• The Korean Journal of Financial Management
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• v.17 no.1
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• pp.181-202
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• 2000

본 연구는 지수선물 시장에서 호가스프레드에 영향을 줄 수 있는 요인변수를 탐색하였다. 호가스프레드는 1996년 5월 3일부터 1997년 7월 31일까지 일중 4시간 5분의 거래시간을 5분 간격으로 나누어 49개의 시간대별 잔량을 구하여 호가스프레드를 계산하였으며, 요인변수는 주문 거래자료를 이용하여 산출하였다. 분석결과는 다음과 같다. 첫째로, 호가스프레드 측정결과 개장직후 10분과 폐장직전 10분간의 호가스프레드가 다른 시간대보다 크게 나타났다. 우리나라 주가지수선물시장에서도 이상의 두 시간대에서는 거래자들이 현저히 높은 정보불균형이 있었고, 역선택과정이 심한 것으로 보여진다. 이는 McInish와 Wood(1992) 및 Jang과 Lee(1995) 그리고 Daigler(1997)의 U자형 패턴과 유사하게 나타났다. 둘째로, 거래빈도, 총주문량은 호가스프레드에 유의적인 음(-)의 영향을 주어 호가스프레드를 줄이는데 정보적 역할을 하고 있었던 것으로 생각된다. 그리고 주문빈도 및 변동성과 수익률이 모두 호가스프레드에 유의적인 양(+)의 영향을 주고 있었다. 회귀분석결과 관찰자료로 총주문량, 거래빈도가 유동성변수로서 의미가 있었고, 묵시적 거래비용을 줄여줄 수 있을 것이라 보여진다. 한편 주문빈도는 정보탐색을 위한 허수주문으로 여겨진다. 우리나라 선물시장에서는 투자자들이 가격 변동성에 대한 보상을 원하고 있었다. 일반적으로 투자자들은 가격위험하에서는 거래 체결을 원하지 않기 때문에 이러한 점이 호가스프레드를 커지게 하였던 원인으로 보여진다.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.2
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• pp.15-21
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• 2014

The long memory properties of the hedge ratio for stock and futures have not been systematically investigated by the extant literature. To investigate hedge ratio' long memory, this paper employs a data set including KOSPI200 and S&P500. Coakley, Dollery, and Kellard(2008) employ a data set including a stock index and commodities foreign exchange, and suggested the S&P500 to be a fractionally integrated process. This paper firstly estimates hedge ratios with two dynamic models, BEKK(Bollerslev, Engle, Kroner, and Kraft) and diagonal-BEKK, and tests the long memory of hedge ratios with Geweke and Porter-Hudak(1983)(henceforth GPH) and Lo's modified rescaled adjusted range test by Lo(1991). In empirical results, two hedge ratios based on KOSPI200 and S&P500 show considerably significant long memory behaviours. Thus, such results show the hedge ratios to be stationary and strongly reject the random walk hypothesis on hedge ratios, which violates the efficient market hypothesis.

• The Korean Journal of Financial Management
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• v.22 no.1
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• pp.71-91
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• 2005

The object or this paper is to analyze the rollover effect on KOSPI 200 index option prices. Especially we analyze the implied volatilities of the options that became the near maturity options as the old one expired. For this analysis, a panel data of KOSPI 200 Index Option Prices from year 1999 to year 2001 were used, and following results were obtained. First, after controlling for the underlying index returns, strike prices and other pricing factors, the call option prices tend to decrease while the put option prices tend to increase during the week of expiry. Second, if one concentrates on the daily price changes, call option prices tend to go up on Thursday (as the old options expire), and then experience a price decrease on the following day, while the reverse is true for the put options. These results imply that the option prices are affected by some of the market micro-structure effects such as whether the option is the near maturity option. We conjecture that the reason for this is related to the undervaluation of KOSPI 200 futures. The results from this paper have implications on the timing of option trades. If one wants to buy put options, and/or sell call options, he has better off by executing his intended trades before the old options expire. On the other hand, if one wants to buy call options, and/or sell put options, hi has better off by executing his intended trades after the expiry.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.6
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• pp.23-29
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• 2017

Due to the success of Wealthfront, Betterment, etc., there is a growing interest in RoboAdvisor that is an automated asset allocation methodology globally. RoboAdvisor minimizes human involvement in managing assets, thereby reducing the costs of using services and eliminating human psychological factors. In this paper, we developed a predictive model for the KOSPI 200 Futures Index using deep learning, in order to replace the existing technical analysis technique. And the proposed model confirmed that When the KOSPI 200 Gift Index is small, it can be used to predict direction and price of index. In combination with the existing technical analysis, It is confirmed that the proposed models combining with existing technical analyses and can be applied to the RoboAdvisor Service in the future.

• The Korean Journal of Financial Studies
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• v.5 no.1
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• pp.191-212
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• 1999

1997년에 우리 나라는 외환충격으로 인한 금융위기 속에서 시장가격이 급격하게 변동하였다. 이로 인해 차익거래를 가능하게 하는 차입과 대출이 크게 제약되었고, 이것은 시장간 균형관계에 중요한 영향을 줄 수 있다. 이에 이러한 금융위기에서도 주요 시장간의 균형관계가 유지되었는지를 검정하는 것이 이 연구의 목적이다. 분석자료로 KOSPI 200 현물 종가 및 선물 결제가격, 연간 회사채 수익률, 양도성 예금 연간이자율, 기준환율의 일일 자료를 사용하였다. 1996년 5월 3일부터 1998년 5월 21일까지의 기간을 외환충격에 의한 금융위기 전, 중, 후의 3단계로 구분하여 각 단계별로 백터오차수정모형 분석과 충격반응분석을 하였다. 금융위기 이전인 제1단계에서는 5개 내생변수간의 균형관계가 존재하였다. 금융위기가 급속하게 진행된 제2단계에서는 균형관계가 존재하지 않았다. 그러나 주가지수, 주가지수 선물가격 및 기준환율 변수를 내생변수로 하고, 나머지 변수를 외생변수로 분석한 경우에는 균형관계가 존재하였다. 금융위기 진정단계인 제3단계에서는 5개 내생변수간의 균형관계가 성립하였다.

