• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.905-914
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• 2014

In this study, we examine hedging effectiveness of KODEX200 ETF and KOSPI200 futures with respect to KOSPI200 spot or KODEX200 ETF using naive, the risk-minimization models and the VECM. The sample period covers from January 5. 2010 to October 31. 2013. Daily prices of the KOSPI200 spot, KOSPI200 futures and KODEX200 were used in this study. The results are summarized ans follows. First, this study show that there is cointegration relationship among KOSPI200 spot, futures and KODEX200 ETF market. Second, there is no significant difference in hedging performance among the models. Finally, hedged position of KOSPI200 cash(unhedged position)-KODEX200 ETF(hedge vehicle) or KODEX200 ETF-KOSPI200 futures seems to improve hedging performance compared to KOSPI200 cash-KOSPI200 futures. This implies that the portfolio managers may be encouraged to use the former than the latter.

• Management & Information Systems Review
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• v.32 no.3
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• pp.285-297
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• 2013

In this study, price discovery among the KOSPI200 markets(KOSPI200 spot, KOSPI200 Futures and The ETFs) is investigated using the vector error correction model(VECM). The main findings are as follows. KODEX200(KOSEF200), KOSPI200 spot and Futures are cointegrated in most cases. Daily data from KODEX200(KOSEF200), KOSPI200 spot and KOSPI200 futures show that the movements of the three markets are interrelated. Specially, KODEX200 contains the most information, followed by the KOSPI200 spot and futures markets. KODEX200 contribute to the price discovery process. Namely KODEX200 plays a more dominant role in price discovery than the KOSPI200 spot and futures.

• Management Science and Financial Engineering
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• v.19 no.1
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• pp.37-41
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• 2013

This study presents an application of stochastic model for limit order book (LOB) dynamics to Korean Stock Index Futures (KOSPI 200 Futures). Since KOSPI 200 futures market is widely known as one of the most liquid markets in the world, direct application of an existing model is hardly possible. Therefore, we modified an existing model to successfully model and predict the dynamics of extremely liquid KOSPI 200 futures market.

• Management & Information Systems Review
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• v.31 no.4
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• pp.287-307
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• 2012

We empirically examine the price discovery dynamics among the VKOSPI, the KOSPI200 spot, and the KOSPI200 futures markets. The analysis employs the vector-autoregression, Granger causality, impulse response function, and variance decomposition using both daily data from 2009. 04. 13 to 2011. 12. 30 and 1 minute data from the bull market, bear market, and the flat period. The main results are as follows; First, the lead lag relationships between KOSPI200 spot(futures) yield VKOSPI returns could not be found from the daily data analysis. But KOSPI200 spot(futures) have a predictive power for VKOSPI from 1 minute data. Especially KOSPI200 spot(futures) and VKOSPI show the bi-directional effects to each other during the return rising period Second, We chose the VAR(1) the model in daily data but adopt the VAR(3) model in the one minute data to determine the lead lag time. We know that there is predictability during the very short period Third, Spot returns and futures returns makes no difference in daily data results. According to the one minite data results, VKOSPI returns have a predictive power for KOSPI200 spot return, but have no predictive power for KOSPI200 futures return.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.187-201
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• 2011

KOSPI200 index is the Korean stock price index consisting of actively traded 200 stocks in the Korean stock market. Its base value of 100 was set on January 3, 1990. The Korea Exchange (KRX) developed derivatives markets on the KOSPI200 index. KOSPI200 index futures market, introduced in 1996, has become one of the most actively traded indexes markets in the world. Traders can make profit by entering a long position on the KOSPI200 index futures contract if the KOSPI200 index will rise in the future. Likewise, they can make profit by entering a short position if the KOSPI200 index will decline in the future. Basically, KOSPI200 index futures trading is a short-term zero-sum game and therefore most futures traders are using technical indicators. Advanced traders make stable profits by using system trading technique, also known as algorithm trading. Algorithm trading uses computer programs for receiving real-time stock market data, analyzing stock price movements with various technical indicators and automatically entering trading orders such as timing, price or quantity of the order without any human intervention. Recent studies have shown the usefulness of artificial intelligent systems in forecasting stock prices or investment risk. KOSPI200 index data is numerical time-series data which is a sequence of data points measured at successive uniform time intervals such as minute, day, week or month. KOSPI200 index futures traders use technical analysis to find out some patterns on the time-series chart. Although there are many technical indicators, their results indicate the market states among bull, bear and flat. Most strategies based on technical analysis are divided into trend following strategy and non-trend following strategy. Both strategies decide the market states based on the patterns of the KOSPI200 index time-series data. This goes well with Markov model (MM). Everybody knows that the next price is upper or lower than the last price or similar to the last price, and knows that the next price is influenced by the last price. However, nobody knows the exact status of the next price whether it goes up or down or flat. So, hidden Markov model (HMM) is better fitted than MM. HMM is divided into discrete HMM (DHMM) and continuous HMM (CHMM). The only difference between DHMM and CHMM is in their representation of state probabilities. DHMM uses discrete probability density function and CHMM uses continuous probability density function such as Gaussian Mixture Model. KOSPI200 index values are real number and these follow a continuous probability density function, so CHMM is proper than DHMM for the KOSPI200 index. In this paper, we present an artificial intelligent trading system based on CHMM for the KOSPI200 index futures system traders. Traders have experienced on technical trading for the KOSPI200 index futures market ever since the introduction of the KOSPI200 index futures market. They have applied many strategies to make profit in trading the KOSPI200 index futures. Some strategies are based on technical indicators such as moving averages or stochastics, and others are based on candlestick patterns such as three outside up, three outside down, harami or doji star. We show a trading system of moving average cross strategy based on CHMM, and we compare it to a traditional algorithmic trading system. We set the parameter values of moving averages at common values used by market practitioners. Empirical results are presented to compare the simulation performance with the traditional algorithmic trading system using long-term daily KOSPI200 index data of more than 20 years. Our suggested trading system shows higher trading performance than naive system trading.

