• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.119-133
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• 2020

Fama asserted that in an efficient market, we can't make a trading rule that consistently outperforms the average stock market returns. This study aims to suggest a machine learning algorithm to improve the trading performance of an intraday short volatility strategy applying asymmetric volatility spillover effect, and analyze its trading performance improvement. Generally stock market volatility has a negative relation with stock market return and the Korean stock market volatility is influenced by the US stock market volatility. This volatility spillover effect is asymmetric. The asymmetric volatility spillover effect refers to the phenomenon that the US stock market volatility up and down differently influence the next day's volatility of the Korean stock market. We collected the S&P 500 index, VIX, KOSPI 200 index, and V-KOSPI 200 from 2008 to 2018. We found the negative relation between the S&P 500 and VIX, and the KOSPI 200 and V-KOSPI 200. We also documented the strong volatility spillover effect from the VIX to the V-KOSPI 200. Interestingly, the asymmetric volatility spillover was also found. Whereas the VIX up is fully reflected in the opening volatility of the V-KOSPI 200, the VIX down influences partially in the opening volatility and its influence lasts to the Korean market close. If the stock market is efficient, there is no reason why there exists the asymmetric volatility spillover effect. It is a counter example of the efficient market hypothesis. To utilize this type of anomalous volatility spillover pattern, we analyzed the intraday volatility selling strategy. This strategy sells short the Korean volatility market in the morning after the US stock market volatility closes down and takes no position in the volatility market after the VIX closes up. It produced profit every year between 2008 and 2018 and the percent profitable is 68%. The trading performance showed the higher average annual return of 129% relative to the benchmark average annual return of 33%. The maximum draw down, MDD, is -41%, which is lower than that of benchmark -101%. The Sharpe ratio 0.32 of SVS strategy is much greater than the Sharpe ratio 0.08 of the Benchmark strategy. The Sharpe ratio simultaneously considers return and risk and is calculated as return divided by risk. Therefore, high Sharpe ratio means high performance when comparing different strategies with different risk and return structure. Real world trading gives rise to the trading costs including brokerage cost and slippage cost. When the trading cost is considered, the performance difference between 76% and -10% average annual returns becomes clear. To improve the performance of the suggested volatility trading strategy, we used the well-known SVM algorithm. Input variables include the VIX close to close return at day t-1, the VIX open to close return at day t-1, the VK open return at day t, and output is the up and down classification of the VK open to close return at day t. The training period is from 2008 to 2014 and the testing period is from 2015 to 2018. The kernel functions are linear function, radial basis function, and polynomial function. We suggested the modified-short volatility strategy that sells the VK in the morning when the SVM output is Down and takes no position when the SVM output is Up. The trading performance was remarkably improved. The 5-year testing period trading results of the m-SVS strategy showed very high profit and low risk relative to the benchmark SVS strategy. The annual return of the m-SVS strategy is 123% and it is higher than that of SVS strategy. The risk factor, MDD, was also significantly improved from -41% to -29%.

• Proceedings of the Korean Society of Dyers and Finishers Conference
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• 2002.04a
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• pp.25-29
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• 2002

• Textile Coloration and Finishing
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• v.23 no.2
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• pp.118-130
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• 2011

In this study, hydrophilic polyurethane (PU) was synthesized by one shot process to get good non-swelling effect and to keep high breathability using reactive silicone oil of mono terminal and bi-terminal types. We also blended non reactive silicone oil with pure hydrophilic PU to compare non-swelling effect and breathability with hydrophilic PU synthesized by the two types of reactive silicone oils. The hydrophilic films were analyzed by nuclear magnetic resonance (NMR) spectroscopy, scanning electron microscope (SEM), Fourier transform infrared (FT-IR) spectroscopy, X-ray photo electron (XPS) spectroscopy, energy dispersive spectrometry (EDS), breathability, waterproofness, tensile strength, contact angle and swelling effect. The results showed that the film made by hydrophilic PU which was synthesized with mono terminal type silicone oil provided good non-swelling effect and acceptable moisture permeability due to the modified surface properties.

• Proceedings of the Korean Society of Dyers and Finishers Conference
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• 2003.04a
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• pp.78-82
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• 2003

셀룰로오스계 재생섬유 중 가장 먼저 개발된 Viscose rayon은 셀룰로오스 펄프를 17-18%의 가성소다 수용액에 팽윤시켜 알칼리 셀룰로스를 만든 뒤 이황화탄소(CS$_2$)에 반응시킨 후 방사하여 제조하며 흡습성, 제전성, 드레이프성 및 광택이 우수한 쾌적성 섬유이다. 그러나 탄성 회복율이 좋지 않으며 강력이 적고 특히 습윤시의 강력 저하의 단점이 있을 뿐만 아니라 제조과정에서 사용하는 이황화탄소의 환경 유해성이 큰 문제점으로 지적되고 있어 선진국들이 점차 설비를 폐쇄하거나 개도국으로 이전하고 있는 실정이다. (중략)

• The Korean Journal of Applied Statistics
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• v.36 no.2
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• pp.129-139
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• 2023

Forecasting volatility is essential to avoiding the risk caused by the uncertainties of an financial asset. Complicated financial volatility features such as ambiguity between non-stationarity and stationarity, asymmetry, long-memory, sudden fairly large values like outliers bring great challenges to volatility forecasts. In order to address such complicated features implicity, we consider machine leaning models such as LSTM (1997) and GRU (2014), which are known to be suitable for existing time series forecasting. However, there are the problems of vanishing gradients, of enormous amount of computation, and of a huge memory. To solve these problems, a causal temporal convolutional network (TCN) model, an advanced form of 1D CNN, is also applied. It is confirmed that the overall forecasting power of TCN model is higher than that of the RNN models in forecasting VIX, VXD, and VXN, the daily volatility indices of S&P 500, DJIA, Nasdaq, respectively.

