Search results

The purpose of this paper is to explore whether the out-of-sample model bias plays an important role in predicting volatility.

Under the heterogeneous autoregressive realized volatility (HAR-RV) framework, we analyze the predictive power of out-of-sample model bias for the realized volatility (RV) of the Dow Jones Industrial Average (DJI) and the S&P 500 (SPX) indices from in-sample and out-of-sample perspectives respectively.

The in-sample results reveal that the prediction model including the model bias can obtain bigger R², and the out-of-sample empirical results based on several evaluation methods suggest that the prediction model incorporating model bias can improve forecast accuracy for the RV of the DJI and the SPX indices. That is, model bias can enhance the predictability of original HAR family models.

The author introduce out-of-sample model bias into HAR family models to enhance model capability in predicting realized volatility.

This chapter develops a framework for quantile regression in binary longitudinal data settings. A novel Markov chain Monte Carlo (MCMC) method is designed to fit the model and its computational efficiency is demonstrated in a simulation study. The proposed approach is flexible in that it can account for common and individual-specific parameters, as well as multivariate heterogeneity associated with several covariates. The methodology is applied to study female labor force participation and home ownership in the United States. The results offer new insights at the various quantiles, which are of interest to policymakers and researchers alike.

This article is motivated by the lack of flexibility in Bayesian quantile regression for ordinal models where the error follows an asymmetric Laplace (AL) distribution. The inflexibility arises because the skewness of the distribution is completely specified when a quantile is chosen. To overcome this shortcoming, we derive the cumulative distribution function (and the moment-generating function) of the generalized asymmetric Laplace (GAL) distribution – a generalization of AL distribution that separates the skewness from the quantile parameter – and construct a working likelihood for the ordinal quantile model. The resulting framework is termed flexible Bayesian quantile regression for ordinal (FBQROR) models. However, its estimation is not straightforward. We address estimation issues and propose an efficient Markov chain Monte Carlo (MCMC) procedure based on Gibbs sampling and joint Metropolis–Hastings algorithm. The advantages of the proposed model are demonstrated in multiple simulation studies and implemented to analyze public opinion on homeownership as the best long-term investment in the United States following the Great Recession.

Provides a brief outline of the Philippines before looking at the values held by the culture and the management style commonly adopted. Outlines personnel management practices with references to short case studies. Concludes that one must first understand the culture in relation to feelings, honour and relationships and the successful company is one which has formed effective relationships with not only customers but employees, suppliers and dealers and also provides staff with development opportunities.

This paper aims to propose DisDSS: a Web-based smart disaster management (DM) system for decision-making that will assist disaster professionals in determining the nature of disaster-related social media (SM) messages. The research classifies the tweets into need-based, availability-based, situational-based, general and irrelevant categories and visualizes them on a web interface, location-wise.

It is worth mentioning that a fusion-based deep learning (DL) model is introduced to objectively determine the nature of an SM message. The proposed model uses the convolution neural network and bidirectional long short-term memory network layers.

The developed system leads to a better performance in accuracy, precision, recall, F-score, area under receiver operating characteristic curve and area under precision-recall curve, compared to other state-of-the-art methods in the literature. The contribution of this paper is three fold. First, it presents a new covid data set of SM messages with the label of nature of the message. Second, it offers a fusion-based DL model to classify SM data. Third, it presents a Web-based interface to visualize the structured information.

The architecture of DisDSS is analyzed based on the practical case study, i.e. COVID-19. The proposed DL-based model is embedded into a Web-based interface for decision support. To the best of the authors’ knowledge, this is India’s first SM-based DM system.

The purpose of this paper is to design a component synthesis method to suppress the vibration of the flexible spacecraft, which has the constant amplitude force/moment actuator.

The authors proposed a method to construct constant amplitude of time delay and composite coefficient sequences based on the principles of the component synthesis vibration suppression (CSVS). The associated design strategy of the CSVS torque control is also developed. The dynamic model consisting of a single axis rotating rigid central body and a fixed flexibility panel is used to validate the proposed method. Constraint modal and free modal method are both tested to analyse the natural frequencies of the panel and dynamic properties of rigid–flexible decoupling system, under the conditions of known and unknown frequencies. The feasibility of constructing CSVS control force based on the constraint modal frequency is also analysed.

