• Journal of the Korean Operations Research and Management Science Society
• /
• v.37 no.3
• /
• pp.135-150
• /
• 2012

This paper presents a portfolio model for a long-term power generation mix problem. The proposed portfolio model evaluates generation mix by considering the tradeoffs between the expected cost for power generation and its variability. Unlike conventional portfolio models measuring variance, we introduce Conditional Value-at-Risk (CVaR) in designing the variability with aims to considering events that are enormously expensive but are rare such as nuclear power plant accidents. Further, we consider uncertainties associated with future electricity demand, fuel prices and their correlations, and capital costs for power plant investments. To obtain an objective generation by each energy source, we employ the sample average approximation method that approximates the stochastic objective function by taking the average of large sample values so that provides asymptotic convergence of optimal solutions. In addition, the method includes Monte Carlo simulation techniques in generating random samples from multivariate distributions. Applications of the proposed model and method are demonstrated through a case study of an electricity industry with nuclear, coal, oil (OCGT), and LNG (CCGT) in South Korea.

• The Korean Journal of Applied Statistics
• /
• v.28 no.1
• /
• pp.115-122
• /
• 2015

This article is concerned with count time series taking values in non-negative integers. Along with the first order mean of the count time series, conditional variance (volatility) has recently been paid attention to and therefore various integer-valued GARCH(generalized autoregressive conditional heteroscedasticity) models have been suggested in the last decade. We introduce diverse integer-valued GARCH(INGARCH, for short) processes to count time series and a real data application is illustrated as a case study. In addition, zero inflated INGARCH models are discussed to accommodate zero-inflated count time series.

• Environmental and Resource Economics Review
• /
• v.13 no.2
• /
• pp.175-194
• /
• 2004

Volumes of research have been implemented to estimate and predict the oil price. These models, however, fail in accurately predicting oil price as a model composed of only a few observable variables is limiting. Unobservable variables and news that have been overlooked in past research, yet have a high likelihood of affecting the oil price. Hence, this paper analyses the news impact on the price. The standard GARCH model fails in capturing some important features of the data. The estimated news impact curve for the GARCH model, which imposes symmetry on the conditional variances, suggests that the conditional variance is underestimated for negative shocks and overestimated for positive shocks. Hence, this paper introduces the asymmetric or leverage volatility models, in which good news and bad news have different impact on volatility. They include the EGARCH, AGARCH, and GJR models. The empirical results showed that negative shocks introduced more volatility than positive shocks. Overall, the AGARCH and GJR were the best at capturing this asymmetric effect. Furthermore, the GJR model successfully revealed the shape of the news impact curve and was a useful approach to modeling conditional heteroscedasticity.

• Journal of the Korean earth science society
• /
• v.31 no.4
• /
• pp.301-312
• /
• 2010

Geochemical data have been regarded as one of the important environmental variables in the environmental management. Since they are often sampled at sparse locations, it is important not only to predict attribute values at unsampled locations, but also to assess the uncertainty attached to the prediction for further analysis. The main objective of this paper is to exemplify how indicator geostatistics can be effectively applied to geochemical data processing for providing decision-supporting information as well as spatial distribution of the geochemical data. A whole geostatistical analysis framework, which includes probabilistic uncertainty modeling, classification and risk analysis, was illustrated through a case study of cadmium mapping. A conditional cumulative distribution function (ccdf) was first modeled by indicator kriging, and then e-type estimates and conditional variance were computed for spatial distribution of cadmium and quantitative uncertainty measures, respectively. Two different classification criteria such as a probability thresholding and an attribute thresholding were applied to delineate contaminated and safe areas. Finally, additional sampling locations were extracted from the coefficient of variation that accounts for both the conditional variance and the difference between attribute values and thresholding values. It is suggested that the indicator geostatistical framework illustrated in this study be a useful tool for analyzing any environmental variables including geochemical data for decision-making in the presence of uncertainty.

• The Journal of Asian Finance, Economics and Business
• /
• v.3 no.3
• /
• pp.79-91
• /
• 2016

This paper is the first of its kind using a non-linear approach based on cross-correlation function (CCF) to investigate the information arrival hypothesis in crude palm oil (CPO) futures market. Based on daily data from 1986 to 2010, our empirical results reveal that: First, the volume of volatility is not a proxy of information flow. Second, dependence causality running from current return to future volume in conditional variance exhibit an asymmetric pattern of time span with different signs of correlation between price and volume series. This finding indicates the presence of noise traders' hypothesis of price-volume interaction in CPO futures market. Both findings suggest that this futures market is weak-form inefficiency. In terms of investors' behavior, they tend to change their expectations on current return based on errors made in previous trade in generating abnormal volume in the subsequent period. As implied, it is advisable for the investors devise their future trading strategies according to time span and changes of return.

