Assessing Volatility Modelling using three Error Distributions

The current study focuses on estimating the volatility of stock returns in the presence of flat tails error distribution (i.e. asymmetry of the distribution) which a traditional generalized auto-regressive conditional heteroscedasticity GARCH model usually fails to explain. The study, unlike the previous studies, compares three sets of error distributions for GARCH (1, 1) model of stock returns.  The three sets of error distributions used for comparing the predictive ability of GARCH (1, 1) model are –Gaussian (normal distribution), student’s t and generalized error distribution (GED). Eviews software was used for analyzing a time series data of Flying cement stock shares consisting of 245 days of in sample and 15 days of out-of-sample data. To compare the forecasting capability of three models root mean square (RMSE) and Theil’s Inequality Coefficient (TIC) were used. Akaike information criterion (AIC), the Schwarz information criterion (SIC), Hannan, and Quin (HQ) information criteria were examined for selection of a suitable model for capturing volatility of stock returns in the presence of symmetrical and asymmetrical distributions. Results of the study revealed that GARCH (1, 1) with GED is the best model for capturing the volatility of stock returns of Flying Cement Industry. Results of the present study will provide a stimulus to academia and practitioners for incorporating asymmetry aspect of the distribution in future prediction and capturing volatility of stock returns

Assessing Volatility Modelling using three Error Distributions

The current study focuses on estimating the volatility of stock returns in the presence of flat tails error distribution (i.e. asymmetry of the distribution) which a traditional generalized auto-regressive conditional heteroscedasticity GARCH model usually fails to explain. The study, unlike the previous studies, compares three sets of error distributions for GARCH (1, 1) model of stock returns.  The three sets of error distributions used for comparing the predictive ability of GARCH (1, 1) model are –Gaussian (normal distribution), student’s t and generalized error distribution (GED). Eviews software was used for analyzing a time series data of Flying cement stock shares consisting of 245 days of in sample and 15 days of out-of-sample data. To compare the forecasting capability of three models root mean square (RMSE) and Theil’s Inequality Coefficient (TIC) were used. Akaike information criterion (AIC), the Schwarz information criterion (SIC), Hannan, and Quin (HQ) information criteria were examined for selection of a suitable model for capturing volatility of stock returns in the presence of symmetrical and asymmetrical distributions. Results of the study revealed that GARCH (1, 1) with GED is the best model for capturing the volatility of stock returns of Flying Cement Industry. Results of the present study will provide a stimulus to academia and practitioners for incorporating asymmetry aspect of the distribution in future prediction and capturing volatility of stock returns

Students’ Survey Evaluation: A New Paradigm

The lynchpin of any educational setup is the duo of student and teacher; the third vital component which regulates the activities of the duo is educational management of the setup. The present study focuses on eliciting the opinions of students from three diplomas organized by Deanship of Community Services and Continuing Education, King Abdulaziz University, Jeddah to study the effectiveness of diplomas. The instrument diploma evaluation questionnaire (DEQ) used to collect data was a modified version of the course evaluation questionnaire (CEQ) developed by the Saudi National commission of Assessment and Academic Accreditation (NCAAA). A sample of 240 diploma students both male and female participated in the study. Statistical evaluation was carried out using SPSS ver 21 and some relevant figures were drawn using AMOS software. Findings of this study coupled with other inputs can simultaneously be used by pedagogical staff and administrators to frame future policies for improving the quality of educational diplomas in an institution or program. Results of the study pinpointed some areas which need to be focused on in future diplomas: for instance, orientation about the diplomas needs more elaboration, provision of training material and linkage between the theory and practice be established. The relationship between the three subscales and Overall Evaluation (OE) is significant with ‘Diploma evaluation’ subscale as the most effective predictor for OE followed by ‘During the diploma’ subscale.  The study also demonstrated the robust evidence of objectivity and data authenticity. The easy-to-follow approach has been adopted so that pedagogical and administrative staff can effectively use the techniques proposed in the current study. The evidence thus extracted can be used to structure efficient prospective policies than can surely enhance student experiences during their educational discourses

