• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.15-26
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• 2014

This study focus on a economic value of the Big Data technologies by real options model using big data technology company's stock price to determine the price of the economic value of incremental assessed value. For estimating stochastic process of company's stock price by big data technology to extract the incremental shares, Generalized Moments Method (GMM) are used. Option value for Black-Scholes partial differential equation was derived, in which finite difference numerical methods to obtain the Big Data technology was introduced to estimate the economic value. As a result, a option value of big data technology investment is 38.5 billion under assumption which investment cost is 50 million won and time value is a about 1 million, respectively. Thus, introduction of big data technology to create a substantial effect on corporate profits, is valuable and there are an effects on the additional time value. Sensitivity analysis of lower underlying asset value appear decreased options value and the lower investment cost showed increased options value. A volatility are not sensitive on the option value due to the big data technological characteristics which are low stock volatility and introduction periods.

• Journal of Korea Port Economic Association
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• v.34 no.1
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• pp.99-110
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• 2018

This study empirically examines simple methodology to quantify the risk resulted from the uncertainty of bunker price and foreign exchange rate, which cause main resources of the cost in shipping industry during the periods between $1^{st}$ of January 2010 and $31^{st}$ of January 2018. To shed light on the risk measurement in cash flows we tested GBM(Geometric Brownian Motion) frameworks such as the model with conditional heteroskedasticity and jump diffusion process. The main contribution based on empirical results are summarized as following three: first, the risk analysis, which is dependent on a single variable such as freight yield, is extended to analyze the effects of multiple factors such as bunker price and exchange rate return volatility. Second, at the individual firm level, the need for risk management in bunker price and exchange rate is presented as cash flow. Finally, based on the scale of the risk presented by the analysis results, the shipping companies are required that there is a need to consider what is appropriate as a means of risk management.

• Environmental and Resource Economics Review
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• v.24 no.4
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• pp.607-627
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• 2015

As observed and experienced in EU ETS, allowance price volatility is one of major concerns in decision making process for $CO_2$ abatement investment. The problem of linearly non-separable profits functions could emerge when one power company holds several power plants with different technology specifications. Under this circumstance, conventional analytical solution for investment option is no longer available, thereby calling for the development of numerical analysis. This paper attempts to develop a Monte-Carlo least squares model to analyze investment options for power companies under emission trading scheme regulations. Stochastic allowance price is considered, and simulation is performed to verify model performance.

• Korean Journal of Construction Engineering and Management
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• v.24 no.3
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• pp.3-11
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• 2023

This research examines the need for a quantitative and objective method of calculating baseline productivity in the construction industry, which is known for its high volatility in performance and productivity. The existing literature's baseline productivity calculation methods rely heavily on subjective criteria, limiting their effectiveness. Additionally, data collection methods such as the "Five-minute Rating" are costly and time-consuming, making it challenging to collect detailed data at construction sites. To address these issues, this study proposes an objective baseline calculation method using unimpacted productivity BP, a work check sheet to systematically record detailed data, and a data collection and utilization process that minimizes cost and time requirements. This paper also suggests using unimpacted productivity BP and comparative analysis to address the objectivity and reliability issues of existing baseline productivity calculation methods.

• The Korean Journal of Financial Management
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• v.23 no.2
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• pp.189-208
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• 2006

In this paper we are concerned with estimation of tail related risk measures for heteroscedastic financial time series and VaR limits that VaR tells us nothing about the potential size of the loss given. So we use GARCH-EVT model describing the tail of the conditional distribution for heteroscedastic financial series and adopt Expected Shortfall to overcome VaR limits. The main results can be summarized as follows. First, the distribution of stock return series is not normal but fat tail and heteroscedastic. When we calculate VaR under normal distribution we can ignore the heavy tails of the innovations or the stochastic nature of the volatility. Second, GARCH-EVT model is vindicated by the very satisfying overall performance in various backtesting experiments. Third, we founded the expected shortfall as an alternative risk measures.

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

• Environmental and Resource Economics Review
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• v.27 no.3
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• pp.495-520
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• 2018

Because of structural changes in the international oil market and the economy, it is widely recognized that the impact of oil shocks on the economy has weaken since the mid-1980s. This study tries to examine the validity of the recent perception about the relationship between oils shocks and the economy, estimating the time-varying effects of oil shocks on the Korean economy. The results show that the dynamic effects of oil shocks normalized to a one standard deviation has been relatively constant, in contrast to the recent perception and a number of existing studies. In addition, because the shape of impulse response functions at each point in time spanning from 1984:II to 2017:IV has not been changed significantly, it seems that the propagation mechanism of oil shocks also has not been substantially altered. These findings indicate that even though structural changes of the economy, such as the reduction in the share of oil consumption and the spread of high-efficiency energy technologies, have been rapidly progressed, it is not still enough to offset the negative effects of oil shocks. Rather, it seems that the recent perception about the shrinking effects of oil shocks is mainly due to the assumptions that do not reflect changes in the size of oil shocks. In particular, this problem appears more pronounced in the case of the typical a one standard deviation increase oil shock under homoskedasticity assumption, which is widely adopted in the most VAR analyses. Therefore, in estimating the effects of oil shocks on the economy, it is important to specify the correct model and normalization method, to reflect changes in the size of oil shocks.

• Korea Trade Review
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• v.43 no.3
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• pp.1-26
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• 2018

This study estimates the technical efficiency and total factor productivity(TFP) of and analyzes the relationship between TFP and exports for Korean manufacturing companies from 2000 to 2016. Specially, TFP is decomposed into Technical Change(TC), Technical Efficiency Change (TEC), and Sale Effect(SE), and compared between large and small enterprises. First, in the case of technical efficiency, the Korean economy has been very vulnerable to external shocks, such as the sharp decline following the 2008 financial crisis. The efficiency of the electronics, automobile, and machinery sectors is low and needs to be improved. In addition, the technological efficiency of large enterprises is higher than that of SMEs in most manufacturing sub-sectors except for non-ferrous metals. In the case of TFP, most changes are due to TC, and the effective combination of labor, capital and the effect of scale have little effect, suggesting that improvement of internal structure is urgent. In addition, volatility due to the impact of the financial crisis in 2008 was much larger in SMEs than in large companies, so external economic impacts are more greater for SMEs than large enterprises. The relationship between TFP decomposition factors and exports shows that TC has a positive effect only on exports of SMEs. Therefore, in order to increase exports, in the case of SMEs, R&D support to promote technological development is needed. In the case of large companies, it is necessary to establish differentiated strategies for each export market, competitor company, and item to link efficiency and scale effect of exports.