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The purpose of this paper is to consider that the model of volatility characteristics is more reasonable and the description of volatility is more explanatory.

This paper analyzes the basic characteristics of market yield volatility based on the five-minute trading data of the Chinese CSI300 stock index futures from 2012 to 2017 by Hurst index and GPH test, A-J and J-O Jumping test and Realized-EGARCH model, respectively. The results show that the yield fluctuation rate of CSI300 stock index futures market has obvious non-linear characteristics including long memory, jumpy and asymmetry.

This paper finds that the LHAR-RV-CJ model has a better prediction effect on the volatility of CSI300 stock index futures. The research shows that CSI300 stock index futures market is heterogeneous, means that long-term investors are focused on long-term market fluctuations rather than short-term fluctuations; the influence of the short-term jumping component on the market volatility is limited, and the long jump has a greater negative influence on market fluctuation; the negative impact of long-period yield is limited to short-term market fluctuation, while, with the period extending, the negative influence of long-period impact is gradually increased.

This paper has research limitations in variable measurement and data selection.

This study is based on the high-frequency data or the application number of financial modeling analysis, especially in the study of asset price volatility. It makes full use of all kinds of information contained in high-frequency data, compared to low-frequency data such as day, weekly or monthly data. High-frequency data can be more accurate, better guide financial asset pricing and risk management, and result in effective configuration.

The existing research on the futures market volatility of high frequency data, mainly focus on single feature analysis, and the comprehensive comparative analysis on the volatility characteristics of study is less, at the same time in setting up the model for the forecast of volatility, based on the model research on the basic characteristics is less, so the construction of a model is relatively subjective, in this paper, considering the fluctuation characteristics of the model is more reasonable, characterization of volatility will also be more explanatory power. The difference between this paper and the existing literature lies in that this paper establishes a prediction model based on the basic characteristics of market return volatility, and conducts a description and prediction study on volatility.

The purpose of this paper is to investigate the role of volatility risk on stock return predictability specified on two global financial crises: the dot-com bubble and recent financial crisis.

Using a broad sample of stock options traded on the American Stock Exchange and the Chicago Board Options Exchange from January 2001 to December 2010, the effect of different idiosyncratic volatility forecasting measures are examined on future stock returns in four different periods (Bear and Bull markets).

First, the authors find clear and robust empirical evidence that the implied idiosyncratic volatility is the best stock return predictor for every sub-period both in Bear and Bull markets. Second, the cross-section firm-specific characteristics are important when it comes to stock returns forecasts, as the latter have mixed positive and negative effects on Bear and Bull markets. Third, the authors provide evidence that short selling constraints impact negatively on stock returns for only a Bull market and that liquidity is meaningless for both Bear and Bull markets after the recent financial crisis.

These results would be helpful to disclose more information on the best idiosyncratic volatility measure to be implemented in global financial crises.

This study empirically analyses the effect of different idiosyncratic volatility measures for a period that involves both the dotcom bubble and the recent financial crisis in four different periods (Bear and Bull markets) and contributes the existing literature on volatility measures, volatility risk and stock return predictability in global financial crises.

The purpose of this article is to propose a detailed methodology to estimate, model and incorporate the non-constant volatility onto a numerical tree scheme, to evaluate a real option, using a quadrinomial multiplicative recombination.

This article uses the multiplicative quadrinomial tree numerical method with non-constant volatility, based on stochastic differential equations of the GARCH-diffusion type to value real options when the volatility is stochastic.

Findings showed that in the proposed method with volatility tends to zero, the multiplicative binomial traditional method is a particular case, and results are comparable between these methodologies, as well as to the exact solution offered by the Black–Scholes model.

The originality of this paper lies in try to model the implicit (conditional) market volatility to assess, based on that, a real option using a quadrinomial tree, including into this valuation the stochastic volatility of the underlying asset. The main contribution is the formal derivation of a risk-neutral valuation as well as the market risk premium associated with volatility, verifying this condition via numerical test on simulated and real data, showing that our proposal is consistent with Black and Scholes formula and multiplicative binomial trees method.

This comprehensive study aims to take a punctilious approach intended to present qualitative and quantitative knowledge on the emerging concept of noise trading and identify the emerging themes associated with noise trading.

This study combines bibliometric and content analysis to review 350 publications from top-ranked journals published from 1986 to 2020.

The bibliometric and content analysis identified three major themes: the impact of noise traders on the functioning of the stock market, traits of noise traders and different proxies used to measure the impact of noise trading.

This study undertakes research papers related to the field of finance, published in peer-reviewed journals and that too in the English language.

This study shall accommodate rational traders, portfolio consultants and other investors to gain deeper insights into the functioning of noise traders. This will further help them to formulate their trading/investment strategies accordingly.

The successful combination of the bibliometric and content analysis revealed major gaps in the literature and provided future research directions.

With over 3,000 academic journals in the fields of Business and Economics, most academics face a hard time selecting an adequate journal to submit their work to. In today's demanding academic environment and with the presence of different journal ranking lists (JRLs), the selection becomes more difficult when considering employment, promotion and funding. The purpose of this paper is to explore key differences among multiple JRLs pertinent to the latter common objectives. An extensive analysis is conducted to compare the content of journals in the Australian Business Deans Council (ABDC) Journal Quality list, Scopus and Web of Science (WoS) in the fields of Business and Economics. Then, a case of a university with medium research output is considered where scholarly performance evaluation is based on the ABDC Journal Quality List.

After ranking journals in the fields of Business and Economics based on SCImago Journal Rank (SJR) indicator, JCR's Journal Impact Factor (JIF) and JCR's Eigenfactor (EF), a methodology is proposed to categorize journals in the three JRLs into the same categorization adopted by ABDC. The latter establishes a way to compare the four JRLs under consideration and serves as a basis to compare and analyze the content of journals in the ABDC Journal Quality list, Scopus and WoS. As a proxy impact metric, a normalized citation count is associated with each article based on Google Scholar. The publications of the considered university are then evaluated from the perspective of the four JRLs in terms of citation-based impact and quality while considering the exposure to popular world university ranking tables.

For journals classified under fourth tier by ABDC, over 53 and 59% are not indexed by Scopus and WoS, respectively. In this case study, over 42% of the publications appear in journals that are not listed in JCR despite the fact that over 94% of them are listed by the SJR list. Generally, publications that appear in journals listed by JCR achieve, on a yearly average, significantly higher citation rates when compared to those that appear in journals listed in ABDC and SJR Lists.

A four-tier mapping is proposed for consistent comparison among JRLs. Normalized citation count associated with each article based on Google Scholar is employed for evaluation. The findings provide recommendations for scholars, administrators and global universities, including Euro-Med Universities, on which JRL can be more influential for both faculty development and positioning of the university.