• Journal of Korean Institute of Industrial Engineers
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• v.38 no.4
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• pp.237-243
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• 2012

This paper proposes quantitative investment strategies for KOSPI200 index futures using VKOSPI and control chart. Stochastic control chart is employed to decide when to take a position as well as what position out of long and short should be taken by monitoring whether VKOSPI or difference of VKOSPI touches the control limit lines. The strategies include 4 approaches, which are traditional control chart and 2-Area control chart coupled with VKOSPI and its difference, respectively. Computational experiments using real KOSPI200 futures index for recent 3 years are conducted to show the excellence of the proposed investment strategies under control chart framework.

• The Korean Journal of Financial Management
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• v.20 no.1
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• pp.191-212
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• 2003

Recently, there is a growing body of literature that suggests that information inefficiency is one of the causes of the asymmetric volatility. If this explanation for the asymmetric volatility is appropriate, then innovations, such as the introduction of futures, may be expected to impact the asymmetric volatility of stock market. As transaction costs and margin requirements in the futures market are lower than those in the spot market, new information is transmitted to futures prices more quickly and affects spot prices through arbitrage trading with spots. Also, the merit of the futures market may attract noise traders away from the spot market to the futures market. This study examines the impact of futures on the asymmetry of stock market volatility. If the asymmetric volatility is significant lower post-futures and exist in the futures market, it has validity that the asymmetric volatility is caused by information inefficiency in the spot market. The data examined are daily logarithmic returns on KOSPI 200 stock price index from January 4, 1993 to December 26, 2000. To examine the existence of the asymmetric volatility in the futures market, logarithmic returns on KOSPI 200 futures are used from May 4, 1996 to December 26, 2000. We used a conditional mode of TGARCH(threshold GARCH) of Glosten, Jagannathan and Runkel(1993). Pre-futures the spot market exhibits significant asymmetric responses of volatility to news and post-futures asymmetries are significantly lower, irrespective of bear market and bull market. The results suggest that the introduction of stock index futures has an effect on the asymmetric volatility of the spot market and are inconsistent with leverage being the sole explanation of asymmetry. However, it is found that the volatility of futures is not so asymmetric as expected.

• Asia-Pacific Journal of Business
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• v.9 no.4
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• pp.85-97
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• 2018

This paper investigates factors underlying a decrease in trading values in KOSPI 200 futures/options market on the basis of the current state of the markets. Among the factors that could affect trading values in KOSPI 200 derivatives market, we focus on the market activity of underlying assets as it has an impact on the trading of financial derivatives. Trading value and volatility are designated as market activity and the empirical results confirm that the market activity of the underlying assets is significant in explaining the decrease in trading values in KOSPI 200 futures/options market. To figure out fundamental reasons of the decrease in trading values in this market, we examine mitigation of home bias and decrease in leverage incentives as they are presumed to have influence on KOSPI 200 index market. As the global and local financial environment is time-varying, the degree of home bias and the leverage demand also changes. It implies that institutional change and/or policy effort to promote the trading of KOSPI 200 financial derivatives should be made taking into account the fact that considerable portion of the change in trading values in financial derivatives market depends on the state of the market.

• Management & Information Systems Review
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• v.38 no.4
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• pp.79-93
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• 2019

This study analyzed the information effect of KOSPI200 market and KOSPI200 futures market and volume synchronized probability of informed trading (VPIN). The data period is 760 days from July 8, 2015 to August 9, 2018, and the intraday trading data is used based on the trading period of the KOSPI 200 Index. The findings of the empirical analysis are as follows. First, as a result of regression analysis of the same parallax, when the level of VPIN is high, the return and volatility of KOSPI200 are high. Second, the KOSPI200 returns before and after the VPIN measurement and the return of the KOSPI200 future had a positive relationship with the VPIN. The cumulative returns of KOSPI200 futures were positive for about 15 minutes.Finally, we find that portfolios with high levels of VPIN showed high KOSPI200 and KOSPI200 futures return. These results confirmed the applicability of VPIN as a trading strategy index. The above results suggest that KOSPI200 and KOSPI200 futures markets will be able to explore volatility and price changes, and also be useful indicators of financial market risk.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.734-737
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• 2004

When the margin level is set relatively low, margin violation probability increases and the default probability of the futures market rises. On the other hand, if the margin level is set high, the margin violation probability decreases, but the futures market becomes less attractive to hedgers as the investor's opportunity cost increases. In this paper, we investigate whether the movement of KOSPI200(Korea Composite Stock Price Index 200) futures daily prices can be modeled with the extreme value theory. Base on this investigation, we examine the validity of the margin level set by the extreme value theory. Computational results are presented to compare the extreme value distribution and the empirical distribution of margin violation in KOSPI200. Some observations and implications drawn from the computational experiment are also discussed.