Earticle

본 연구의 목적은 음의 변동성위험프리미엄 특성에 기반한 전통적인 옵션 양매도전략의 문제점을 개선하기 위해, 변동성 예측을 이용한 양매도 포지션의 선택적 진입전략을 제안하고 그 투자 성과를 분석하고자 하였다. 선 택적 진입전략은 비대칭적 변동성 전이효과와 SVM 모형을 결합하여 KOSPI 200 주가지수옵션시장의 장중 변동 성이 하락이나 횡보로 예측되는 날만 양매도 포지션을 진입하는 옵션의 스트래들 매도전략이다. 2008년부터 2014 년까지의 실험데이터에서 변동성의 최적 분류 모형을 찾아내고, 2015년부터 2018년까지의 검증데이터에 적용해 본 결과 제안모형이 비교모형보다 수익은 증가하고 투자 위험은 감소하는 우수한 결과를 보여주었다. 따라서 투자 성과지표인 Sharpe Ratio가 증가하는 좋은 결과를 얻을 수 있었다. 제안 모형은 옵션 거래자들에게 언제 포지션을 진입하고 언제 진입하지 말아야 하는지에 대한 가이드라인을 제시하고 있다.

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.

In this study we examine whether daily S&P 500 index volatility can be modeled parametrically as a long-memory process by extending an integrated process to a fractionally integrated one. The modified R/S test statistic and others are significant at the 1% level of significance, so we reject the null hypothesis of no long-term dependence. We have found that there is strong evidence for long memory in the series analyzed. We compare the out-of-sample forecasting performance of volatility models from 1962 to 2009. For various forecasting horizons, the long-memory FIGARCH model tends to make more accurate forecasts. Our empirical finding that the index volatility has long memory is consistent with prior evidence showing that an asset market volatility model such as plain GARCH puts too much weight on recent observations in the estimation process relative to those of the past. The forecasting model with the lowest MSFE and VaR forecast error among the models we consider is the FIGARCH model. In terms of forecasting accuracy, it dominates the widely accepted GARCH and rolling window GARCH models. We find that the White’s reality check p-values for the FIGARCH (1, 1) expanding window model reject the hypothesis that there exists a better model than the two benchmark models. The Hansen’s p-values report the same results.

This paper investigates the information content of the risk-neutral skewness derived from S&P 500 index option prices for forecasting future volatility. Empirical results show that the risk-neutral skewness provides incremental explanatory power for future volatility. Moreover, the models with the risk-neutral skewness dominate in terms of out-of-sample forecasting performance, especially during the 2007-2008 financial crisis.

The implied volatility from Black and Scholes (1973) model has been empirically tested for the forecasting performance of future volatility and commonly shown to be biased. Based on the belief that the implied volatility from option prices is the best estimate of future volatility, this study tries to find out a better model, which can derive the implied volatility from option prices, to overcome the forecasting bias from Black and Scholes (1973) model. Heston (1993)’s model which improves on the problems of Black and Scholes (1973) model the most for pricing and hedging options is one candidate, and VIX which is the expected risk neutral value of realized volatility under the discrete version is the other. This study conducts a comparative analysis on the implied volatility from Black and Scholes (1973) model, that from Heston (1993)’s model, and VIX for the forecasting performance of future volatility. From the empirical analysis on KOSPI200 option market, it is found that Heston (1993)’s implied volatility eliminates the bias mostly which Black and Scholes (1973) implied volatility has. VIX, on the other hand, does not show any improvement for the forecasting performance.

This article examines the volatility forecasting abilities of two approaches: one is GARCH-type model that uses carbon futures prices, and the other is an implied volatility from carbon options prices. Based on the results, we document that GARCH-type models perform better than an implied volatility. This result suggests that carbon options have little information about carbon futures due to their low trading volume. We also investigate whether the volatilities of energy markets, i.e., Brent oil, coal, natural gas, and electricity, forecast following day’s carbon futures volatility. According to the results, we suggest that Brent oil and natural gas may be used to forecast the volatility of carbon futures.

