• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.297-308
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• 2020

We focus on the functional autoregressive conditional heteroscedasticity (fARCH) modelling to analyze intraday volatilities based on high frequency financial time series. Multivariate volatility models are investigated to approximate fARCH(1). A formula of multi-step ahead volatilities for fARCH(1) model is derived. As an application, in implementing fARCH(1), a choice of appropriate time interval for the intraday return is discussed. High frequency KOSPI data analysis is conducted to illustrate the main contributions of the article.

• Journal of Family Resource Management and Policy Review
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• v.3 no.1
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• pp.105-121
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• 1999

The major purpose of this study is to examine the effect of the extracurricular lesson expenses on the Burden of Household Expenditure. The data are collected from 537 housewives with a child aged 18(2nd grade in high school) in Seoul. The statistics used for the data analysis are frequency, percentage, crosstables, one way ANOVA, and multiple regression. The statistic softwear used for this study is SPSS. The Result on a base of empirical analyses follow: 1. Respondentg’s expenditure on extracurricular lesson fees is about 430,000 won per month, which is about 28% of the living cost. 2. There are four types of the methods preparing extracurricular lesson fees: ‘Frugal Type’, ‘Excess Type’, ‘Composit Type’, ‘Self-supportable Type’. 3. Region, the living cost, sex of children, net worth, and the mother’s education show statistically significant effect on the extracutticular lesson expenses. 4. The regression analyses incicate that Burden of Household Expenditure increased by 1.9 point according to 1% increase in expenditure on extracurridular lesson fees. The higher budget share of expenditure on extracurricular fees, the higher level of Burden of Household Expenditure.

• Journal of the Korean Society of Food Culture
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• v.14 no.2
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• pp.131-137
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• 1999

The purpose of this study is to investigate the drinking patterns and behaviors of Yangsan College students. Data were collected by a self-administered survey from the subjects, of which male students were 336 and female 165. The results of this study were as follows: 1. With regard to attitude toward drinking, 93.1% of the male subjects and 84.3% female subjects reported to have favor for drinking while only 9.3% of the subjects against drinking. 2. Those who reported to have at least a drink everyday were 13.3% of the subjects. For drinking frequency subjects who reported once in two or three day were most popular(21.9%). The frequency of drinking alcohol was associated positively with amount of discretionary money the students have. 3. For amount of drinking, 42.5% of subjects responded that they were able to drink soju at least one bottle per occasion. Data showed a high positive correlation between drinking frequency and financial costs they spent to drink. 4. The reasons subjects gave to drink included social gatherings after school or student activities (40.1%), change of mood(16.5%), and personal cerebration(16.5%). 5. The most popular place for the subject to go for a drink was neighborhood pubs(43.5%), followed by bar or pubs at downtown(28.3%) and nearby campus(12.2%).

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.99-109
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• 2021

This study aims to examine the causal links between the fiscal components, i.e., government expenditures (GE) and government revenues (GR), and their impact on the economic growth of the Association of Southeast Asian Nations (ASEAN) region. This analysis considered secondary panel data from 1990 to 2019 at an annual frequency. The data is obtained from the Asian Development Bank (ADB) and World Bank Database. A panel cointegration and panel DH causality (Dumitrescu and Hurlin) approach was employed on financial data at an annual frequency from 1990 to 2019. The findings from panel unit root and panel cointegration tests demonstrate that, at first, all the variables are stationary and cointegrated. The panel ARDL disclosed that GE has a long-run connection with GDP, is significantly and positively associated with economic growth in the long run, whereas GR is significant in the short run. The contribution of GE is high in sustaining economic growth as compared to GR. Also, cointegration regression disclosed that GE is more sensitive toward GDP, while GR is less elastic. Lastly, the findings reveal that bidirectional causality exists between GE and GR variables. These results have policy implications for sustainable economic growth in the ASEAN region.

• Journal of the Korean Home Economics Association
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• v.36 no.7
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• pp.57-67
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• 1998

This study was to compare the financial status between elderly households - retired vs employed. The sample obtained from 1994 KHPS, and consisted of 628 Korean aged households who are currently married. Statistics employed to analyze the data are mean, frequency, percentile, t-test, and relative-ratio. The results of this study were as follows ; In income sources, earned income was majority of employed households, but the percent of unearned income was greater than retired households. While the percent of cloth, education, recreation expenditures were high in employed, and medical, housing expenditures wee high percentage in retired. The percentage of real asset(housing) was majority of total asset in two groups. And the percentage of safe liquid asset of retired households was relatively higher than employed households.