Globalization and the Determinants of Innovation in BRICS versus OECD Economies: A Macroeconomic Study

This study examines the influence of select macroeconomic variables and globalization variables on the volume of patented innovations in five industrialized OECD countries (the U.S., the U.K., Germany, Japan, and Australia) and in five emerging countries known as BRICS (Brazil, Russia, India, China, and South Africa). The regression results indicate that trade openness has a more positive impact in boosting innovation in BRICS than in the five OECD countries, whereas ethnic diversity and R&D expenditure have a more positive influence on innovation activities in OECD countries than in BRICS. Surprisingly, despite popular presumption that the Internet has dramatically leveled the innovation playing field by making distant communication and idea pooling faster and cheaper, in our regression results, we find little support to the notion. It appears that emerging countries need to find new ways to leverage ethnic diversity and R&D expenditure as catalysts for innovation. A caveat to our findings is potential bias in our data, first from the choice of which countries to include but also from the quality and quantity of data within the countries. We must also keep in mind the potential socio-political factors behind the issuance of patents. However, we nonetheless believe this study to be an insightfully comparative exploration of some macro determinants of innovation

Learning Minimal and Maximal Rules from Observations of Graph Transformations

Graph transformations have been used to model services and systems where rules describe pre and post conditions of operations changing a complex state. However, despite their intuitive nature, creating such models is a time-consuming and error-prone process. In this paper we investigate the possibility of extracting rules from observations of transformations, i.e., pairs of input and output graphs resulting from successful transformations and individual input graphs were they have failed. From such positive and negative examples, minimal rules are extracted, to be extended by context that is present in all positive examples and missing in at least one negative example. The result is are a maximal and a required rule, jointly with the minimal rule defining the range of possible rules that could have created the observed transformations. We report on an implementation of the approach, evaluate its accuracy, scalability and limitations, and discuss applications to reverse engineering visual constructs from observations of object states of components under test

Wave-dominated Shoreline Sediments in Early Cretaceous Surajdeval Formation, Saurashtra Basin, Gujarat Western India

The Early Cretaceous Surajdeval Formation of the Dhrangadhra group consists of the following three major facies: 1) a sand dominated facies (S), characterized by hummocky cross-stratification, planar and trough cross-bedding (Sp, St), and swaley cross stratification (SCS). 2) the hetro lithic facies (H) characterized by shatter red mudstone and fine-to very fine grained sandstone / red siltstone, exhibiting parallel and low-angle cross-laminations including symmetrical and interference ripples 3) sand matrix-supported conglomerate/pebbly facies (Cg-S) displaying an alternation of coarse (50mm), medium to fine (lt10-20 mm) pebbles in successive beds, and couplets of crudely graded conglomerate. nbspnbspnbspnbspnbsp The sedimentary facies and structures of the Surajdeval Formation reflect deposition by wave, and tide dominated events. Parallel-laminated and fine-grained sandstones are deposited in response to decrease incompetency and capacity of the flow. nbspnbspnbspnbspnbsp The conglomerates and couplets with sandstone may be the product of longshore deposits. Mudstone may have accumulated in protected lagoonal environment. Paleocurrent indicators, such as cross-bedding and pebble fabric show orientation toward south-south-west, and north-north-west, and may be attributed to ebb tidal (onshore), and those exhibiting north-north-west orientation to flood tidal/ longshore (off-shore) currents. Thus, the sedimentological features of the Surajdeval Formation suggest that wave tides and storm processes were involved during its deposition

4-[(Anthracen-9-yl­methyl­idene)amino]-1,5-dimethyl-2-phenyl-1H-pyrazol-3(2H)-one

In the title compound, C26H21N3O, the phenyl ring of the 4-amino­anti­pyrine group and the heterocyclic five-membered ring along with its substituents, except for the N-bound methyl group (r.m.s. deviation = 0.0027 Å), form a dihedral angle of 54.20 (5)°. Two S(6) ring motifs are formed due to intra­molecular C—H⋯N and C—H⋯O hydrogen bonds. In the crystal, mol­ecules are linked into supra­molecular chains along the a-axis direction via C—H⋯O contacts