• Journal of Information Technology Services
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• v.13 no.2
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• pp.151-161
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• 2014

KOSPI 200 index options market has the highest trading volume in the global options markets. The risk and return structure of options contracts are very complex. Volatility complicates options trading because volatility plays a central role in options pricing process. This study develops a trading system for KOSPI 200 index options trading using KOSPI 200 volatility index. We design a database system to handle the complex options information such as price, volume, maturity, strike price, and volatility using Oracle DBMS. We then develop options trading strategies to test how the volatility index is related to the prices of complicated options trading strategies. Back test procedure is presented with PL/SQL of Oracle DBMS. We simulate the suggested trading system using historical data set of KOSPI 200 index options from December 2008 to April 2012.

• 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.

• Journal of Service Research and Studies
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• v.9 no.1
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• pp.51-71
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• 2019

The purpose of this research is to analyze the ETF market, which has a large increase in the number of listed shares and the market capitalization, and to identify the investment effects of ETFs. The study procedure and method used to calculate the return and change trend of ETFs for the sample of the transaction information, the transaction amount, and the market capitalization for the period from 2010 to 2018, and performed correlation and regression analysis. As a result, the ETF's total return was 2.11%, the domestic underwriting market ETF yield was 2.39%, and the stock ETF yield was 2.59%, which was lower than the KOSPI 200 index and the KOSPI 200 index. Index ETF was 2.63%, followed by stock ETF and oversea underwriting market ETF. The problem with ETF investment is that the annual return of ETFs and domestic ETFs is as low as 2%, which is not enough for investors to expect more than 5%. The study contributes to the realization of the ETF by analyzing the actual effect of the investment and to establishing considerations when buying ETFs from the viewpoint of investors. The direction of the research is to accumulate more ETF data and present the investment direction precisely.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06d
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• pp.450-452
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• 2012

본 논문에서는 뉴스 기사의 효과적인 구독을 위한 뉴스리더의 개발에 대하여 기술한다. 뉴스리더는 RSS 형식의 뉴스 기사를 사용자의 기호에 따라 미리 지정한 시간대에 수신하며 저장하여 둠으로써 빠르게 사용자에게 보여줄 수 있다. 또한, 기사에 코스피 200 상장 기업에 해당하는 기업명이 나올 경우, 한국 거래소와 연동하여 해당 기업의 종합적인 주식 정보를 보여줌으로써 효용성을 높였다.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.513-522
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• 2014

Using the assumption that the price of a stock follows a geometric Brownian motion with constant volatility, Black and Scholes (BS) derived a formula that gives the price of a European call option on the stock as a function of the stock price, the strike price, the time to maturity, the risk-free interest rate, the dividend rate paid by the stock, and the volatility of the stock's return. However, implied volatilities of BS method tend to depend on the stock prices and the time to maturity in practice. To address this shortcoming, we estimate the implied volatility function as a function of the strike priceand the time to maturity for data consisting of the daily prices for KOSPI200 call options from January 2007 to May 2009 using support vector regression (SVR), the multiple additive regression trees (MART) algorithm, and ordinary least squaress (OLS) regression. In conclusion, use of MART or SVR in the BS pricing model reduced both RMSE and MAE, compared to the OLS-based BS pricing model.

• 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.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.1013-1024
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• 2015

We compare the performance of two representative estimation methods for the dynamic conditional correlation (DCC) GARCH model. The first method is the pairwise estimation which exploits partial information from the paired series, irrespective to the time series dimension. The second is the multi-dimensional estimation that uses full information of the time series. As a simulation for the comparison, we generate a multivariate time series similar to those observed in real markets and construct a DCC GARCH model. As an empirical example, we constitute various portfolios using real KOSPI 200 sector indices and estimate volatility and VaR of the portfolios. Through the estimated dynamic correlations from the simulation and the estimated volatility and value at risk (VaR) of the portfolios, we evaluate the performance of the estimations. We observe that the multi-dimensional estimation tends to be superior to pairwise estimation; in addition, relatively-uncorrelated series can improve the performance of the multi-dimensional estimation.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.461-475
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• 2021

Hidden Markov model (HMM) is a statistical model in which the system consists of two elements, hidden states and observable results. HMM has been actively used in various fields, especially for time series data in the financial sector, since it has a variety of mathematical structures. Based on the HMM theory, this research is intended to apply the domestic KOSPI200 stock index as well as the prediction of global stock indexes such as NIKKEI225, HSI, S&P500 and FTSE100. In addition, we would like to compare and examine the differences in results between the HMM and support vector regression (SVR), which is frequently used to predict the stock price, due to recent developments in the artificial intelligence sector.

• 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.

• 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.