many advantages of the modified VaR is that it is appropriate for measuring VaR of the portfolio, because it can reflect not only the linear relationship but also the nonlinear relationship between individual assets of the portfolio through coskewness and cokurtosis. The empirical analysis about decomposing VaR of the portfolio of 6 stock indices confirmed that the component VaR is very useful for the re-allocation of component assets to achieve higher Sharpe ratio and the active risk management.

• Communications for Statistical Applications and Methods
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• v.28 no.1
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• pp.59-79
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• 2021

Value at Risk (VaR) is one of the most common risk management tools in finance. Since a portfolio of several assets, rather than one asset portfolio, is advantageous in the risk diversification for investment, VaR for a portfolio of two or more assets is often used. In such cases, multivariate distributions of asset returns are considered to calculate VaR of the corresponding portfolio. Copulas are one way of generating a multivariate distribution by identifying the dependence structure of asset returns while allowing many different marginal distributions. However, they are used mainly for bivariate distributions and are not widely used in modeling joint distributions for many variables in finance. In this study, we would like to examine the performance of various copulas for high dimensional data and several different dependence structures. This paper compares copulas such as elliptical, vine, and hierarchical copulas in computing the VaR of portfolios to find appropriate copula functions in various dependence structures among asset return distributions. In the simulation studies under various dependence structures and real data analysis, the hierarchical Clayton copula shows the best performance in the VaR calculation using four assets. For marginal distributions of single asset returns, normal inverse Gaussian distribution was used to model asset return distributions, which are generally high-peaked and heavy-tailed.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.471-481
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• 2013

The current VaR Model based on J. P. Morgan's RiskMetrics has problem that actual loss exceeds VaR under unstable economic conditions because the current VaR Model can't re ect future economic conditions. In general, any corporation's stock price is determined by the rm's idiosyncratic factor as well as the common systematic factor that in uences all stocks in the portfolio. In this study, we propose an One-factor VaR Model for stock portfolio which is decomposed into the common systematic factor and the rm's idiosyncratic factor. We expect that the actual loss will not exceed VaR when the One-factor Model is implemented because the common systematic factor considering the future economic conditions is estimated. Also, we can allocate the stock portfolio to minimize the loss.

• The Korean Journal of Financial Management
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• v.21 no.1
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• pp.87-124
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• 2004

Some studies indicate that investors systematically underreact to new information in the stock market and Other studies indicate that investors systematically overreact. If investors irrationally react to the R&D intensity information, The portfolio strategy based on the R&D intensity information will be provided substantial excess returns. This study investigate that investors systematically underreact or overreact to the R&D intensity and whether portfolio strategy based on the R&D intensity is useful or not. Major results we as follows. First, This study indicate that investor systematically underreact to high R&D intensity and overreact low R&D intensity information. Second, after controlling the firm's specific factor such as firm size, BV/MV and past price performance, it is found that the performance of portfolio strategy based on the R&D intensity is not significant.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.689-697
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• 2016

The most useful method for financial market risk management may be Value at Risk (VaR) which estimates the maximum loss amount statistically. The VaR is used as a risk measure for one industry. Many real cases estimate VaRs for many industries or nationwide industries; consequently, it is necessary to estimate the VaR for multivariate distributions when a specific portfolio is established. In this paper, the multivariate quantile vector is proposed to estimate VaR for multivariate distribution, and the Vector at Risk for multivariate space is defined based on the quantile vector. When a weight vector for a specific portfolio is given, one point among Vector at Risk could be found as the best VaR which is called as an alternative VaR. The alternative VaR proposed in this work is compared with the VaR of Morgan with bivariate and trivariate examples; in addition, some properties of the alternative VaR are also explored.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2017.11a
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• pp.129-129
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• 2017

In planning and evaluating government R&D programs, one of the first steps is to understand the government's current R&D investment portfolio - which fields or topics the government is now investing in in R&D. Analysis methods of an investment portfolio of government R&D tend traditionally to rely on keyword searches or ad-hoc two-dimensional classifications. The main drawback of these approaches is their limited ability to account for the characteristics of the whole government investment in R&D and the role of individual R&D program in it, which tends to depend on the relationship with other programs. This paper suggests a new method for mapping and analyzing government investment in R&D using a combination of methods from natural language processing (NLP) and network analysis. The NLP enables us to build a network of government R&D programs whose links are defined as similarity in R&D topics. Then methods from network analysis show the characteristics of government investment in R&D, including major investment fields, unexplored topics, and key R&D programs which play a role like a hub or a bridge in the network of R&D programs, which are difficult to be identified by conventional methods. These insights can be utilized in planning a new R&D program, in reviewing its proposal, or in evaluating the performance of R&D programs. The utilized (filtered) Korean text corpus consists of hundreds of R&D program descriptions in the budget requests for fiscal year 2017 submitted by government departments to the Korean Ministry of Strategy and Finance.