• The Korean Journal of Applied Statistics
• /
• v.29 no.7
• /
• pp.1201-1212
• /
• 2016

Value at Risk (VaR) for market risk management is a favorite method used by financial companies; however, there are some problems that cannot be explained for the amount of loss when a specific investment fails. Conditional Tail Expectation (CTE) is an alternative risk measure defined as the conditional expectation exceeded VaR. Multivariate loss rates are transformed into a univariate distribution in real financial markets in order to obtain CTE for some portfolio as well as to estimate CTE. We propose multivariate CTEs using multivariate quantile vectors. A relationship among multivariate CTEs is also derived by extending univariate CTEs. Multivariate CTEs are obtained from bivariate and trivariate normal distributions; in addition, relationships among multivariate CTEs are also explored. We then discuss the extensibility to high dimension as well as illustrate some examples. Multivariate CTEs (using variance-covariance matrix and multivariate quantile vector) are found to have smaller values than CTEs transformed to univariate. Therefore, it can be concluded that the proposed multivariate CTEs provides smaller estimates that represent less risk than others and that a drastic investment using this CTE is also possible when a diversified investment strategy includes many companies in a portfolio.

• Journal of Wetlands Research
• /
• v.6 no.4
• /
• pp.45-57
• /
• 2004

This study analyzed the effect of land-use enhancement benefits with the flood safety which it is not quantified in the flood damage analysis, Korea. The land-use enhancement benefits mean the enhancement of land-use value according to the rise of flood safety of the protected area by the flood control projects and we performed the analysis of land-use enhancement benefits with the publicly announced land price which can objectively represent the land-use value of a specific area. We verified the statistical significance of the floating rate of land price according to the effects of flood control projects and the characteristics of a river through the analysis of variance. As a result of the verification, the increase of land-use value was represented by the net annual average floating rate of land price. The flood safety was classified as flood damage potential and flood prevention capacity. The flood damage potential was classified according to the rate of urbanization and flood prevention capacity was represented by the conditional annual non-exceedance probability obtained from the frequency analysis with uncertainty for the flood discharge. The study areas were small urban cities and we calculated the conditional annual non-exceedance probabilities of 200-year flood event for the levees constructed with the conditions of 10- and 50-year design frequency. The result was shown that the net annual average floating rate of land price would be raised nearly 5 times for 10%-increase of the conditional annual non-exceedance probability in small city areas.

• Proceedings of the Korean Geotechical Society Conference
• /
• 2003.03a
• /
• pp.123-130
• /
• 2003

The estimation of key soil properties and subsequent quantitative assessment of the associated uncertainties has always been an important issue in geotechnical engineering. It is well recognized that soil properties vary spatially as a result of depositional and post-depositional processes. The stochastic nature of spatially varying soil properties can be treated as a random field. A practical statistical approach that can be used to systematically model various sources of uncertainty is presented in the context of reliability analysis of slope stability Newly developed expressions for probabilistic characterization of soil properties incorporate sampling and measurement errors, as well as spatial variability and its reduced variance due to spatial averaging. Reliability analyses of the probability of slope failure using the different statistical representations of soil properties show that the incorporation of spatial correlation and conditional simulation leads to significantly lower probability of failure than obtained using simple random variable approach.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.26 no.9
• /
• pp.1218-1225
• /
• 2002

Large-scale vortical structure of a turbulent separation bubble affected by unsteady wake is essential to understand flow mechanisms in various fluid devices. A spoked-wheel type of wake generator provides unsteady wake, which modifies the turbulent separation bubble significantly by changing rotation directions and passing frequencies. A detailed mechanism of vortex shedding from the separation bubble with unsteady wake is analyzed by taking a conditional average with spatial box filtering, which spatially integrates measured signals at pre-determined wavelength. A convecting nature of the large-scale vortical structure is analyzed carefully. Spatial evolution of the large-scale vortical structure with frequency variance is also exemplified.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2001.01a
• /
• pp.325-329
• /
• 2001

This paper tests existence of precautionary saving motive under health uncertainty, using household level panel data from Korea. For this purpose, this paper considers a dynamic health capital model with health uncertainty and derives testable equations for changes in consumption and medical expenditures. Under this framework, households who face future health uncertainty will exhibit precautionary behavior by depressing consumption or increasing investment in health. To test this hypothesis, the paper uses the conditional variance of health as the direct measure of health uncertainty, obtained by estimating a multinomial logit model. Empirical results using the Korean Household Panel Study (KHPS, 1993 - 1997) suggest that Korean elderly households follow the precautionary behavior to insure against future health risk.