• Korean Management Science Review
• /
• v.18 no.1
• /
• pp.29-39
• /
• 2001

Traditionally, companies have been concerned with making an investment decision either to go now or never to go forever. However, owing to the development of the theory of options pricing in a financial investment field and its introduction to the appraisal of real investments in these days, we are now partially allowed to derive the value of a managerial flexibility of real investment projects. In this paper, we derived a general mathematical model to price the option value of real investment projects assuming that they have only one-period of time under which uncertainty exists. This mathematical model was developed based on the opportunity cost concept. We will show a simple numerical example to illustrate how the mathematical model works comparing it with the existing models.

• International journal of advanced smart convergence
• /
• v.9 no.4
• /
• pp.120-123
• /
• 2020

The surveillance system to prevent crime and accidents in advance has become a necessity, not an option in real life. Not only public institutions but also individuals are installing surveillance cameras to protect their property and privacy. However, since the installed surveillance camera cannot be monitored for 24 hours, the focus is on the technology that tracks the video after an accident occurs rather than prevention. In this paper, we propose a system model that monitors abnormal behaviors that may cause crimes through real-time video, and when a specific behavior occurs, the surveillance system automatically detects it and responds immediately through an alarm. We are a model that analyzes real-time images from surveillance cameras and uses I3D models from analysis servers to analyze abnormal behavior and deliver notifications to web servers and then to clients. If the system is implemented with the proposed model, immediate response can be expected when a crime occurs.

• Journal of Korea Society of Industrial Information Systems
• /
• v.16 no.3
• /
• pp.121-135
• /
• 2011

We provide an economic evaluation model to help managers make reasonable decision for the investment in the appropriate type of cloud computing. Cloud computing can be classified into public, private and hybrid architecture and we evaluate their attractiveness using traditional NPV and real option methods. We conduct economic analysis by comparing traditional software delivery model with various types of cloud computing. The work compares each mode of cloud computing against each other using passive NPV and dynamic real-option method. For more objective and conservative evaluation of investment alternatives, we eliminate conventional benefits that are often subjective or hard to measure, and count only the reduction of investment cost and maintenance cost as benefit. We argue that hybrid and public cloud computing can be undervalued without their intrinsic options such as abandonment, expansion and contraction.

• The Korean Journal of Applied Statistics
• /
• v.29 no.1
• /
• pp.181-192
• /
• 2016

We consider several methods to approximate option prices with correction terms to the Black-Scholes option price. These methods are able to compute option prices from various risk-neutral distributions using relatively small data and simple computation. In this paper, we compare the performance of Edgeworth expansion, A-type and C-type Gram-Charlier expansions, a method of using Normal inverse gaussian distribution, and an asymptotic method of using nonlinear regression through simulation experiments and real KOSPI200 option data. We assume the variance gamma model in the simulation experiment, which has a closed-form solution for the option price among the pure jump $L{\acute{e}}vy$ processes. As a result, we found that methods to approximate an option price directly from the approximate price formula are better than methods to approximate option prices through the approximate risk-neutral density function. The method to approximate option prices by nonlinear regression showed relatively better performance among those compared.

• 한국경영정보학회:학술대회논문집
• /
• 2008.06a
• /
• pp.957-962
• /
• 2008

We propose a model for the line expansion problem in the AMOLED (Active Matrix Organic Light Emitting Diodes) industry, which now faces market uncertainty: for example, changing customer needs, technological development path, etc. We focus on the optimal investment time and size of the AMOLED production lines. In particular, employed here is the ROV (Real Options Valuation) model to show how to capture the value of line expansion and to determine the optimal investment time. The ROV framework provides a systematic procedure to quantify an expected outcome of a flexible decision which is not possible in the frame of the traditional NPV (Net Present Value) approach. Furthermore, we also use Monte Carlo simulation to measure the uncertainty associated with the line expansion decision; Monte Carlo simulation estimates the volatility of a decision alternative. Lastly, we present a scenario planning to be conducted for what-if analysis of the ROV model.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.9 no.1
• /
• pp.31-53
• /
• 2005

We analyze some real data and, from the background of analysis of data, we define a multi-dimensional jump-type asset model which is derived from short-sampling asset prices. We study some basic properties of this asset model.

• The Korean Journal of Applied Statistics
• /
• v.30 no.2
• /
• pp.243-257
• /
• 2017

In this paper, we compare several methods to approximate option prices: Edgeworth expansion, A-type and C-type Gram-Charlier expansions, a method using normal inverse gaussian (NIG) distribution, and an asymptotic method using nonlinear regression. We used two different types of approximation. The first (called the RNM method) approximates the risk neutral probability density function of the log return of the underlying asset and computes the option price. The second (called the OPTIM method) finds the approximate option pricing formula and then estimates parameters to compute the option price. For simulation experiments, we generated underlying asset data from the Heston model and NIG model, a well-known stochastic volatility model and a well-known Levy model, respectively. We also applied the above approximating methods to the KOSPI200 call option price as a real data application. We then found that the OPTIM method shows better performance on average than the RNM method. Among the OPTIM, A-type Gram-Charlier expansion and the asymptotic method that uses nonlinear regression showed relatively better performance; in addition, among RNM, the method of using NIG distribution was relatively better than others.

• Journal of the Korean Data and Information Science Society
• /
• v.28 no.2
• /
• pp.251-260
• /
• 2017

The option pricing of Black와 Scholes (1973) and Merton (1973) has been widely reported to fail to reflect the time varying volatility of financial time series in many real applications. For example, Duan (1995) proposed GARCH option pricing method through Monte Carlo simulation. However, financial time series is known to follow a fat-tailed and leptokurtic probability distribution, which is not explained by Duan (1995). In this paper, in order to overcome such defects, we proposed the option pricing method based on GARCH models with normal mixture errors. According to the analysis of KOSPI200 option price data, the option pricing based on GARCH models with normal mixture errors outperformed the option pricing based on GARCH models with normal errors in the unstable period with high volatility.

• Proceedings of the Technology Innovation Conference
• /
• 2004.02a
• /
• pp.222-245
• /
• 2004

This paper has an intention of proposing useful parametric tables of each industry group within Korea. These parametric tables can be insightful criteria for those who are dealing with the exact valuation of company, technology or industry through Real Option Analysis (ROA) since the identification of the movement of underlying asset is the very first step to be done. To give the exact estimations of parameters and the most preferred model in each industry group, we cover topics on ROA, stochastic process, and parametric estimation method like Generalized Method of Moments (GMM) and Maximum Likelihood Estimation (MLE). Additionally, specific industry groups, such as, Internet service group and mobile telecommunication service group defined independently in this paper are also examined in terms of its property of movement with the suggesting of the most fitting stochastic model.

• Environmental and Resource Economics Review
• /
• v.14 no.3
• /
• pp.753-773
• /
• 2005

This paper is a study of the investment dynamics focusing on crude oil supply by OPEC and non-OPEC. Oil supply capacity is first determined by a leader, OPEC, and by an aggregate that represents non-OPEC producers. OPEC wants to increase a gain from oil price increase while keeping its market share relative to non-OPEC's share. An investment rule model is developed for OPEC crude oil supply capacity in response to non-OPEC's decision. In presence of oil price uncertainty, oil price threshold is derived above which it is optimal for OPEC to expand oil supply capacity since otherwise the increased supply of non-OPEC results in weakening of OPEC market share in the world oil market. In addition, a lower threshold price is derived below which OPEC triggers a capacity reduction to regain its otherwise forgone profits. A simulation is provided for calculating the capacity expansion and reduction thresholds.