• Journal of Convergence for Information Technology
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• v.11 no.5
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• pp.94-103
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• 2021

This study aims to propose technical trading rules for Bitcoin futures and empirically analyze investment performance. Investment strategies include standard trading rules such as VMA, TRB, FR, MACD, RSI, BB, using Bitcoin futures daily data from December 18, 2017 to March 31, 2021. The trend-following rules showed higher investment performance than the comparative strategy B&H. Compared to KOSPI200 index futures, Bitcoin futures investment performance was higher. In particular, the investment performance has increased significantly in Sortino Ratio, which reflects downside risk. This study can find academic significance in that it is the first attempt to systematically analyze the investment performance of standard technical trading rules of Bitcoin futures. In future research, it is necessary to improve investment performance through the use of deep learning models or machine learning models to predict the price of Bitcoin futures.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.52-56
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• 2004

The Information of classification and estimate about KOSPI200 index`s up and down in the stock market becomes an important standard of decision-making in designing portofolio in futures and option market. Because the coming trend of time series patterns, an economic indicator, is very subordinate to the most recent economic pattern, it is necessary to study the recent patterns most preferentially. This paper compares classification and estimated performance of SVM(Support Vector Machine) and Fuzzy SVM model that are getting into the spotlight in time series analyses, neural net models and various fields. Specially, it proves that Fuzzy SVM is superior by presenting the most suitable dimension to fuzzy membership function that has time series attribute in accordance with learning Data Base.

• Management & Information Systems Review
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• v.35 no.2
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• pp.273-284
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• 2016

The objective of this study is to analyze the market efficiency in the futures markets. Although many previous studies have investigated market efficiency between spot and futures prices, that with different maturities has not been studied in the futures markets extensively. For our objective, this paper examines KOSPI200 stock index future market with different maturities. We analyze the dynamic serial relationship of the difference of basis between nearest-month contract and next nearest-month contract using dynamic regression analysis suggested by Kawamoto and Hamori(2011) Using the data from 2000. 1 to 2013. 12, the major empirical findings are as follows: First. the mean and standard deviation of basis of next nearest-month contract is bigger than those of nearest-month contract. Second, the t-period basis of nearest-month contract can be explained by (t-1)period basis of that. Third, the basis spread of t-period and (t-1)period have negative affect on the return of underlying assets. This result is very reasonable because two basis spreads are derived from same underlying assets. Finally, basis information of next nearest-month contract can be used for the prediction of nearest-month contract and spot market return.

• The Korean Journal of Financial Management
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• v.25 no.2
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• pp.165-190
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• 2008

The degree of informational asymmetry relating to the expiration of index derivatives is usually increased as an expiration day of index derivatives approaches. The increase in the degree of informational asymmetry may have some effects on trading behavior of investors. To examine what the effects look like, 'life cycle of index derivatives' in this study is defined as three adjacent periods around expiration day: pre-expiration period(a week before the expiration day), post-expiration period(a week after the expiration day), and remaining period. It is inspected whether stock investor's trading behavior is changed according to the life cycle of KOSPI200 derivatives and what the reason of the changing behavior is. We have four results. First, trading behavior of each investor group is categorized into three patterns: ㄱ-pattern, L-pattern and U-pattern. The level of trading activity is low for pre-expiration period and normal for other periods in the ㄱ-pattern. L-pattern means that the level of trading activity is high for post-expiration period and normal for other periods. In the U-pattern, the trading activity is reduced for remaining period compared to other periods. Second, individual investors have ㄱ-pattern of trading large stocks according to the life cycle of KOSPI200 index futures while they show U-pattern according to the life cycle of KOSPI200 index options. Their trading behavior is consistent with the prediction of Foster and Viswanathan(1990)'s model for strategic liquidity investors. Third, trading pattern of foreign investors in relation to life cycle of index derivatives is partially explained by the model, but trading pattern of institutional investors has nothing to do with the predictions of the model.

• The Journal of the Korea Contents Association
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• v.19 no.7
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• pp.36-46
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• 2019

If stock market is efficient, any well-devised trading rule can't consistently outperform the average stock market returns. This study aims to verify whether the strategy based on bid-ask volume information can beat the stock market. I suggested a day trading strategy using order imbalance indicator and empirically analyzed its profitability with the KOSPI 200 index futures data from 2001 to 2018. Entry rules are as follows: If BSI is over 50%, enter buy order, otherwise enter sell order, assuming that stock price rises after BSI is over 50% and stock price falls after BSI is less than 50%. The empirical results showed that the suggested trading strategy generated very high trading profit, that is, its annual return runs to minimum 71% per annum even after the transaction costs. The profit was generated consistently during 18 years. This study also improved the suggested trading strategy applying the genetic algorithm, which may help the market practitioners who trade the KOSPI 200 index futures.