• 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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.409-412
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• 2006

펀드의 성과 평가는 펀드선택 의사결정시 가장 중요한 고려요인이다. 전통적인 펀드 성과평과 지표인 샤프비율이나 트레이너 비율은 운용비용과 거래비용 등과 같은 다양한 요소들을 반영하지 못하고 있다. 펀드의 여러 수익과 비용요인들을 반영하기 위해, 본 연구에서는 DEA를 이용하여 펀드의 상대적인 효율성을 측정하였다. 분석자료는 2005년 12월 31일까지 국내에 존재하는 3년 이상 운용된 67개의 장기펀드를 대상으로 분석하였다. 입력변수는 비용과 관련된 총 보수율, 월수익률의 표준편차, 펀드 규모를 나타내기 위한 평균설정좌수를 이용하였고 출력변수는 이익과 관련된 예상 월평균수익률과 시장포트폴리오의 월수익률을 상회한 기간의 비율을 사용하였다. 본 연구결과, DEA를 통해 수익률만으로는 평가할 수 없는 펀드의 운용능력을 평가할 수 있었다.

• The Korean Journal of Financial Studies
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• v.8 no.1
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• pp.1-20
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• 2002

이 논문에서는 1991년 1월부터 2000년 12월까지의 납세 후 CD수익률 자료와 소비자물가지수 자료를 사용하여 우리 나라 금융시장에서 단기적 피셔효과가 존재하는지를 검증하고자 시도하였다. Fama(1975)의 방법에 따라 3개월 물가상승률을 CD수익률에 관해 회귀분석한 결과, CD수익률이 미래 물가상승률의 예측치로서 충분한 역할을 하지 못한다는 결과를 얻었다. 단기적 피셔효과를 검증하기 위하여 CD수익률을 기대물가상승률에 관하여 회귀분석하였다. 기대물가상승률은 상수 및 시간추세와 계절성을 반영하는 부분과 확률적 부분으로 구분하고, 확률적 부분이 랜돔워크 모형에 따르는 경우와 AR(1) 모형에 따르는 경우에 대해 기대물가상승률을 구하였다. 랜돔워크모형에 의해 예측하든 AR(1)에 의해 예측하든 기대물가상승률의 회귀계수는 유의한 양(+)의 값이긴 1보다는 훨씬 작은 값으로 추정되었다. 이것은 우리나라의 CD수익률에 단기 피셔효과가 부분적으로만 존재하고 있다는 것을 의미한다. 그리고 AR(1)을 사용하여 예측한 기대물가상승률이 랜돔워크모형을 사용한 경우보다 나은 추정결과를 보여주고 있다.

• Journal of the Korean Society for Railway
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• v.13 no.4
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• pp.462-467
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• 2010

This paper uses MPI (Malmquist Production Index) method to provide basic information and policy direction derived to improve the productivity. This can identify whether or not the railroad service have excellent services, if there are structural problem, in which sector is experiencing a degree of productivity, in which sector should be set targets to some degree. According to the result, the productivity of Korea railway lines is measured 1.1 both publicity and profitability aspects. Changes in production technology were the most important causes. Strategic planning should consider to increase the productivity such as modernization of facilities and equipment, cost savings, and increase in productivity of employee and rail cars.

• Management & Information Systems Review
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• v.33 no.1
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• pp.45-63
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• 2014

A trading system is a computer trading program that automatically submits trades to an exchange. Mechanical a trading system to execute trade is spreading in the stock market. However, a trading system to trade a single asset might occur instability of the profit because payoff of this system is determined a asset movement. Therefore, it is necessary to develop a trading system that is trade two assets such as a pair trading that is to sell overvalued assets and buy the undervalued ones. The aim of this study is to propose a relative value based trading system designed to yield stable and profitable profits regardless of market conditions. In fact, we propose a procedure for building a trading system that is based on the rough set analysis of indicators derived from a price ratio between two assets. KOSPI 200 index futures and S&P 500 index futures are used as a data for evaluation of the proposed trading system. We intend to examine the usefulness of this model through an empirical study.

