• 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.

• Korean Journal of Crystallography
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• v.6 no.1
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• pp.1-13
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• 1995

Cr:A12O3 and Ti:A12O3 single crystals were grown by Czochralski method, and the effects of crystal growth parameters such as pulling rate, rotation rate, dopant and growth atmosphere on crystal quality were investigated. And spectroscopic properties including lasing efficiency were also measured. Single crystals, sized of 20mm in diameter and 100-135mm in length, were successfully grown from the seed of <001> direction. With the doping level of 0.5w/o Cr2O3, pulling rate 2.0mm/hr, rotation rate of 30rpm and inert atmosphere by nitrogen gas, high quality crystals of Cr:A12O3 were grown. While in case of Ti:A12O3 crystals, high quality crystals were grown under the conditions of the doping level of 0.25w/o TiO2, pulling rate of 1.5mm/hr, rotation rate of 30rpm and reducing atmosphere by hydrogen - nitrogen mixed gas. It was confirmed that Cr3+ ion which maintains its ionoc valence during growth easily de-bubbled than Ti4+ ion which changes its valence, Fe3+ ion also has do-bubbling effect to Ti:A12O3 crystal and the reducing atmosphere by 90% N2 - 10% H2 mixed gas gave effective result on the changing of Ti4+ to Ti3+ and de-bubbling. As a result of spectroscopic measurements of Cr:A12O3 crystal, 4A2 →4F2 and 4F1 absorption transitions and E →4A2(R1) and 2A →4A2(R2) fluorenscence transitions were confirmed. And it was measured that wavelengths of laser R1 and R2 transitions were 696±5nm and 692±5nm respectively, line width of these transitions were 12A, and life-time of fluorenscence was 152μsec. In case of Ti:A12O3 crystals, it was confirmed that absortion transition of 4T2→4E and fluorescence transition of 4E→4T2 with wide range of 650-1050nm was occured. And 147μsec of life-time of fluorescence, 125.4 of figure of merit and 9% of laser efficience were also measured.

• Journal of Broadcast Engineering
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• v.8 no.4
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• pp.325-338
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• 2003

Video adaptation in response to dynamic resource conditions and user preferences is required as a key technology to enable universal multimedia access (UMA) through heterogeneous networks by a multitude of devices In a seamless way. Although many adaptation techniques exist, selections of appropriate adaptations among multiple choices that would satisfy given constraints are often ad hoc. To provide a systematic solution, we present a general conceptual framework to model video entity, adaptation, resource, utility, and relations among them. It allows for formulation of various adaptation problems as resource-constrained utility maximization. We apply the framework to a practical case of dynamic bit rate adaptation of MPEG-4 video streams by employing combination of frame dropping and DCT coefficient dropping. Furthermore, we present a descriptor, which has been accepted as a part of MPEG-21 Digital Item Adaptation (DIA), for supporting terminal and network quality of service (QoS) in an interoperable manner. Experiments are presented to demonstrate the feasibility of the presented framework using the descriptor.