This study tries to find whether the volatility skew has some information contents about index and futures return. We use Corrado and Su(1996) and Câmara and Heston (2008) models to calculate the volatility skew. And then, we conduct Granger causality test to find whether there is a lead-lag relationship between the market returns and the volatility skew. As result, we found that while there is a strong bi-directional Granger causality between the volatility skew and the returns, the volatility skew Granger-cause the index return stronger than the index return does themselves, and Grangercause futures return weaker than futures return does. Also, there exists a stronger bi-directional leadlag relationship between the returns and the volatility skew when market is exceptionally bullish or bearish, or volatile. We can conjecture that while all of the market achieve a similar level of efficiency, options market is slightly more efficient than spot market, and is slightly less efficient than futures market.

한국의 주가지수파생상품 시장이 급속히 성장함에 따라 금융상품의 변동성이 가격 결정에 주요한 영향 변수로 인식되고 있다. 이 연구는 금융시계열과 인공지능기법의 결합을 통하여 단일 시계열모형의 예측 정확도를 향상하고, 변동성의 방향성과 예측력을 동시에 향상시킬 수 있는 결합 모형을 제안한다. 이를 위하여 변동성 예측을 위한 결합모형에서는 인공지능(ANN)기법과 금융시계열 예측모형인 ARCH, GARCH, IGARCH, EGARCH, EWMA 모형을 이용하였다. 모형의 적용을 위하여 KOSPI200(2001.1.2.~2010.12.30. 10년, 2,480거래일) 데이터를 사용하였고, 비교를 위하여 KOSDAQ100에도 제안 모형의 적용 가능성을 검증하였다. 방향성의 예측에서는 ARCH 계열의 시계열 모형이 우수성을 나타내었으며, 이를 이용하여 인공신경망기법의 학습을 위한 변수 및 계수 설정을 위한 방법론으로 사용하였다. 실험 결과, 금융시계열의 변동성 예측을 위해 GARCH 및 EGARCH 기법에서 도출된 입력변수를 이용한 결합 인공신경망결합 모형이 우수함을 나타내었으며, 10년간의 장기 데이터를 활용하여 모형의 적용 가능성을 보였다.

The volatility is one of the important issues in the financial market, as the Korean derivatives market of stock index is growing fast. The study suggests an experimental research about volatility forecasting model for financial time series, which overcomes the limit of the econometric volatility estimation methods and artificial intelligence techniques. It tries to improve the forecasting accuracy in terms of direction and deviation of volatility, which uses the integrated machine learning (Artificial Neural Network) and econometric ARCH(Autoregressive Conditional Heteroscedasticity)model such as GARCH, IGARCH, EGARCH. EWMA model. For this experiment, KOSPI200 data(2001.1.2.~2010.12.30, 10 years, 2,480 day trading data) was used, and KOSDAQ100 data was used for generalization of the suggested model. The main idea is that: As the ARCH models are superior in the direction forecasting, and AI techniques are better in the accuracy, so this model integrates the two techniques. Input variables of ANN are extracted by the estimation process of the ARCH models, then the ANN learning process build the volatility forecasting model. As the outcomes of the suggested model, it shows the ability of integration method between the ARCH models and AI techniques for volatility forecasting; especially GARCH and EGARCH model for input variables of ANN.

