• 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.

• Proceedings of the KOR-BRONCHOESO Conference
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• 1972.03a
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• pp.6-7
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• 1972

The air pollutants can be classified into the irritant gas and the asphixation gas, and the irritant gas is closely related to the otorhinolaryngological diseases. The common irritant gases are nitrogen oxides, sulfur oxides, hydrogen carbon compounds, and the potent and irritating PAN (peroxy acyl nitrate) which is secondarily liberated from photosynthesis. Those gases adhers to the mucous membrane to result in ulceration and secondary infection due to their potent oxidizing power. 1. Sulfur dioxide gas Sulfur dioxide gas has the typical characteristics of the air pollutants. Because of its high solubility it gets easily absorbed in the respiratory tract, when the symptoms and signs by irritation become manifested initially and later the resistance in the respiratory tract brings central about pulmonary edema and respiratory paralysis of origin. Chronic exposure to the gas leads to rhinitis, pharyngitis, laryngitis, and olfactory or gustatory disturbances. 2. Carbon monoxide Toxicity of carbon monoxide is due to its deprivation of the oxygen carrying capacity of the hemoglobin. The degree of the carbon monoxide intoxication varies according to its concentration and the duration of inhalation. It starts with headache, vertigo, nausea, vomiting and tinnitus, which can progress to respiratory difficulty, muscular laxity, syncope, and coma leading to death. 3. Nitrogen dioxide Nitrogen dioxide causes respiratory disturbances by formation of methemoglobin. In acute poisoning, it can cause pulmonary congestion, pulmonary edema, bronchitis, and pneumonia due to its strong irritation on the eyes and the nose. In chronic poisoning, it causes chronic pulmonary fibrosis and pulmonary edema. 4. Ozone It has offending irritating odor, and causes dryness of na sopharyngolaryngeal mucosa, headache and depressed pulmonary function which may eventually lead to pulmonary congestion or edema. 5. Smog The most outstanding incident of the smog occurred in London from December 5 through 8, 1952, because of which the mortality of the respiratory diseases increased fourfold. The smog was thought to be due to the smoke produced by incomplete combustion and its byproduct the sulfur oxides, and the dust was thought to play the secondary role. In new sense, hazardous is the photochemical smog which is produced by combination of light energy and the hydrocarbons and oxidant in the air. The Yonsei University Institute for Environmental :pollution Research launched a project to determine the relationship between the pollution and the medical, ophthalmological and rhinopharyngological disorders. The students (469) of the "S" Technical School in the most heavily polluted area in Pusan (Uham Dong district) were compared with those (345) of "K" High School in the less polluted area. The investigated group had those with subjective symptoms twice as much as the control group, 22.6% (106) in investigated group and 11.3% (39) in the control group. Among those symptomatic students of the investigated group. There were 29 with respiratory symptoms (29%), 22 with eye symptoms (21%), 50 with stuffy nose and rhinorrhea (47%), and 5 with sore thorat (5%), which revealed that more than half the students (52%) had subjective symptoms of the rhinopharyngological aspects. Physical examination revealed that the investigated group had more number of students with signs than those of the control group by 10%, 180 (38.4%) versus 99 (28.8%). Among the preceding 180 students of the investigated group, there were 8 with eye diseases (44%), 1 with respiratory disease (0.6%), 97 with rhinitis (54%), and 74 with pharyngotonsillitis (41%) which means that 95% of them had rharygoical diseases. The preceding data revealed that the otolaryngological diseases are conspicuously outnumbered in the heavily polluted area, and that there must be very close relationship between the air pollution and the otolaryngological diseases, and the anti-pollution measure is urgently needed.