• Textile Coloration and Finishing
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• v.25 no.1
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• pp.30-36
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• 2013

Swelling and subsequent deformation of membranes by water wetting are regarded as a prime drawback of hydrophilic polyurethane breathable film. Fluoroalkylated surface was prepared by reacting the film with hexamethylene diisocyanate(HDI) and 2-perfluorohexyl ethanol. IR spectra and XPS results showed that the fluoroalkyl group was successfully introduced to the film surface with hexamethylene linkage. Water contact angle was increased from $68.7^{\circ}$ up to $144.2^{\circ}$ with the degree of fluoroalkylation. Decrease in water-vapor permeability was minimized even for the film of highest fluoroalkylation.

• Textile Coloration and Finishing
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• v.25 no.4
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• pp.279-286
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• 2013

Application properties of ultra light weight silica aerogel toward polyurethane membranes were investigated. From the results of pre-milling process of the silica aerogel, the solvent for dispersion of the aerogel was determined for methyl ethyl ketone and its content in the solvent was determined by 30%. Using this aerogel dispersion, the polyurethane membranes were prepared according to the mixing amount of silica aerogel and various properties of the membranes were investigated. As results, the optimum mixing amount of silica aerogel inside polyurethane membranes was decided at 11%, because the improvement of light weight property, air permeability, and moisture vapor permeability were improved upto 11% of silica aerogel content, maintaining the water penetration resistance almost unchanged.

• Journal of Distribution Science
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• v.12 no.3
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• pp.75-83
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• 2014

Purpose - The oil price affects company value, which is the present value of the expected cash flow, by affecting the discount rate and cash flow. This study examines the nonlinear relationships between oil price and stock price using the AlphaShares Chinese Volatility Index as the threshold. Research design, data, and methodology - Data comprise daily closing values of the Shanghai Stock Exchange Composite Index, Shenzhen Stock Exchange Composite Index, and Hang Seng Index of ChinaWest Texas Intermediate crude oil spot price and AlphaShares Chinese Volatility Index from May 25, 2007 to May 24, 2012. The Threshold Error Correction Model is used. Results - The results demonstrate different relationships between the stock price index and oil price under different investor sentiments; however, the stock price index and oil price could adjust to a long-term equilibrium the long-term causality tests between them were all significant. Conclusions - The relationship between the WTI and HANG SENG Index is more significant than the Shanghai Composites Index and Shenzhen Composite Index, when using the AlphaShares Chinese Volatility Index (ASC-VIX) as the investor sentiment variable and threshold.

• Communications for Statistical Applications and Methods
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• v.27 no.4
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• pp.487-500
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• 2020

Insurers face increasing demands for cyber liability; entailed in part by a variety of new forms of risk of data breaches. As data breach occurrences develop, our understanding of the volatility in data breach counts has also become important as well as its expected occurrences. Volatility clustering, the tendency of large changes in a random variable to cluster together in time, are frequently observed in many financial asset prices, asset returns, and it is questioned whether the volatility of data breach occurrences are also clustered in time. We now present volatility analysis based on INGARCH models, i.e., integer-valued generalized autoregressive conditional heteroskedasticity time series model for frequency counts due to data breaches. Using the INGARCH(1, 1) model with data breach samples, we show evidence of temporal volatility clustering for data breaches. In addition, we present that the firms' volatilities are correlated between some they belong to and that such a clustering effect remains even after excluding the effect of financial covariates such as the VIX and the stock return of S&P500 that have their own volatility clustering.

• Annual Conference of KIPS
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• 2024.05a
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• pp.576-579
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• 2024

경제 위기 대비를 위해 인공지능을 활용한 주식시장 변동성 이상을 탐지하는 목적을 가지고 있다. 글로벌 이슈와 경제 위기 대비를 위해 주식시장 변동성 예측의 중요성이 부각되고 있으며, 기존의 주식시장 변동성 지수인 VIX 의 한계로 인해 더 복잡한 모델 및 인공지능을 활용한 연구에 관심이 집중되고 있다. 기존의 주식시장 변동성 예측에 관한 연구들은 통계적인 방법을 사용했으며 인공지능을 이용한 연구 또한 대부분 이상치 구간을 표시하여 예측을 목표로 하고 있으나 이러한 접근법은 라벨이 있는 데이터 수집 어려움, 클래스 불균형 문제가 있다. 본 연구는 인공지능을 활용한 주식시장 변동성 탐지에 기여하고 지도 학습 방식 대신 비지도 학습 기반의 이상탐지모델을 사용하여 주식시장 변동성을 예측하는 새로운 방법론을 제안한다. 본 연구에서 개발한 인공지능 모델은 IsolationForest 모델을 활용하며, 시계열 데이터를 전처리한 후 정상성을 확보하는 등의 과정을 거친다. 실험 결과로 인공지능 모델이 주요 경제이슈를 이상치로 검출하는 성능을 확인하였으며 재현율 약 93.6%, 정밀도 100%로 높은 성능을 달성했다.