• The Korean Journal of Financial Management
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• v.26 no.1
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• pp.165-188
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• 2009

This paper has suggested the methodology for the frontier portfolios and the optimal portfolio under the mean-VaR framework, not assuming the normal distribution and considering the investor's preferences for the higher moments of return distributions. It suggested the grid and rank approach which did not need an assumption about return distributions to find the frontier portfolios. And the optimal portfolio was selected using the utility function that considered the 3rd and the 4th moments. For the application of the methodology, weekly returns of the developed countries index, the emerging market index and the KOSPI index were used. After the frontier portfolios of the mean-variance framework and the mean-VaR framework were selected, the optimal portfolios of each framework were compared. This application compared not only the difference of the standard deviation but also the difference of the utility level and the certainty equivalent expressed by weekly expected returns. In order to verify statistical significances about the differences between the mean-VaR and the mean-variance, this paper presented the statistics which were obtained by the historical simulation method using the bootstrapping. The results showed that an investor under the mean-VaR framework had a tendency to select the optimal portfolio which has bigger standard deviation, comparing to an investor under the mean-variance framework. In addition, the more risk averse an investor is, the bigger utility level and certainty equivalent he achieves under the mean-VaR framework. However, the difference between the two frameworks were not significant in statistical as well as economic criterion.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2000.11a
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• pp.259-277
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• 2000

In this paper, we suggest theoretical grounds on the problem of R&D portfolio with different option premiums utilizing the Real Options Model, which has received intensified attention as the method of assessment of R&D project with high risk. Even though there have been many studies focused on the evaluation of option value of single project from technology valuation's perspective. there are few study on the portfolio of multiple technology investment by option value using. This paper bears practical importance by showing simple examples with the option value of investment alternatives and the valuation of related risk, the construction of optimum portfolio in technology investment alternatives.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.677-682
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• 2018

The relationship-type research and development (R&D) portfolio is a method for selecting core technologies based on their unique purposes and characteristics when the criteria for selecting them are independent. This study presents a relationship-type R&D portfolio method as a way to derive core technologies, and describes the methodology by dividing it into three steps: 1) analyze the relationships between selection criteria and analytical indicators, 2) form a portfolio matrix that best matches each selection criteria, and 3) derive the core technologies. In this study, the relationships between four selection criteria for selecting core technologies and the analytical indicators for identifying the technology level, economics, and the technology itself, are written in a table with HoQ. Based on the relationship table, analytical indicators to be considered were derived to satisfy each selection criterion, and the derived analytical indicators and the selected technologies were constructed with two axes in the portfolio matrix. The satisfied portfolio, P0, that satisfies all four criteria, and the portfolio, P1~P4, that satisfies selection criteria based on the unique characteristics of the four criteria, were constructed, and core technologies derived. The selected core technologies can be utilized in selecting a core area against the future security environment through a process like key word analysis based on the specifications.

• Korean Management Science Review
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• v.26 no.1
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• pp.37-51
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• 2009

Korean IT industry has been given much weight in national R&D management. A negative side of this fact is that Korean economy is likely to become vulnerable to a condition of the export business in certain items of IT industry which has a serious influence on the national economy. A customized investment strategy through the analysis of technology competitiveness and R&D status in each technology of IT field is required in order to rectify the structural vulnerability and pursue a continuous growth. In this research, a strategic direction to set up an efficient investment strategy is presented. In this process, it draws a portfolio analysis with two axes of technology level and technology life cycle. It also derives a priority order of the national investment considering the degree of technological impact, marketability, and adequacy of public support from AHP (Analytic Hierarchy Process) method by a survey of IT experts. A portfolio analysis in the prior stage helps the respondents in AHP become more familiar with the alternatives' characteristics so that their decision making process more corresponds with national R&D strategies.