• The Korean Journal of Financial Management
• /
• v.21 no.2
• /
• pp.211-233
• /
• 2004

This purpose of paper is to propose a European option pricing formula when the rate of return follows the leptokurtic distribution instead of normal. This distribution explains well the volatility smile and furthermore the option prices calculated under the leptokurtic distribution are shown to be closer to the market prices than those of Black-Scholes model. We make an estimation of the implied volatility and kurtosis to verify the fitness of the pricing formula that we propose here.

• Journal of Intelligence and Information Systems
• /
• v.16 no.3
• /
• pp.77-97
• /
• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.9 no.2
• /
• pp.15-21
• /
• 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 Journal of the Korea Contents Association
• /
• v.19 no.7
• /
• pp.36-46
• /
• 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.

• Journal of Distribution Science
• /
• v.13 no.5
• /
• pp.53-60
• /
• 2015

Purpose - The purpose of this paper is to shed light on the importance of the slope and curvature of the volatility curve implied in option prices in the KOSPI 200 options index. A number of studies examine the implied volatility curve, however, these usually focus on cross-sectional characteristics such as the volatility smile. Contrary to previous studies, we focus on time-series characteristics; we investigate correlation dynamics among slope, curvature, and level of the implied volatility curve to capture market information embodied therein. Our study may provide useful implications for investors to utilize current market expectations in managing portfolios dynamically and efficiently. Research design, data, and methodology - For our empirical purpose, we gathered daily KOSPI200 index option prices executed at 2:50 pm in the Korean Exchange distribution market during the period of January 2, 2004 and January 31, 2012. In order to measure slope and curvature of the volatility curve, we use approximated delta distance; the slope is defined as the difference of implied volatilities between 15 delta call options and 15 delta put options; the curvature is defined as the difference between out-of-the-money (OTM) options and at-the-money (ATM) options. We use generalized method of moments (GMM) and the seemingly unrelated regression (SUR) method to verify correlations among level, slope, and curvature of the implied volatility curve with statistical support. Results - We find that slope as well as curvature is positively correlated with volatility level, implying that put option prices increase in a downward market. Further, we find that curvature and slope are positively correlated; however, the relation is weakened at deep moneyness. The results lead us to examine whether slope decreases monotonically as the delta increases, and it is verified with statistical significance that the deeper the moneyness, the lower the slope. It enables us to infer that when volatility surges above a certain level due to any tail risk, investors would rather take long positions in OTM call options, expecting market recovery in the near future. Conclusions - Our results are the evidence of the investor's increasing hedging demand for put options when downside market risks are expected. Adding to this, the slope and curvature of the volatility curve may provide important information regarding the timing of market recovery from a nosedive. For financial product distributors, using the dynamic relation among the three key indicators of the implied volatility curve might be helpful in enhancing profit and gaining trust and loyalty. However, it should be noted that our implications are limited since we do not provide rigorous evidence for the predictability power of volatility curves. Meaning, we need to verify whether the slope and curvature of the volatility curve have statistical significance in predicting the market trough. As one of the verifications, for instance, the performance of trading strategy based on information of slope and curvature could be tested. We reserve this for the future research.

• The Journal of Information Systems
• /
• v.30 no.3
• /
• pp.21-45
• /
• 2021

Purpose This paper analyzed the impacts of domestic stock market by a global pandemic such as COVID-19. We investigated how the overall pattern of the stock market changed due to the impact of the COVID-19 pandemic. In particular, we analyzed in depth the pattern of stock price, as well, tried to find what factors affect on stock market index(KOSPI) in the healthcare industry due to the COVID-19 pandemic. Design/methodology/approach We built a data warehouse from the databases in various industrial and economic fields to analyze the changes in the KOSPI due to COVID-19, particularly, the changes in the healthcare industry centered on bio-medicine. We collected daily stock price data of the KOSPI centered on the KOSPI-200 about two years before and one year after the outbreak of COVID-19. In addition, we also collected various news related to COVID-19 from the stock market by applying text mining techniques. We designed four experimental data sets to develop decision tree-based prediction models. Findings All prediction models from the four data sets showed the significant predictive power with explainable decision tree models. In addition, we derived significant 10 to 14 decision rules for each prediction model. The experimental results showed that the decision rules were enough to explain the domestic healthcare stock market patterns for before and after COVID-19.

• Communications of the Korean Mathematical Society
• /
• v.23 no.1
• /
• pp.141-151
• /
• 2008

In this paper, we derive the nonlinear equation for European option pricing containing liquidity risk which can be defined as the inverse of the partial derivative of the underlying asset price with respect to the amount of assets traded in the efficient market. Numerical solutions are obtained by using finite element method and compared with option prices of KOSPI200 Stock Index. These prices computed with liquidity risk are considered more realistic than the prices of Black-Scholes model without liquidity risk.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2020.11a
• /
• pp.751-754
• /
• 2020

본 논문은 지수를 추종하기 위해 유전 알고리즘에 몬테카를로 샘플링을 추가한 방법을 제안한다. 몬테카를로 샘플링을 통해 효율적으로 축소된 탐색공간을 탐험하는 유전 알고리즘은 최적의 종목들을 선택한다. 제안된 방법을 KOSPI200 지수 추종에 대하여 실험하였다. 제안된 방법이 몬테카를로 샘플링을 사용하지 않는 유전 알고리즘에 비해 지수 추종 오차가 더 낮고 더 빠르게 수렴하는 것을 보여주었다.

• Journal of the Korean Data and Information Science Society
• /
• v.22 no.1
• /
• pp.37-47
• /
• 2011

Stock price index option market has various investment strategies that have been developed. Specially, arbitrage strategies are very important to be efficient in option market. The purpose of this study is to improve profit using rough set and Box spread by using past option trading data. Option trading data was based on an actual stock exchange market tick data ranging from 2001 to 2006. Validation process was carried out by transferring the tick data into one-minute intervals. Box spread arbitrage strategies is low risk but low profit. It can be accomplished by back-testing of the existing strategy of the past data and by using rough set, which limit the time line of dealing. This study can make more stable profits with lower risk if control the strategy that can produces a higher profit module compared to that of the same level of risk.

• Journal of Convergence for Information Technology
• /
• v.10 no.4
• /
• pp.46-54
• /
• 2020

This study aims to develop and analyze the performance of a selective option straddle strategy based on forecasted volatility to improve the weakness of typical straddle strategy solely based on negative volatility risk premium. The KOSPI 200 option volatility is forecasted by the SVM model combined with the asymmetric volatility spillover effect. The selective straddle strategy enters option position only when the volatility is forecasted downwardly or sideways. The SVM model is trained for 2008-2014 training period and applied for 2015-2018 testing period. The suggested model showed improved performance, that is, its profit becomes higher and risk becomes lower than the benchmark strategies, and consequently typical performance index, Sharpe Ratio, increases. The suggested model gives option traders guidelines as to when they enter option position.