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

• Communications for Statistical Applications and Methods
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• v.16 no.2
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• pp.249-264
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• 2009

Equity-indexed annuities(EIAs) provide their customers with the greater of either the return linked to the underlying index or the minimum guaranteed return. Insurance companies have developed EIAs to attract customers reluctant to buy traditional fixed annuities because of low returns and also reluctant to buy mutual funds for fear of the high volatility in the stock market. This paper proposes a new type of EIA embedded with an outside barrier option with flexible monitoring period in order to increase its participation rate. It also derives an explicit pricing formula for this proposed product, and discusses numerical examples to show relationships among participation rate, barrier level, index volatility and correlation.

• The Korean Journal of Financial Management
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• v.22 no.2
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• pp.91-105
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• 2005

Using Campa and Chang's Expectations Hypothesis model, We test the expectations hypothesis in the term structure of volatilities in options on KOSPI 200 by using daily dosing prices from January 1999 to December 2003. In particular, it addresses whether long-dated volatilities are consistent with expected future short-dated volatilities, assuming rational expectation. Our results do not support the expectations hypothesis : long-term volatilities rise relative to short-term volatilities, but the increases are not matched as predicted by the expectations hypothesis. In addition, an increase in the current long-term volatilities relative to the current short-term volatilities is followed by at a random.

• International Area Studies Review
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• v.14 no.2
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• pp.25-40
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• 2010

Accurate forecasting of volatility is of considerable interest in financial volatility research, particularly in regard to portfolio allocation, option pricing and risk management because volatility is equal to market risk. So, we attempted to delineate a model with good ability to forecast and identified stylized features of volatility, with a focus on volatility persistence or long memory in the Australian futures market. In this context, we assessed the long-memory property in the volatility of index futures contracts using three conditional volatility models, namely the GARCH, IGARCH and FIGARCH models. We found that the FIGARCH model better captures the long-memory property than do the GARCH and IGARCH models. Additionally, we found that the FIGARCH model provides superior performance in one-day-ahead volatility forecasts. As discussed in this paper, the FIGARCH model should prove a useful technique in forecasting the long-memory volatility in the Australian index futures market.

• The Korean Journal of Financial Management
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• v.24 no.2
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• pp.41-79
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• 2007

This study examines the expiration-day effects of stock index futures and options in the Korean stock market. The so-called 'expiration-day effects', which are the abnormal stock price movements on derivatives expiration days, arise mainly from cash settlement. Index arbitragers have to bear the risk of their positions unless they liquidate their index stocks on the expiration day. If many arbitragers execute large buy or sell orders on the expiration day, abnormal trading volumes are likely to be observed. If a lot of arbitragers unwind positions in the same direction, temporary trading imbalances induce abnormal stock market volatility. By contrast, if some information arrives at market, the abnormal trading activity must be considered a normal process of price discovery. Stoll and Whaley(1987) investigated the aggregate price and volume effects of the S&P 500 index on the expiration day. In a related study, Stoll and Whaley(1990) found a similarity between the price behavior of stocks that are subject to program trading and of the stocks that are not. Thus far, there have been few studies about the expiration-day effects in the Korean stock market. While previous Korean studies use the KOSPI 200 index data, we analyze the price and trading volume behavior of individual stocks as well as the index. Analyzing individual stocks is important for two reasons. First, stock index is a market average. Consequently, it cannot reflect the behavior of many individual stocks. For example, if the expiration-day effects are mainly related to a specific group, it cannot be said that the expiration of derivatives itself destabilizes the stock market. Analyzing individual stocks enables us to investigate the scope of the expiration-day effects. Second, we can find the relationship between the firm characteristics and the expiration-day effects. For example, if the expiration-day effects exist in large stocks not belonging to the KOSPI 200 index, program trading may not be related to the expiration-day effects. The examination of individual stocks has led us to the cause of the expiration-day effects. Using the intraday data during the period May 3, 1996 through December 30, 2003, we first examine the price and volume effects of the KOSPI 200 and NON-KOSPI 200 index following the Stoll and Whaley(1987) methodology. We calculate the NON-KOSPI 200 index by using the returns and market capitalization of the KOSPI and KOSPI 200 index. In individual stocks, we divide KOSPI 200 stocks by size into three groups and match NON-KOSPI 200 stocks with KOSPI 200 stocks having the closest firm characteristics. We compare KOSPI 200 stocks with NON-KOSPI 200 stocks. To test whether the expiration-day effects are related to order imbalances or new information, we check price reversals on the next day. Finally, we perform a cross-sectional regression analysis to elaborate on the impact of the firm characteristics on price reversals. The main results seem to support the expiration-day effects, especially on stock index futures expiration days. The price behavior of stocks that are subject to program trading is shown to have price effects, abnormal return volatility, and large volumes during the last half hour of trading on the expiration day. Return reversals are also found in the KOSPI 200 index and stocks. However, there is no evidence of abnormal trading volume, or price reversals in the NON-KOSPI 200 index and stocks. The expiration-day effects are proportional to the size of stocks and the nearness to the settlement time. Since program trading is often said to be concentrated in high capitalization stocks, these results imply that the expiration-day effects seem to be associated with program trading and the settlement price determination procedure. In summary, the expiration-day effects in the Korean stock market do not exist in all stocks, but in large capitalization stocks belonging to the KOSPI 200 index. Additionally, the expiration-day effects in the Korean stock market are generally due, not to information, but to trading imbalances.