변동성을 측정하는 데에는 주로 종가기반(close-to-close)의 수익률 자료를 이용 하여 이루어지고 있지만, 일중 변동폭을 나타내는 가격범위에 관한 정보인 고가와 저가를 포함한 범위변동성에 대한 연구가 최근 활발해지고 있다. 본 연구는 범위 변동성에 대한 개념이 생긴 이후 최근 확장되고 있는 다양한 연구주제와 더불어 범위변동성을 실무적으로 활용하기 위한 것으로 범위변동성 예측에 있어 적절한 예측기간을 제시하는 것을 목적으로 하고 있다. 범위변동성은 Parkinson(1980; PK), Garman and Klass(1980; GK) Rogers and Satchell(1991; RS), Yang and Zhang(2008; YZ)이 제시한 추정치를 이용하였으며, AR(1), MA(1)모형을 이용하 여 예측된 변동성과 실현변동성간의 예측오차를 비교하는데 이때 예측기간을 시변 하여 산출함으로써 예측력을 비교분석하였다. 2000.5.22～2009.9.18(총 2,307일간)의 KOSPI200지수를 대상으로 분석한 결과는 다음과 같다. 첫째, PK, GK, RS, YZ 변동성 중 KOSPI200의 변동성을 가장 잘 예측하는 변 동성은 PK변동성 또는 RS변동성으로 보인다. 두 변동성의 예측력 우위는 분석기 간에 따라 미세한 차이를 보이는데 금융위기를 포함하는 경우 PK변동성이 우수하 며, 포함하지 않는 경우는 RS변동성이 우수한 것으로 나타났다. 둘째, 금융위기를 포함하지 않는 경우 대부분의 경우 예측오차가 크게 줄어드는 것으로 나타나 금융위기처럼 변동성이 크게 나타나는 경우에는 범위변동성을 이용 한 변동성예측력이 상당히 떨어질 수 있음을 확인하였다. 셋째, 범위변동성을 이용하여 변동성을 예측하는 경우 AR(1), MA(1)모형의 모 수추정기간을 길게 하는 경우 예측오차의 평균은 감소하는 경향이 확인되었다. 특 징적인 점은 60일 또는 90일로 기간을 늘일 경우에 예측오차가 급격하게 감소하는 경향을 보이는 것인데, 각각의 변동성과 예측모형에 따라 다소의 차이가 나타난다. 그리고, 예측오차의 편차는 90일 이후 큰 변화를 보이지 않고 있는 것으로 보인다. 따라서, 범위변동성을 이용하여 범위변동성을 예측할 경우 90거래일 이상의 가격 정보를 이용하여 예측을 하는 것이 예측오차를 줄여 예측력을 높일 수 있을 것으 로 판단된다.

This empirical study is focused on practical application of Range-Based Volatility which is estimated by opening, high, low, closing price of overall asset. Especially proper forecasting period is what I want to know. There is four useful Range-Based Volatility(RV) such as Parkinson(1980; PK), Garman and Klass(1980; GK) Rogers and Satchell(1991; RS), Yang and Zhang(2008; YZ). So, four RV of KOPSI 200 index during 2000.5.22-2009.9.18 was used for empirical test. The emprirical result as follows. First, the best RV which shows the best forecasting performance is PK volatility among PK, GK, RS, YZ volatility. According to estimating period forcasting performance of RV shows delicate difference. PK has better performance in the period with financial crisis of sub-prime mortgage loan. if not, RS is better. Second, almost result shows better performance on forecasting volatility without sub-prime mortgage loan period. so we can say that forecasting performance is lower when historical volatiltiy is comparatively high. Finally, I find that longer estimating period in AR(1) and MA(1) model can reduce forecasting error. More interesting point is that the result shows rapid decrease form 60 days to 90 days and there is no more after 90 days. So, if we forecast the volatility using Range-Based volaility it is better to estimate with 90 trading period or over 90 days.

본 연구는 KOSPI 200 주가지수옵션에서 옵션의 변동성 스큐가 주가지수의 점프 예측력을 지니는 지를 검증한다. 변동성 스큐는 Doran, Peterson and Tarrant(2007) 모형과 Bakshi, Kapadia, and Madan(2003) 모형, Corrado and Su(1996) 모형을 이용하여 추정하고 주가지수의 점프는 신뢰구간 을 벗어난 주가지수의 움직임과 Lee and Mykland(2006) 모형을 이용하여 추정한다. 변동성 스큐 와 주가지수 점프 간의 관계는 프로빗 모형을 이용하여 검증한다.연구 결과 풋옵션의 변동성 스 큐는 미래의 음의 주가지수 점프 대한 정보를 가지고 있으며, 콜옵션의 변동성 스큐는 양의 주가 지수 점프에 대한 정보를 가지고 있는 것으로 나타났으며, 콜옵션 변동성 스큐의 주가지수 상승 예측력에 비해 풋옵션 변동성 스큐의 주가지수 하락 예측력이 더 좋은 것으로 나타났다. 또한 잔 존만기가 짧은 옵션으로부터 추정한 변동성 스큐가 잔존만기가 긴 경우에 비해서 옵션 변동성 스 큐를 통한 주가지수 점프 예측력이 더 좋은 것으로 나타났다.

