• Title/Summary/Keyword: 코스피200선물

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The Relationship among Returns, Volatilities, Trading Volume and Open Interests of KOSPI 200 Futures Markets (코스피 200 선물시장의 수익률, 변동성, 거래량 및 미결제약정간의 관련성)

  • Moon, Gyu-Hyen;Hong, Chung-Hyo
    • The Korean Journal of Financial Management
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    • v.24 no.4
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    • pp.107-134
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    • 2007
  • This paper tests the relationship among returns, volatilities, contracts and open interests of KOSPI 200 futures markets with the various dynamic models such as granger-causality, impulse response, variance decomposition and ARMA(1, 1)-GJR-GARCH(1, 1)-M. The sample period is from July 7, 1998 to December 29, 2005. The main empirical results are as follows; First, both contract change and open interest change of KOSPI 200 futures market tend to lead the returns of that according to the results of granger-causality, impulse response and variance decomposition with VAR. These results are likely to support the KOSPI 200 futures market seems to be inefficient with rejecting the hypothesis 1. Second, we also find that the returns and volatilities of the KOSPI 200 futures market are effected by both contract change and open interest change of that due to the results of ARMA(1,1)-GJR-GARCH(1,1)-M. These results also reject the hypothesis 1 and 2 suggesting the evidences of inefficiency of the KOSPI 200 futures market. Third, the study shows the asymmetric information effects among the variables. In addition, we can find the feedback relationship between the contract change and open interest change of KOSPI 200 futures market.

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Hedging effectiveness of KOSPI200 index futures through VECM-CC-GARCH model (벡터오차수정모형과 다변량 GARCH 모형을 이용한 코스피200 선물의 헷지성과 분석)

  • Kwon, Dongan;Lee, Taewook
    • Journal of the Korean Data and Information Science Society
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    • v.25 no.6
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    • pp.1449-1466
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    • 2014
  • In this paper, we consider a hedge portfolio based on futures of underlying asset. A classical way to estimate a hedge ratio for a hedge portfolio of a spot and futures is a regression analysis. However, a regression analysis is not capable of reflecting long-run equilibrium between a spot and futures and volatility clustering in the conditional variance of financial time series. In order to overcome such defects, we analyzed KOSPI200 index and futures using VECM-CC-GARCH model and computed a hedge ratio from the estimated conditional covariance-variance matrix. In real data analysis, we compared a regression and VECM-CC-GARCH models in terms of hedge effectiveness based on variance, value at risk and expected shortfall of log-returns of hedge portfolio. The empirical results show that the multivariate GARCH models significantly outperform a regression analysis and improve hedging effectiveness in the period of high volatility.

Using correlated volume index to support investment strategies in Kospi200 future market (거래량 지표를 이용한 코스피200 선물 매매 전략)

  • Cho, Seong-Hyun;Oh, Kyong Joo
    • Journal of the Korean Data and Information Science Society
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    • v.24 no.2
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    • pp.235-244
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    • 2013
  • In this study, we propose a new trading strategy by using a trading volume index in KOSPI200 futures market. Many studies have been conducted with respect to the relationship between volume and price, but none of them is clearly concluded. This study analyzes the economic usefulness of investment strategy, using volume index. This analysis shows that the trading volume is a preceding index. This paper contains two objectives. The first objective is to make an index using Correlated Volume Index (CVI) and second objective is to find an appropriate timing to buy or sell the Kospi200 future index. The results of this study proved the importance of the proposed model in KOSPI200 futures market, and it will help many investors to make the right investment decision.

A Study on the Cross Hedge Performance of KOSPI 200 Stock Index Futures (코스피 200 주가지수선물을 이용한 교차헤지 (cross-hedge))

  • Hong, Chung-Hyo;Moon, Gyu-Hyun
    • The Korean Journal of Financial Management
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    • v.23 no.1
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    • pp.243-266
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    • 2006
  • This paper tests cross hedging performance of the KOSPI 200 stock index futures to hedge the downside risk of the KOSPI, KOSPI 200 and KOSDAQ50 spot market. For this purpose we introduce the minimum variance hedge model, bivariate GARCH(1,1) and EGARCH(1,1) model as hedge models. The main results are as follows; First, we find that the direct hedge performance of KOSPI 200 index futures is better than those of indirect hedge performance. second, in case or cross hedge performance the hedge effect of KOSPI 200 stock index futures market against KOSPI 200 stock index spot market is relatively better than those of KOSPI 200 index futures against KOSPI and KOSDAQ spot position. Third, for the out-sample, hedging effectiveness of the risk-minimization with constant hedge ratios is higher than those of the time varying bivariate GARCH(1,1) and EGARCH(1,1) model. In conclusion, investors are encouraged to use simple risk-minimization model rather than the time varying hedge models like GARCH and EGARCH model to hedge the position of the Korean stock index cash markets.