• The Korean Journal of Financial Management
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• v.11 no.1
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• pp.191-232
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• 1994

본 연구는 앞으로 우리나라에 증권선물 옵션거래제도가 도입될 것에 대비하여 선물규제체계와 선물회계제도를 연구하는데 촛점을 두고 있다. 선물규제법체계 및 시장규제의 동향에 대해서는 미국, 영국, 일본 등 선물거래가 급속도로 발전하고 있는 금융선진국의 경험적 사례를 사적으로 연구하고 선물규제에 관한 제반의 문제점을 파악함으로서 앞으로 우리나라에 선물거래가 도입될 경우 이들 문제점에 대해 어떻게 대응할 것인가를 모색하였다. 특히 시장규제에 관해서는 1996년에 도입이 예정되어 있는 주가지수선물거래의 도입이 현물 주식시장에 미치는 영향을 집중적으로 분석하고 규제상의 고려사항을 제시하였다. 한편 우리나라에 증권선물거래가 도입되면 이에 대응하여 선물거래의 회계처리기준과 시가정보의 공시기준이 마련되어야 한다. 본 연구에서는 선물거래의 회계처리에 있어서 손익의 인식기준 및 헷지회계의 기준을 설정함에 있어서의 고려사항을 제시함과 동시에 선물거래의 공시 기준설정시의 고려사항을 아울러 제시하고 있다.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.19-32
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• 2010

Volatility plays a central role in both academic and practical applications, especially in pricing financial derivative products and trading volatility strategies. This study presents a novel mechanism based on generalized autoregressive conditional heteroskedasticity (GARCH) models that is able to enhance the performance of intelligent volatility trading systems by predicting Korean stock market volatility more accurately. In particular, we embedded the concept of the volatility asymmetry documented widely in the literature into our model. The newly developed Korean stock market volatility index of KOSPI 200, VKOSPI, is used as a volatility proxy. It is the price of a linear portfolio of the KOSPI 200 index options and measures the effect of the expectations of dealers and option traders on stock market volatility for 30 calendar days. The KOSPI 200 index options market started in 1997 and has become the most actively traded market in the world. Its trading volume is more than 10 million contracts a day and records the highest of all the stock index option markets. Therefore, analyzing the VKOSPI has great importance in understanding volatility inherent in option prices and can afford some trading ideas for futures and option dealers. Use of the VKOSPI as volatility proxy avoids statistical estimation problems associated with other measures of volatility since the VKOSPI is model-free expected volatility of market participants calculated directly from the transacted option prices. This study estimates the symmetric and asymmetric GARCH models for the KOSPI 200 index from January 2003 to December 2006 by the maximum likelihood procedure. Asymmetric GARCH models include GJR-GARCH model of Glosten, Jagannathan and Runke, exponential GARCH model of Nelson and power autoregressive conditional heteroskedasticity (ARCH) of Ding, Granger and Engle. Symmetric GARCH model indicates basic GARCH (1, 1). Tomorrow's forecasted value and change direction of stock market volatility are obtained by recursive GARCH specifications from January 2007 to December 2009 and are compared with the VKOSPI. Empirical results indicate that negative unanticipated returns increase volatility more than positive return shocks of equal magnitude decrease volatility, indicating the existence of volatility asymmetry in the Korean stock market. The point value and change direction of tomorrow VKOSPI are estimated and forecasted by GARCH models. Volatility trading system is developed using the forecasted change direction of the VKOSPI, that is, if tomorrow VKOSPI is expected to rise, a long straddle or strangle position is established. A short straddle or strangle position is taken if VKOSPI is expected to fall tomorrow. Total profit is calculated as the cumulative sum of the VKOSPI percentage change. If forecasted direction is correct, the absolute value of the VKOSPI percentage changes is added to trading profit. It is subtracted from the trading profit if forecasted direction is not correct. For the in-sample period, the power ARCH model best fits in a statistical metric, Mean Squared Prediction Error (MSPE), and the exponential GARCH model shows the highest Mean Correct Prediction (MCP). The power ARCH model best fits also for the out-of-sample period and provides the highest probability for the VKOSPI change direction tomorrow. Generally, the power ARCH model shows the best fit for the VKOSPI. All the GARCH models provide trading profits for volatility trading system and the exponential GARCH model shows the best performance, annual profit of 197.56%, during the in-sample period. The GARCH models present trading profits during the out-of-sample period except for the exponential GARCH model. During the out-of-sample period, the power ARCH model shows the largest annual trading profit of 38%. The volatility clustering and asymmetry found in this research are the reflection of volatility non-linearity. This further suggests that combining the asymmetric GARCH models and artificial neural networks can significantly enhance the performance of the suggested volatility trading system, since artificial neural networks have been shown to effectively model nonlinear relationships.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• 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.

• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.33-41
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• 2006

In this paper, we analyse the volatilities in financial data such as stock prices and exchange rates in term of a class of nonlinear time series models. We compare the performance of Generalized Autoregressive Conditional Heteroscadastic(GARCH) , Integrated GARCH(IGARCH), Exponential GARCH(EGARCH) models by KOSPI (Korean stock Prices Index) data. The estimation for the parameters in the models was carried out by the ML methods.