• The Korean Journal of Applied Statistics
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• v.18 no.1
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• pp.129-135
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• 2005

Representative observations are useful to express explicitly the distributional variation of the data by a few selected observations corresponding to the quantiles in the univariate situation. Jones and Rice(1992) extended it to the multidimensional case by the principal component based method. This study introduces a modified version of Jones and Rice exploiting the self-consistency of principal components in expressing the chosen observation vectors. Compared to that of Jones and Rice, the suggested method tends to provide with less susceptible representative observations to the sampling variation of the data and the resulted vectors benefits from the self-consistency.

• Journal of Life Science
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• v.26 no.11
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• pp.1237-1244
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• 2016

A comprehensive analysis of the genetic diversity and relationship of the cold-water fishery walleye pollock (Theragra chalcogramma), the most abundant economically important fishery resource in the East sea of Korea, has not been carried out, despite its importance in Korea. The present study assessed the genetic diversity and relationship between five walleye pollock populations (Korean population, Russian population, USA population, and Japanese populations) of T. chalcogramma using eight microsatellite DNA (msDNA) markers to provide the scientific data for the preservation and management of the Pollock fishery resource. The results of the analysis of 186 individuals of the Pollock revealed a range of 7.13-10.63 numbers of alleles (mean number of alleles=9.05). The means of observed heterozygosity ($H_O$), expected heterozygosity ($H_E$) were 0.732 and 0.698, respectively. The results of genetic distance, Pairwise $F_{ST}$, UPGMA (UPGMA: un-weighted pair-group method with an arithmetical average) (the phylogenetic tree), PCA (PCA: Principal Coordinate analysis) analysis pointed to significant differences between the Korean population, Russian population, USA population, and Japanese populations, although small (p<0.05). These results shed light on the genetic diversity and relationships of T. chalcogramma and can be utilized for research on the evaluation and conservation of Korean T. chalcogramma as genetic resources.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.