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Do the Futures and Spot Markets Respond Differently to the News? : An Empirical Study of KOSPI200 Futures Market (선물 및 현물시장은 뉴스에 대해 동일하게 반응하는가? : 코스피200 선물시장에 대한 실증적 연구)

  • Cho, Dam
    • The Korean Journal of Financial Management
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    • v.23 no.2
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    • pp.85-107
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    • 2006
  • This paper investigates whether the futures market responds to the news more sensitively and uses more diverse information than the spot market. The sensitivity to the news is measured by the coefficients of the model which regresses the daily changes in the futures prices to the daily changes in the theoretical prices computed from spot prices using the spot-futures parity. The diversity of news is measured by the mean range differences ($\overline{RD}$), mean hi-price differences($\overline{HD}$) and mean low-price differences. The data in this paper is the closing prices of the nearest-to-maturity and the second-nearest-to-maturity contracts of the KOSPI 200 index futures. As the estimates of the relative sensitivity of the futures prices($^{\beta}$) for the whole-period sample are not significantly different from 1, the sensitivity of two markets to the news are not different. However, $\hat{\beta}$ of the most recent period(Nov. 2002 to Dec. 2005) are strongly different from 1. And, in the most recent period, the futures price changes for the good news, which is defined as the price increase of KOSPI of more than 1.5% in a day, show additional sensitivity. Since the mean range different which measures the relative diversity of information used, are not significantly different from 0 for the whole-period and subperiod samples, and this can be interpreted that the futures market does not use more diverse information than the spot market. However, the mean high-price difference, which measures the relative diversity of good news, are significantly different from 0 for the nearest-maturity contracts in the whole-period and subperiod samples. This evidence supports that the futures prices reflects more diverse good news which brings price increase in the market.

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Development and Performance Analysis of Predictive Model for KOSPI 200 Index using Recurrent Neural Networks (순환 신경망 기술을 이용한 코스피 200 지수에 대한 예측 모델 개발 및 성능 분석 연구)

  • Kim, Sung Soo;Hong, Kwang Jin
    • 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.

Technical Trading Rules for Bitcoin Futures (비트코인 선물의 기술적 거래 규칙)

  • Kim, Sun Woong
    • 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.

Using rough set to develop a volatility reverting strategy in options market (러프집합을 활용한 KOSPI200 옵션시장의 변동성 회귀 전략)

  • Kang, Young Joong;Oh, Kyong Joo
    • Journal of the Korean Data and Information Science Society
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    • v.24 no.1
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    • pp.135-150
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    • 2013
  • This study proposes a novel option strategy by using characteristic of volatility reversion and rough set algorithm in options market. Until now, various research has been conducted on stock and future markets, but minimal research has been done in options market. Particularly, research on the option trading strategy using high frequency data is limited. This study consists of two purposes. The first is to enjoy a profit using volatility reversion model when volatility gap is occurred. The second is to pursue a more stable profit by filtering inaccurate entry point through rough set algorithm. Since options market is affected by various elements like underlying assets, volatility and interest rate, the point of this study is to hedge elements except volatility and enjoy the profit following the volatility gap.

Net Buying Ratios by Trader Types and Volatility in Korea's Financial Markets (투자자별 순매수율과 변동성: 한국 금융시장의 사례)

  • Yoo, Shiyong
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.15 no.1
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    • pp.189-195
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    • 2014
  • In this research, we investigate the relationship between volatility and the trading volumes of trader types in the KOSPI 200 index stock market, futures market, and options market. Three types of investors are considered: individual, institutional, and foreign investors. The empirical results show that the volatility of the stock market and futures market are affected by the transaction information from another market. This means that there exists the cross-market effect of trading volume to explain volatility. It turns out that the option market volatility is not explained by any trading volume of trader types. This is because the option market volatility, VKOSPI, is the volatility index that reflects traders' expectation on one month ahead underlying volatility. Third, individual investors tend to increase volatilities, whereas institutions and foreign investors tend to stabilize volatilities. These results can be used in the areas of investment strategies, risk management, and financial market stability.

Fuzzy Support Vector Machine for Pattern Classification of Time Series Data of KOSPI200 Index (시계열 자료 코스피200의 패턴분류를 위한 퍼지 서포트 벡타 기계)

  • Lee, S.Y.;Sohn, S.Y.;Kim, C.E.;Lee, Y.B.
    • 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.