• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.805-812
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• 2020

High-frequency data are now prevalent in financial time series. As a functional data arising from high-frequency financial time series, we are concerned with the intraday volatility to which functional principal component analysis (FPCA) is applied in order to achieve a dimension reduction. A review on FPCA and R function is made and high-frequency KOSPI volatility is analysed as an application.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1163-1170
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• 2015

Various computational methods for obtaining volatilities for financial time series are reviewed and compared with each other. We reviewed model based GARCH approach as well as the data based method which can essentially be regarded as a smoothing technique applied to the squared data. The method for high frequency data is focused to obtain the realized volatility. A hybrid method is suggested by combining the model based GARCH and the historical volatility which is a data based method. Korea stock prices are analysed to illustrate various computational methods for volatilities.

• Communications for Statistical Applications and Methods
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• v.23 no.2
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• pp.163-177
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• 2016

We consider a jump diffusion process for high-frequency financial asset data. We apply the stationary bootstrapping to construct a bootstrap test for jumps. First-order asymptotic validity is established for the stationary bootstrapping of the jump ratio test under the null hypothesis of no jump. Consistency of the stationary bootstrap test is proved under the alternative of jumps. A Monte-Carlo experiment shows the advantage of a stationary bootstrapping test over the test based on the normal asymptotic theory. The proposed bootstrap test is applied to construct continuous-jump decomposition of the daily realized variance of the KOSPI for the year 2008 of the world-wide financial crisis.

• KDI Journal of Economic Policy
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• v.38 no.3
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• pp.53-77
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• 2016

Financial soundness in the household sector matters for financial stability and for the real economy. The level of household debt in Korea raises concern about the financial soundness of the household sector due to its size, growth rate and quality. Against this backdrop, we assess the financial vulnerability of borrowers based on an analysis of credit bureau (CB) data, in which the actual credit activities of most individuals are recorded at a high frequency in Korea. We construct over-indebtedness indicators from the CB data and then assess the predictability of forthcoming defaults. Based on the over-indebtedness indicators, we show how borrowers are distributed in terms of over-indebtedness and how the over-indebted differ from average borrowers in terms of their characteristics. Furthermore, we show how the aggregate credit risk in the household sector would change under macroeconomic distress by analyzing how each borrower's credit quality would be affected by adverse shocks. The findings of this paper may contribute to assessing household debt vulnerability and to enhancing regulatory and supervisory practices for financial stability.

• Journal of Nutrition and Health
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• v.36 no.10
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• pp.1083-1093
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• 2003

Efficient financial management is a critical factor in achieving school foodservice goals. The objective of this study was to suggest efficient financial management practices in secondary school foodservices. In pursuit of this objective, we first identified performance indexes for measuring the success of financial management. Second, we suggested financial management standards, financial data classification methods and a report system. Last, we analyzed operating ratios with the financial data of self-operated and contract-managed school food services. The data were collected through an open-ended questionnaire from 10 middle/high school foodservices in Seoul and Kyeonggi Provincial during on-sites visits and interviews with dieticians and managers. Student participation, sales goals, re-contract frequency and number and cost of disaster loss were identified as the performance indexes for financial management. Income statements were compiled by identifying and classifying financial data. Total revenues consisted of subsidies, meal sales, other revenue and interest. Expenditures consisted of purchased food, salaries and wages, utility costs, office supplies, kitchen supplies, purchased services, company overhead indirect costs, facility investment and maintenance, facility usage expenses, employee benefits and miscellaneous. Mean price of a meal was 2,326 won at self-operated foodservices when the subsidies were included as revenues and 2,360 won at contract-managed foodservices. When including the subsidies as revenues, the operating ratios of self-operated foodservice showed that the food cost percentage was 66.9％, labor cost 23.2％, operation cost 9.9％ and profit 0％. The correspond figures at contract-managed foodservices were 57.6％, 21.5％, 15.3％, and 5.5％, respectively. Food costs in self-operated foodservices was significantly higher than that for contract-managed foodservices, however, facility investment and maintenance and facility usage expenses at self-operated foodservices was significantly lower than those for contract-managed foodservices. Based on this study, the methodology and classification system of financial data was found to be applicable to assess the financial structure of school foodservices.