This paper investigates whether the volatility skew has predictive information about the jumps of stock index.When estimating volatility skew, we use three different methodologies, Doran, Peterson and Tarrant(2007), Corrado and Su(1996), Bakshi, Kapadia and Madan(2003) models to give more robustness to this paper. We categorized jumps on the base of the percentage change in stock index over a previous day. Additionally, We employ Lee and Mykland(2006) methodology which isolate the volatility effects on jumps.Followings are the major findings and implications drawn from the empirical analysis of the Korean options market. First of all, Bakshi, Kapadia and Madan(2003) skew and Corrado and Su (1996)skew has power in predicting the market crash while does not have for the market spikes. As for Doran, Peterson and Tarrant(2007) skew, put volatility skew has some information about downward market crash while call volatility skew has for upward market spikes. However, the predictive power of put volatility skew is stronger than that of call volatility skew. Second, the predictive power of volatility skew for forecasting the jumps of underlying assets weakens as time to maturity of options increases while implied volatility has still predictive power in the longer maturity options.Third, as a result of conducting probit model using LM jump, we find that the volatility skew still has predictive information for forecasting the movements of underlying assets under controlling volatility impact on jumps.In contrast with Doran, Peterson and Tarrant(2007), we observe that there is some information about future market movements in the volume variable and the predictive power of the volume variable is getting stronger as it is expected to be rather larger market movements. Also, put volume has more information contents in forecasting market spikes or crash than call volume does.

본 연구는 아시아 이머징 주식시장 변동성에 장기기억과 비대칭성이 존재하 는지를 분석하였다. 이를 위해서 AR(1)-FIGARCH모형과 AR(1)-FIAPARCH 모형을 이용하여 실증분석 하였다. 실증분석 결과 아시아 이머징 시장 변동성 에 장기기억과 비대칭성이 존재하는 것으로 분석 되었다. 이는 주식시장에 충 격이 소멸되지 않고 장기간 지속되는 것을 의미한다. 또한, 투자자들은 나쁜 충격에 민감하게 반응하는 것으로 분석 되었다. 마지막으로 장기기억 모형의 예측력을 분석한 결과 장기기억과 비대칭성을 분석할 수 있는 FIAPARCH모 형이 장기기억만을 분석하는 FIGARCH모형보다 우월한 것으로 분석되었다. 이러한 결과는 변동성의 특징은 장기기억 뿐만 아니라 비대칭성을 동시에 포 함하고 있음을 의미하고 있다. 이러한 결과는 변동성을 정확하게 측정하는데 중요한 자료로 이용될 것으로 판단된다. 특히, Value at Risk를 측정하기 위해 서 정교한 변동성 예측 모형이 필요하기 때문에 주식시장 리스크 관리에 도움 이 될 것으로 판단된다.

This study investigates long memory and asymmetry in volatility of Asian emerging markets using the AR(1)-FIGARCH and AR(1)-FIAPARCH models under the Student-t distributions. The empirical results show strong evidence of long memory in the volatility of 8 Asian emerging markets. This evidence indicates that market shocks to volatility slowly disappear over the time. In addition, the FIAPARCH model detect volatility asymmetry in which investors want hedge negative information in Asian emerging markets. Finally, the forecasting error functions suggest that the FIAPARCH model with the Student-t distribution offers a superior forecasting ability to other models. These results provide important implications on assess accurate Value at Risk in the Asian emerging markets.

본 연구에서는 한국, 중국 및 일본 3개국의 주가지수를 사용하여 수익률 및 변동성을 예측하는 방안에 대해 연구한다. 분석 대상인 금융시계열 자료인 주가지수가 장기 기억(long memory)을 가지고 있을 경우 정수만으로 차분 함에 의해 정보가 과도하게 손실되어지는 것을 막기 위해 실수 차분으로 과잉 차분을 피할 수 있는 분수적분 ARMA 또는 FARIMA(AutoRegressive Fractionally Integrated Moving Average) 모델과 FI-GARCH(Fractionally Integrated Generalized AutoRegressive Conditional Heteroscedasticity) 모델을 이용한다. 하지만 분석대상인 원래 시계열 자료의 선정기준이 되는 “시점”의 애매함이 존재하기에 본 연구에서는 퍼지수 및 퍼지 회귀 모델을 고려하여 FUZZY FARIMA 모델로 수익률(return) 을, FUZZY FI-GARCH 모델로 변동성(volatility)을 예측하는 방법을 제안한다. 그리고 수익률은 단변량 ARFIMA모델을 다변량 ARFIMA모델로 확장하여 예측치를 추정한다. 본 연구에서 제안한 퍼지 FARIMA모델은 수익성 분석에 우위성을 가지고 있음을 발견하였다. 또한 FI-GARCH 모델을 이용한 변동성 예측 분석은 아주 타당성을 가지고 있음을 발견하였다.