• Journal of Internet of Things and Convergence
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• v.8 no.4
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• pp.85-96
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• 2022

The purpose of this study was to investigate how the management strategies and organizational culture required in the digital economy have an effect on business performance. It provided basic data on management strategies and organizational culture necessary to approach as a digital leading country. For data collection, a survey was conducted from March 1 to May 30, 2022 for companies located in J province and engaged in industries related to the digital economy. The survey was conducted online and non-face-to-face, and a total of 225 companies participated in the survey. For statistical analysis, frequency analysis, exploratory factor analysis and reliability analysis, cluster analysis, independent sample t-test, and multiple regression analysis were performed. The research results are as follows. First, organizational culture was classified into high and low groups according to preference in innovation oriented, relationship oriented, task oriented, and hierarchical oriented. Second, the 4 types of organizational culture showed differences in prospectors strategy, analyzers strategy, defenders strategy, differentiation strategy, cost leadership strategy, financial performance, and non-financial performance according to preference. Third, management strategies affecting financial performance were found to be analyzers strategy, differentiation strategy, prospectors strategy, and cost leadership strategy. Fourth, management strategies affecting non-financial performance were found to be differentiation strategy, defenders strategy, analysis strategy, offensive strategy, cost leadership strategy, and focus strategy. Fifth, organizational culture affecting financial performance was found to be task oriented. Sixth, organizational culture affecting non-financial performance was found to be innovation oriented and relationship oriented. Through these studies, it is expected that the economy will be revitalized in the domestic market and a growth ecosystem that can take a new leap forward is created in the global market.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.111-118
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• 2021

The purpose of this paper is to study the impact of trade openness on foreign direct investment (FDI) inflows into Vietnam, an emerging country with relatively high trade openness in recent years. The study used the vector autoregression (VAR) model to examine the impact of trade openness on FDI in Vietnam, in the period from 2005 to 2019. The research data are time-series data, with quarterly frequency, from 2005:Q4 to 2019:Q3. The FDI data were collected by International Financial Statistics. The data of trade openness were calculated based on Vietnam's export, import, and GDP data collected by the General Statistics Office of Vietnam. The estimated result shows that the trade openness has a positive effect on FDI. The current FDI is heavily influenced by FDI in the past with an average explanation of 74%. The main findings indicate that trade openness has a positive effect on FDI inflows into Vietnam. The findings also show that FDI in Vietnam is significantly affected by the shocks of the FDI itself in the past. The findings of the study suggest the Vietnamese Government improves the quality of trade openness and FDI, continues and maintains economic relations with other countries to increase trade openness.

• Wind and Structures
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• v.21 no.5
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• pp.537-564
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• 2015

Excessive motions in buildings cause occupants to become uncomfortable and nervous. This is particularly detrimental to the tenants and ultimately the owner of the building, with respect to financial considerations. Serviceability issues, such as excessive accelerations and inter-story drifts, are more prevalent today due to advancements in the structural systems, strength of materials, and design practices. These factors allow buildings to be taller, lighter, and more flexible, thereby exacerbating the impact of dynamic responses. There is a growing need for innovative and effective techniques to reduce the serviceability responses of these tall buildings. The current study considers a case study of a real building to show the effectiveness and robustness of the TLD in reducing the coupled lateral-torsional motion of this high-rise building under wind loading. Three unique multi-modal TLD systems are designed specifically to mitigate the torsional response of the building. A procedure is developed to analyze a structure-TLD system using High Frequency Force Balance (HFFB) test data from the Boundary Layer Wind Tunnel Laboratory (BLWTL) at the University of Western Ontario. The effectiveness of the unique TLD systems is investigated. In addition, a parametric study is conducted to determine the robustness of the systems in reducing the serviceability responses. Three practical parameters are varied to investigate the robustness of the TLD system: the height of water inside the tanks, the amplitude modification factor, and the structural modal frequencies.