• Journal of Digital Convergence
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• 제13권12호
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• pp.151-160
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• 2015

As a lexical meaning, casino is defined as "a certified gambling house, equipped with recreational facilities such as dance and music, etc., where people play roulette or cards." Casinos started from 17th to 18th century for European nobility and their social meetings and established a casino industry framework in the United States in the 1930s. The success of the casino business leads to the increase of sales; it became very helpful for the local and national government revenues and also for the related incidental tasks. Casino operations include a variety of fields, such as general customer management, dealer game management, security, account management, currency exchange, re-exchange management, marketing management, comp management and placement management, etc. These operations should be organically connected to each other by information systems such as a groupware, ERP and Customer Relationship Management (CRM), etc. In addition, in order to effectively manage comprehensive entertainment service, including accommodation and tourism, it is necessary to develop an information system which supports casino business and collateral entertainment service, collects the data generated throughout the business and provides information about the situations of management. Thus, this study will propose a casino information system designed and implemented, considering these details.

• Economic and Environmental Geology
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• 제41권3호
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• pp.299-314
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• 2008

The NE-trending Honam shear zone is a broad, dextral strike-slip fault zone between the southern margin of the Okcheon Belt and the Precambrian Yeongnam Massif in South Korea and is parallel to the trend of Sinian deformation that is conspicuous in Far East Asia. In this paper, we report geochemical and isotopic(Sr and Nd) data of mylonitic quartz-muscovite Precambrian gneisses and surrounding foliated hornblende-biotite granitoids near the Myeongho area in the Yecheon Shear Zone, a representative segment of the Honam Shear Zone. Foliated hornblende-biotite granitoids commonly plot in the granodiorite field($SiO_2=61.9-67.1\;wt%$ and $Na_2O+K_2O=5.21-6.99\;wt%$) on $SiO_2$ vs. $Na_2O+K_2O$ discrimination diagram, whereas quartz-muscovite Precambrian orthogneisses plot in the granite field. The foliated hornblende-biotite granitoids are mostly calcic and calc-alkalic and are dominantly magnesian in a modified alkali-lime index(MALI) and Fe# [$=FeO_{total}(FeO_{total}+MgO)$] versus $SiO_2$ diagrams, which correspond with geochemical characteristics of Cordilleran Mesozoic batholiths. The foliated hornblende-biotite granitoids have molar ratios of $Al_2O_3/(CaO+Na_2O+K_2O)$ ranging from 0.89 to 1.10 and are metaluminous to weakly peraluminous, indicating I type. In contrast, Paleoproterozoic orthogneisses have peraluminous compositions, with molar ratios of $Al_2O_3/(CaO+Na_2O+K_2O)$ ranging from 1.11 to 1.22. On trace element spider diagrams normalized to the primitive mantle, the large ion lithophile element(LILE) enrichments(Rb, Ba, Th and U) and negative Ta-Nb-P-Ti anomalies of foliated hornblende-biotite granitoids and mylonitized quartz-muscovite gneisses in the Yecheon Shear Zone are features common to subduction-related granitoids and are also found in granitoids from a crustal source derived from the arc crust of active continental margin. ${\varepsilon}_{Nd}(T)$ and initial Sr-ratio ratios of foliated hornblende-biotite granitoids with suggest the involvement of upper crust-derived melts in granitoid petrogenesis. Foliated hornblende-biotite granitoids in the study area, together with the Yeongju Batholith, show not changing contents of specific elements(Ti, P, Zr, V and Y) from shear zone to the area near the shear zone. These results suggest that no volume changes and geochemical alterations in fluid-rich foliated hornblende-biotite granitoids may occur during deformation, which mass transfer by fluid flow into the shear zone is equal to the mass transfer out of the shear zone.

• Journal of Intelligence and Information Systems
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• 제16권2호
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• pp.19-32
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• 2010

Volatility plays a central role in both academic and practical applications, especially in pricing financial derivative products and trading volatility strategies. This study presents a novel mechanism based on generalized autoregressive conditional heteroskedasticity (GARCH) models that is able to enhance the performance of intelligent volatility trading systems by predicting Korean stock market volatility more accurately. In particular, we embedded the concept of the volatility asymmetry documented widely in the literature into our model. The newly developed Korean stock market volatility index of KOSPI 200, VKOSPI, is used as a volatility proxy. It is the price of a linear portfolio of the KOSPI 200 index options and measures the effect of the expectations of dealers and option traders on stock market volatility for 30 calendar days. The KOSPI 200 index options market started in 1997 and has become the most actively traded market in the world. Its trading volume is more than 10 million contracts a day and records the highest of all the stock index option markets. Therefore, analyzing the VKOSPI has great importance in understanding volatility inherent in option prices and can afford some trading ideas for futures and option dealers. Use of the VKOSPI as volatility proxy avoids statistical estimation problems associated with other measures of volatility since the VKOSPI is model-free expected volatility of market participants calculated directly from the transacted option prices. This study estimates the symmetric and asymmetric GARCH models for the KOSPI 200 index from January 2003 to December 2006 by the maximum likelihood procedure. Asymmetric GARCH models include GJR-GARCH model of Glosten, Jagannathan and Runke, exponential GARCH model of Nelson and power autoregressive conditional heteroskedasticity (ARCH) of Ding, Granger and Engle. Symmetric GARCH model indicates basic GARCH (1, 1). Tomorrow's forecasted value and change direction of stock market volatility are obtained by recursive GARCH specifications from January 2007 to December 2009 and are compared with the VKOSPI. Empirical results indicate that negative unanticipated returns increase volatility more than positive return shocks of equal magnitude decrease volatility, indicating the existence of volatility asymmetry in the Korean stock market. The point value and change direction of tomorrow VKOSPI are estimated and forecasted by GARCH models. Volatility trading system is developed using the forecasted change direction of the VKOSPI, that is, if tomorrow VKOSPI is expected to rise, a long straddle or strangle position is established. A short straddle or strangle position is taken if VKOSPI is expected to fall tomorrow. Total profit is calculated as the cumulative sum of the VKOSPI percentage change. If forecasted direction is correct, the absolute value of the VKOSPI percentage changes is added to trading profit. It is subtracted from the trading profit if forecasted direction is not correct. For the in-sample period, the power ARCH model best fits in a statistical metric, Mean Squared Prediction Error (MSPE), and the exponential GARCH model shows the highest Mean Correct Prediction (MCP). The power ARCH model best fits also for the out-of-sample period and provides the highest probability for the VKOSPI change direction tomorrow. Generally, the power ARCH model shows the best fit for the VKOSPI. All the GARCH models provide trading profits for volatility trading system and the exponential GARCH model shows the best performance, annual profit of 197.56%, during the in-sample period. The GARCH models present trading profits during the out-of-sample period except for the exponential GARCH model. During the out-of-sample period, the power ARCH model shows the largest annual trading profit of 38%. The volatility clustering and asymmetry found in this research are the reflection of volatility non-linearity. This further suggests that combining the asymmetric GARCH models and artificial neural networks can significantly enhance the performance of the suggested volatility trading system, since artificial neural networks have been shown to effectively model nonlinear relationships.