The proposed method can suppress multistage vibration and has arbitrary order robustness for each order frequencies simultaneously. Simulation results show that only the duration time of the actuator has to be set for the proposed method, reasonable vibration suppression effect can be achieved.

The method can be used in spacecraft, especially flexible spacecraft to suppress the vibration; the approach is convenient for engineering application and can be easily designed.

The authors proposed a method to construct constant amplitude of time delay and composite coefficient sequences based on the principles of the CSVS.

This paper aims to examine the institutional and social determinants, and consequences of social entrepreneurship with respect to China's rural enterprises. It also attempts to provide a conceptual framework concerning how rural Chinese enterprises act as social entrepreneurial institutions and contribute to both business development and social welfare of local communities.

The conceptual framework is developed through a critical review of literature and an integration of multiple disciplinary studies, with a focus on the perspectives of institutional governance, managerial networks, and market orientation.

The study identifies three framework layers for the development of China's rural enterprises, which are fundamentally driven by market preserving authoritarianism, local state corporatism, community culture, social entrepreneurship and market orientation.

The proposed framework can help contribute to the theoretical development of strategic issues of social entrepreneurship in transitional economies. It may also provide insights about local state governance, ownership structures and market competition in China.

As China's rural enterprises are widely regarded as a phenomenon related to the core nature of a “socialist market economy”, an ideology embraced since the beginning of Chinese social‐economic reforms, a study of institutional and entrepreneurial nature of this kind serves as a stepping stone for understanding the emerging phenomenon of the country's social entrepreneurship, which is characterized by open market mechanisms and socialist legacies.

This paper aims to evaluate diethylenetriamine (DETA) as an inhibitor on Q235 (Gr. D) steel in ammonia flue gas desulfurization (AFGD) system.

This research was carried out by weight loss, electrochemical measurements, scanning electron microscopy and X-ray photoelectron spectrometry. The effects of DETA on crystallization of ammonium sulfate and its co-crystallization were also investigated by X-ray diffraction and infrared spectroscopy.

The inhibition efficiency of DETA reached a maximum value of 98.96 per cent. DETA is postulated to adsorb on Q235 steel surface, resulting in the formation of a protective film by the accumulation of many flat particles, and the thickness of protective film is 8 μm. DETA had no effect on the crystallization of ammonium sulfate product.

DETA can be used in AFGD system as an inhibitor to protect the equipment well.

This study uses data from the Chinese banking sector to explore the relationship between green credit and risk-taking in commercial banks. It also examines whether the level of regional green development acts as a moderator regarding this relationship.

Using a dataset composed of annual observations from 57 Chinese commercial banks between 2008 and 2021, this study employs both piecewise and curvilinear models.

Our results indicate that when the scale of green credit is low (<0.164), it increases the risk-taking of commercial banks. Conversely, when the scale of green credit is high (>0.164), it reduces the risk-taking of commercial banks. Moreover, this nonlinear relationship impact exhibits bank heterogeneity. Furthermore, the results show that the level of regional green development and local government policy support negatively moderate the relationship between green credit and commercial bank risk-taking. Furthermore, we find that green credit can directly enhance the net interest margin of commercial banks.

This study is the first to provide evidence of a nonlinear relationship between green credit and risk-taking in commercial banks, and it identifies the significant roles of regional green development level and local government policy support in the Chinese context.

Nowadays people are connected by social media like Facebook, Instagram, Twitter, YouTube and much more. Bullies take advantage of these social networks to share their comments. Cyberbullying is one typical kind of harassment by making aggressive comments, abuses to hurt the netizens. Social media is one of the areas where bullying happens extensively. Hence, it is necessary to develop an efficient and autonomous cyberbullying detection technique.

In this paper, the authors proposed a transformer network-based word embeddings approach for cyberbullying detection. RoBERTa is used to generate word embeddings and Light Gradient Boosting Machine is used as a classifier.

The proposed approach outperforms machine learning algorithms such as logistic regression, support vector machine and deep learning models such as word-level convolutional neural networks (word CNN) and character convolutional neural networks with short cuts (char CNNS) in terms of precision, recall, F1-score.

One of the limitations of traditional word embeddings methods is context-independent. In this work, only text data are utilized to identify cyberbullying. This work can be extended to predict cyberbullying activities in multimedia environment like image, audio and video.