• Quantitative Bio-Science
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• v.37 no.2
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• pp.73-79
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• 2018

As high frequency (HF, for short) time series is now prevalent in the presence of real time big data, volatility computations based on traditional ARCH/GARCH models need to be further developed to suit the high frequency characteristics. This article reviews realized volatilities (RV) and multivariate GARCH (MGARCH) to deal with high frequency volatility computations. As a (functional) infinite dimensional models, the fARCH and fGARCH are introduced to accommodate ultra high frequency (UHF) volatilities. The fARCH and fGARCH models are developed in the recent literature by Hormann et al. [1] and Aue et al. [2], respectively, and our discussions are mainly based on these two key articles. Real data applications to domestic UHF financial time series are illustrated.

• Korean Journal of Human Ecology
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• v.16 no.3
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• pp.563-576
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• 2007

The purpose of this study is to analyze the financial performance according to the economic environment change. The data of 4,577 households in 2003 and 3994 households in 2000 is from the Korean Labor and Income Panel Study. The data were analyzed by various statistical methods such as frequency, mean-test, Duncan's multiple range test, k-mean cluster analysis. Findings were as follows; First, the classified household financial strategy types were Residual(44.3%), Financial Assets(24.0%), Informal Institutional(19.7%), Diversified Portfolio(7.6%), Real Estate(4.5%). Second, the criteria of classification of the financial strategies were relative, not absolute. Third, the rate of economic growth was high and the index of the current money was low in 2000. Fourth, households that employed a diversified portfolio strategy had the greatest net wealth.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1199-1215
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• 2011

Relaxing an unrealistic assumption of a representative percolation model, this paper demonstrates that herd behavior leads to a high increase in volatility but not trading volume, in contrast with information flows that give rise to increases in both volatility and trading volume. Although detecting herd behavior has posed a great challenge due to its empirical difficulty, this paper proposes a new methodology for detecting trading days with herding. Furthermore, this paper suggests a herd-behavior-stochastic-volatility model, which accounts for herding in financial markets. Strong evidence in favor of the model specification over the standard stochastic volatility model is based on empirical application with high frequency data in the Korean equity market, strongly supporting the intuition that herd behavior causes excess volatility. In addition, this research indicates that strong persistence in volatility, which is a prevalent feature in financial markets, is likely attributed to herd behavior rather than news.

• Journal of Families and Better Life
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• v.9 no.2
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• pp.19-35
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• 1991

The Purpose of this study is to search a tendency of financial disagreement and to identify the variables influencing on financial disagreement of husbands and wives. for this purpose, reviewing literatures and empirical research were conducted. The sample was selected from the husbands and wives living in Masan, Changwon and Jinhae. Among 336 respondents. 111 husbands and 225 wives were finally selected as datum sources. The data were analyzed by the statistical method such as frequency distribution percentile ,ANOVA. Peason's correlation and Regression analysis. The main results were as follows; 1) Most husbands and wives showed th high level of financial disagreements. 2) As for the related variables, socio-demographic and psychological variables such as husband's education. family income, communication and financial management behavior had turned out to be significant on the financial disagreement of wives. As for the husband's financial disagreement, husband's education and family income had a significant influence. 3) There were negative correlation between the financial management behavior and the financial disagreement of husbands and wives(r=-0.22. -0.35). 4) the family characteristics which were the best predictors of financial disagreement included; family income, financial management behaviro of husbands and wives.

• The Korean Journal of Applied Statistics
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• v.31 no.5
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• pp.667-675
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• 2018

When a financial time series consists of daily (closing) returns, traditional volatility models such as autoregressive conditional heteroskedasticity (ARCH) and generalized ARCH (GARCH) are useful to figure out daily volatilities. With high frequency returns in a day, one may adopt various multivariate GARCH techniques (MGARCH) (Tsay, Multivariate Time Series Analysis With R and Financial Application, John Wiley, 2014) to obtain intraday volatilities as long as the high frequency is moderate. When it comes to the ultra high frequency (UHF) case (e.g., one minute prices are available everyday), a new model needs to be developed to suit UHF time series in order to figure out continuous time intraday-volatilities. Aue et al. (Journal of Time Series Analysis, 38, 3-21; 2017) proposed functional GARCH (fGARCH) to analyze functional volatilities based on UHF data. This article introduces fGARCH to the readers and illustrates how to estimate fGARCH equations using UHF data of KOSPI and Hyundai motor company.