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.
These studies were conducted to investigate nutrient sources and supplementary materials of synthetic compost media for Agaricus bisporus culture. Investigation were carried out to establish the optimum composition for compost of Agaricus bisporus methods of out-door fermentation and peakheating with rice straw as the main substrate of the media. The incidence and flora of harmful organisms in rice straw compost and their control were also studied. 1. When rice straw was used as the main substrate in synthetic compost as a carbon source. yields were remarkably high. Fermentation was more rapid than that of barley straw or wheat straw, and the total nitrogen content was high in rice straw compost. 2. Since the morphological and physico-chemical nature of Japonica and Indica types of rice straw are greatly dissimilar. there were apparent differences in the process of compost fermentation. Fermentation of Indica type straw proceeded more rapidly with a shortening the compost period, reducing the water supply, and required adding of supplementary materials for producing stable physical conditions. 3. Use of barley straw compost resulted in a smaller crop compared with rice straw. but when a 50%, barley straw and 50% rice straw mixture was used, the yield was almost the same as that using only rice straw. 4. There were extremely high positive correlations between yield of Agaricus bisporus and the total nitrogen, organic nitrogen, amino acids, amides and amino sugar nitrogen content of compost. The mycerial growth and fruit body formation were severely inhibited by ammonium nitrogen. 5. When rice straw was used as the main substrate for compost media, urea was the most suitable source of nitrogen. Poor results were obtained with calcium cyanamide and ammonium sulfate. When urea was applied three separate times, nitrogen loss during composting was decreased and the total nitrogen content of compost was increased. 6. The supplementation of organic nutrient activated compost fermentation and increased yield of Agaricus bisporus. The best sources of organic nutrients were: perilla meal, sesame meal, wheat bran and poultry manure, etc. 7. Soybean meal, tobacco powder and glutamic acid fermentation by-products which were industrial wastes, could be substituted for perilla meal, sesame meal and wheat bran as organic nutrient sources for compost media. B. When gypsum and zeolite were added to rice straw. physical deterioration of compost due to excess moisture and caramelization was observed. The Indica type of straw was more remarkable in increase of yield of Agricus bisporus by addition of supplementing materials than Japonica straw. 9. For preparing rice straw compost, the best mixture was prepared by 10% poultry manure, 5% perilla meal, 1. 2 to 1. 5% urea and 1% gypsum. At spring cropping, it was good to add rice bran to accelerate heat generation of the compost heap. 10. There was significantly high positive correlation (r=0.97) between accumulated temperature and the decomposition degree of compost during outdoor composting. The yield was highest at accumulated temperatures between 900 and