This paper investigates how to protect big loss of information by difference when the time-series data set with its long memory. We use a ARFIMA (AutoRegressive Fractionally Integrated Moving Average) model and FI-GARCH (Fractionally Integrated Generalized AutoRegressive Conditional Heteroscedasticity) model which are possibly to avoid over- difference problem by real number difference. Also we suggest unique forecasting models for return with FUZZY ARFIMA model and volatility with FUZZY FI-GARCH model which are considering Fussy number and Fussy regression model. We forecating for return estimator by extension of single ARFIMA model to multiple ARFIMA model. We also forecasting for volatility estimator, evaluate the degree of forecasting, using PI which is based on the Black and Scholes Put Option model.

Given the growing importance of the Taiwan stock market, the present study sets out to provide a comprehensive investigation of the intraday time series of the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). We begin by exploring the intraday volatility patterns and then go on to examine their impact on intraday volatility forecasting. We find that the volatility of the TAIEX returns exhibits an L-shaped intraday periodic pattern, which is distinct across each day of the week. Our empirical results indicate that taking the intraday periodic pattern into account in a generalized autoregressive conditional heteroskedasticity model can substantially improve the precision of intraday volatility forecasting.

The conditional autoregressive value at risk (CAViaR) model is useful for risk management, which does not require the assumption that the conditional distribution does not vary over time but the volatility does. But it does not provide volatility forecasts, which are needed for several important applications such as option pricing and portfolio management. For a variety of probability distributions, it is known that there is a constant relationship between the standard deviation and the distance between symmetric quantiles in the tails of the distribution. This inspires us to use a support vector quantile regression (SVQR) for volatility forecasts with the distance between CAViaR forecasts of symmetric quantiles. Simulated example and real example are provided to indicate the usefulness of proposed forecasting method for volatility.

The market participants always wonder the use of agriculture futures markets to mitigate risk, as the trading in agricultural commodity not only leads to increasing exposure to external shocks but also raises the uncertainty about the future price movements. The farmers and the consumers generally disfavor the volatility in food markets and consider it as a serious concern; whereas the speculators favor such price fluctuations to profit by predicting which direction prices are headed. Generally such trading activity has an irrational component translated into prices. The unpredictability of food prices is a cause for concern because of the adverse effects it has on the producers as well as the consumers. The agricultural commodity prices all around the world have been substantially sensitive to the movements of macroeconomic indicators in this century. In India, the Dhaanya highlights the importance of agriculture and provides a reliable benchmark for the traded Agri?commodities. Today, the Investors all over the world consider Agri?commodities as one of the major asset class and the other indicators like Nifty market index and the Rupee Dollar (US) exchange rate have major influence on the prices of Dhaanya (Mahalakshmi, et.al., 2012 a, b & c). To gauge this volatility, GARCH class of models is the most appropriate model to estimate the volatility of the returns of groups of stocks with large number of observations. The analysis of ARCH and GARCH models and their many extensions catered many theories of asset pricing and portfolio analysis.

This article investigates and compares the ability to conduct one-day-ahead volatility forecasts in the Korean futures market utilizing three volatility models, including GARCH, IGARCH and FIGARCH models. The estimation results in this study using KOSPI 200 futures market conclude that the FIGARCH model is more adequately equipped to capture the long memory property than are the GARCH and IGARCH models. Additionally, the FIGARCH model provides superior performance in one-day-ahead volatility forecasts. Thus, we conclude that the FIGARCH model should prove useful to financial economists, policy makers, investors and financial analysts who are interested in modeling and forecasting the dynamics of Korean futures market volatility.