• The Korean Journal of Financial Studies
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• v.3 no.2
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• pp.157-185
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• 1996

본 연구는 한국증권시장에서 변동성의 정보비대칭효과를 조건부 이분산모형을 이용하여 검증하고자 하였다. 검증방법으로는 Engle과 Ng (1993)의 연구에 기초하여 정보반응곡선(News impact curve)으로 분석하였다. 분석자료로 1980년 부터 1995년 까지의 한국종합주가지수, 일별 초과수익률자료를 사용하였다. 정보반응곡선에 이용한 모형은 GARCH 모형, EGARCH 모형, TGARCH 모형, AGARCH 모형등 4개의 조건부 이분산 모형이다. 무조건 분산을 이용한 정보 반응곡선의 함수형태로 보면, 분산의 정보반응에 있어서 GARCH 모형은 대칭적으로 반응하며 나머지 조건부 이분산 모형인 EGARCH 모형, TGARCH 모형, 그리고 AGARCH 모형은 비대칭적으로 반응하는 모형임을 알 수 있었다. 실증분석결과 정보반응곡선을 통하여 악재(bad news)정보에 따라 예측하지 못한 주식수익률의 하락이 호재(good news)에 따른 예측하지 못한 주식수익률의 상승보다 더 큰 변동성을 발견할 수 있었다. 그러나 비대칭성의 크기는 그다지 큰 것으로 보이지 않았다. 모형적합성 검정에서도 4개의 조건부 이분산 모형은 모두 적합한 것으로 보인다. 그중에서도 EGARCH 모형과 TGARCH 모형이 상대적으로 주가예측력이 뛰어나 보인다. 그러나 변동성의 정보 비대칭반응을 통계적으로 유의적인 것으로 확인한 모형은 TGARCH모형 뿐이었다.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.299-308
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• 2022

Recent, energy transition policies are driving to increase in the number of small photovoltaic(PV) generators. It is difficult for system operators to accurately anticipate the amount of power generated from such small scale PV generation, and this may disrupt dispatch schedules and result in an increase in cost. The need for a Virtual Power Plant(VPP) is emerging as a way of resolving these problems, as it would integrate small-scale PV plants and eliminate uncertainty about the amount of power generated, control voltage, and provide power reserves. In this paper, the cost evaluation methods are described for determination of VPP cloud charges both Net Present Value(NPV) method and Profitability Index(PI) method, the calculated outcomes of the two types of cost evaluation methods are presented in detail. It seems we secure profitability as we get 1.22 of profitability index from calculation results, it may be attractive for the aggregator as NPV is enough for satisfying profitability.

• The Korean Journal of Financial Management
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• v.25 no.2
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• pp.69-101
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• 2008

We empirically investigate the option leverage property that should be priced under much more general conditions than the Black-Scholes assumptions and the option redundancy property that is based on the assumption that the underlying asset price follows a one-dimensional diffusion process and examine the systematic risk factors implied in the return dynamics of KOSPI 200 index options. We find that the option leverage pattern is similar to the theoretical result but the options are not redundant securities and in the nonlinear structure of option payoffs, the traders of KOSPI 200 index options price the systematic higher-moments and the negative volatility risk premium significantly affects delta-hedged gains, even after accounting for jump fears. But the empirical evidence on jump risk preference is less conclusive.

• Proceedings of the Korean Society of Crop Science Conference
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• 2021.10a
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• pp.119-119
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• 2021

• Journal of Digital Convergence
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• v.11 no.5
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• pp.159-168
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• 2013

This study analyze the business efficiency of securities company based on the 2011 performance of 29 securities firms which engage in domestic investment brokerage by applying a combination model of BSC and DEA. And we evaluate business state focused on efficiency which is based on logical system of BSC as business innovation method. The analysis of result is that companies with high customer efficiency index appeared that business efficiency composite index tended to be higher and we identified that customer perspective have an important factor to calculate business efficiency composite index of korea security company. In addition, based on the results of the efficiency analysis we analyze correlation analysis between traditional financial ratio and business efficiency composite index. We confirmed that company of high business efficiency level in terms of BSC have a good record in terms of profitability. BSC-DEA combination model expect to be utilized in security industry sector as well as other industrial sectors as good business indicator to determine the business efficiency and to be used a model can be evaluated the integrated firm valuation of tangible and intangible assets.