Forecasting Volatility in Financial Markets Using a Bivariate Stochastic Volatility Model with Surprising Information

Most asset returns exhibit high volatility and its persistence. Heuristically, this paper focuses on the role of surprising information in high volatility processes and indicates that dismissing surprising information may lead to considerable loss in forecast accuracy. In response, this paper considers the corresponding extension of the modified MDH to surprising information, and proposes a bivariate stochastic volatility model incorporating surprising information in the volatility equations (BSV-SI), which is also designed to capture the dynamics of returns and trading volume. Using the South Korea stock index and trading volume series, it turns out that performance of the onestep- ahead forecasts of the BSV-SI model is apparently superior to those of other competitive models.Volatility forecasting, Bivariate stochastic volatility model with surprising information, Modified mixture of distribution hypothesis, Realized volatility models, Markov Chain Monte Carlo (MCMC)

Comparison of Place Coding and Thermometer Coding in Neural Networks

In neural network research, there have been many studies about optimization weight updating methods, and various structures of networks. However, there also has been some research on codings in neural networks used in solving the feature extracting problem. Meaningful input representation allows a network to work efficiently because of the specific way the network implements its function with given input samples. This can be explained by the Categorical Perception (CP) effect inside of a neural network. This thesis shows the significance of meaningful input representation by comparing the behavior of a network trained with two different types of input codings (place coding and thermometer coding). Tests are conducted using different numbers of hidden nodes, different numbers of hidden layers, four types of transfer functions, and four sets of real world data

Determination and comparison of freely dissolved PAHs using different types of passive samplers in freshwater

Polycyclic aromatic hydrocarbons (PAHs) are well-known for their recalcitrant properties and biotoxicity in organisms, leading to serious environmental and health problems. Despite various analytical methods available, accurate determination of the bioavailable fraction is warranted in order to evaluate the precise toxic potentials of these compounds. Currently, the passive sampler is used worldwide to measure the bioavailable PAHs in the environment using the equilibrium partitioning principle. In this study, we co-deployed different types of passive samplers, which are linear low-density polyethylene (LLDPE) and low-density polyethylene (LDPE), to determine freely dissolved concentrations (Cfree) of PAHs using the performance reference compounds (PRCs) in Kentucky Lake (KL), Ohio River (OH), and Mississippi River (MS). The fractional equilibrium (feq) of BeP-d12 was observed to be high in LLDPE compared with LDPE in OH and MS. In contrast, the feq of all PRCs was similar in both passive samplers in KL due to slow flow velocity. The sum of average freely dissolved PAH concentrations in LLDPE and LDPE during the exposure period were 2.89 and 1.27 ng/L in KL, 8.13 and 3.31 ng/L in OH, and 5.19 and 3.82 ng/L in MS, respectively. The results revealed that LLDPE is a suitable alternative tool to LDPE for both short-term and long-term monitoring of PAHs

New gene selection method for classification of cancer subtypes considering within-class variation

AbstractIn this work we propose a new method for finding gene subsets of microarray data that effectively discriminates subtypes of disease. We developed a new criterion for measuring the relevance of individual genes by using mean and standard deviation of distances from each sample to the class centroid in order to treat the well-known problem of gene selection, large within-class variation. Also this approach has the advantage that it is applicable not only to binary classification but also to multiple classification problems. We demonstrated the performance of the method by applying it to the publicly available microarray datasets, leukemia (two classes) and small round blue cell tumors (four classes). The proposed method provides a very small number of genes compared with the previous methods without loss of discriminating power and thus it can effectively facilitate further biological and clinical researches

Development of carbon-based adsorbent for separation of impurities such as siloxane and ammonia from land-fill gas

Land-fill gas or bio-gas is composed of large portion of methane and carbon dioxide, and small amount of impurities such as nitrogen, oxygen, hydrogen sulfide, siloxane and ammonia. These gases can be used as a gas-fuel after upgrading treatment. For the application of the land-fill gas and bio-gas as a fuel, we developed highly-performing carbon-based adsorbent which can separate siloxane and ammonia residue from these gases. It was quite necessary to consider the chemical properties of siloxane and ammonia for development of suitable adsorbent of each component. The siloxane can be polymerized in acidic or basic condition to form bulkier species which causes adsorbent deactivation and difficult regeneration. The ammonia gas is well known as basic molecules which have strong affinity to acidic species. In these reasons, we prepared neutral carbon materials by various methods for siloxane adsorption. In addition, we developed carbon-based basic ammonia-adsorbent by simple methods such as the chemical treatment of commercial activated carbon or the impregnation of organic molecules into the activated carbon. And then, adsorption-desorption isotherms and breakthrough curve of siloxane and ammonia were measured for thus synthesized adsorbents. Detail results for synthesis and the adsorption measurement of the studied adsorbents will be presented in the conference