This paper provides empirical results showing Value-at-Risk(VaR) for equity-linked annuities(ELA) calculated by different volatility models: GARCH, EGARCH, and GJR-GARCH models. We will use two procedures to calculate VaR for ELA. The first procedure examines the performance of various GARCH-type models with regard to forecasting the future volatility of S\&P 500 index daily returns. We use the three GARCH-type models mentioned above to forecast volatility. To further the robustness of estimating results, we compare the empirical performances of the three GARCH-type models for the both in-sample and out-of-sample tests. The second is to calculate VaR for S\&P 500 index return and ELA using the forecasting results of each GARCH-type model. We also calculate the value of ELA and VaR under the Student's t-distribution. Additionally, we analyze the effect of contract terms of ELA on the VaR in this procedure.

The depletion problem of the national pension plan is emerging as life expectancy increases and the fertility rate decreases. The retirement pension system is being introduced in earnest to supplement the national pension system. The Target Date Fund, introduced to prepare for retirement, rebalances its portfolio through Glide Path, which has a fixed ratio of risky assets according to the subscriber’s life cycle. The purpose of this study was to propose a new Glide Path that simultaneously considers the life cycle and stock market volatility, and to analyze the possibility of improving the performance of the TDF portfolio through empirical analysis. To this end, we first predict stock market volatility for determining investment risk and derive a Glide Path reflecting the predicted volatility. Stock market volatility, which has the greatest influence on the new Glide Path, is predicted using the GARCH model. If the volatility is expected to increase, the TDF risk will be managed by reducing the risky assets. Results of the study using financial market data from 1987 to 2021 showed as follows. First, the asymmetric phenomenon of volatility was significant, and the usefulness of the asymmetric GARCH model was revealed. Second, the proposed Glide Path was able to lower the risk of TDF funds by lowering the risky asset incorporation ratio in the stock market crash periods such as 1998, 2008, and 2020. Third, the TDF portfolio applied to the proposed model showed higher returns and lower standard deviation, improving Sharpe Ratio. Fourth, the model proposed in the long-term investment performance showed a lower maximum draw down than the comparative model. It was revealed that the TDF performance could be improved by reflecting the market risk.

This paper examines quantile dependence between international stock markets and evaluates its use for improving volatility forecasting. First, we adopt the cross-quantilogram, a correlation statistic of quantile hit processes, and analyze quantile dependence and directional predictability between the US stock market and stock markets in the UK, Germany, France and Japan. Second, we consider a simple quantile-augmented volatility model that accommodates the quantile dependence and directional predictability from the US market to these other markets. The quantile-augmented volatility model provides superior in-sample and out-of-sample volatility forecasts. Finally, we set up a generalized quantile-based approach to improve volatility forecasting for a wide class of asset portfolios.

We combine the integer-valued GARCH(1, 1) model with a generalized regime-switching model to propose a dynamic count time series model. Our model adopts Markov-chains with time-varying dependent transition probabilities to model dynamic count time series called the generalized regime-switching integer-valued GARCH(1, 1) (GRS-INGARCH(1, 1)) models. We derive a recursive formula of the conditional probability of the regime in the Markov-chain given the past information, in terms of transition probabilities of the Markov-chain and the Poisson parameters of the INGARCH(1, 1) process. In addition, we also study the forecasting of the Poisson parameter as well as the cumulative impulse response function of the model, which is a measure for the persistence of volatility. A Monte-Carlo simulation is conducted to see the performances of volatility forecasting and behaviors of cumulative impulse response coefficients as well as conditional maximum likelihood estimation; consequently, a real data application is given.

With regards to the U.S. stock market, today’s heavy trading volume makes option implied volatility more informative as compared to the long history of lagged prices in forecasting future return volatility. The U.S. stock market takes advantage of immediate information, which is an evidence of an advanced application of the information technologies. However, for the Korean stock market, today’s heavy trading volume does not necessarily make option-implied volatility more informative than a long history of lagged prices, which is interpreted as a sluggish application of the information technologies. We find the U.S. option implied volatility to be more informative than the Korean option implied volatility when forecasting Korean future return volatility. The foreign traders in the Korean market might have some advantages in terms of information technologies. Nevertheless, during the recent financial crisis, Korea’s high trading volume makes her option implied volatility more informative than the U.S. option implied volatility, which implies a progress in the application of information technologies among the domestic traders in the Korean stock market.