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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 paper aims to identify the contributors to freight rate fluctuations in the Suezmax tanker market; this study selected the refinery output, crude oil price, one-year charter rate and fleet development as the main influencing factors for the market analysis.

The paper used the vector error correction model to evaluate the degree of impact of each influencing factor on Suezmax tanker freight rates, as well as the interplay between these factors.

The conclusion and results were tested using the 20-year data from 1999 to 2019, and the methodology and theory of this paper were proved to be effective. Results of this study provide effective reference for scholars to find the law of fluctuations in Suezmax tanker freight rates.

This paper provides a decision-making support tool for tanker operators to cope with fluctuation risks in the tanker shipping market.

Since the adoption in 1976 of the Integrated Programme for Commodities, only four international commodity agreements have been concluded, of which only one, the International Natural Rubber Agreement (1979), was new. The others are the International Sugar Agreement (1978), the International Cocoa Agreement (1980) and the International Tin Agreement (1981). In view of the broad political support for international commodity agreements, the progress towards concluding such agreements is slow. Moreover, in some of them universality is lacking. Several countries which account for a substantial proportion of world production and consumption of, and trade in, the commodity subject to an international agreement, have not ratified or acceded to the relevant agreements. The duration of the negotiations has been exceedingly long in comparison with experience on negotiations of the previous agreements. This outcome, several years after the adoption of the Integrated Programme for Commodities, is disappointing and unexpected, especially since the basic purpose of that Programme and the setting up of the Common Fund in accordance with it was “to act as a catalyst for international commodity agreements”. The purpose of this article is to examine the reasons for this outcome.

The purpose of this paper is to understand how small and medium enterprises (SMEs) cope with the need for labour flexibility. Most previous studies ignore the labour flexibility practices of SMEs, especially in times of economic growth and tight labour markets.

A multiple case study approach is applied, with ten Dutch SMEs located in one small province with a similar labour market. A survey was executed as an intake, followed by 48 interviews with the entrepreneurs, HR and other managers and employees, and two focus groups in each company. The findings are based on an analysis of the approved case descriptions.

SMEs, like big companies, do not rely on one flexibility practice. Volume fluctuations are countered with all flexibility strategies, the mix fluctuations and the product innovations are mostly countered with flexible functions and flexible production technology. In general, the data suggest that flexibility strategies of SMEs can be characterised as ad hoc, reactive and with a short-term orientation.

Future research should include other sectors and regions enabling to generalise the findings. Future research should have a longitudinal design to include the pathway dependencies of flexibility practices.

This study identifies the need to analyse flexibility demands; reduce flexibility demands before investments in flexibility practices; create production process flexibility; invest in labour flexibility practices only after the first three steps are taken; and develop basic and more advanced levels of flexible contracts, flexible functions and flexible working times.

This study contributes to the authors’ knowledge on the use of labour flexibility practices in SMEs. In addition, it brings empirical data on how these labour flexibility practices relate to the needs for flexibility and how they relate to other sources of organisational flexibility, such as a flexible market approach and flexible production technologies. Dynamic capabilities should include the suggested operationalisation of the flexibility practices.

In the process of discussing the relationship between volume and price in the stock market, the purpose of this paper is to consider how to take the flow of foreign capital into consideration, to determine whether the inclusion of volume information really contributes to the prediction of the volatility of the stock price.

By comparing the relative advantages and disadvantages of the two main non-parametric methods mainstream, and taking the characteristics of the time series of the volume into consideration, the stochastic volatility with Volume (SV-VOL) model based on the APF-LW simulation method is used in the end, to explore and implement a more efficient estimation algorithm. And the volume is incorporated into the model for submersible quantization, by which the problem of insufficient use of volume information in previous research has been solved, which means that the development of the SV model is realized.

Through the Sequential Monte Carlo (SMC) algorithm, the effective estimation of the SV-VOL model is realized by programming. It is found that the stock market volume information is helpful to the prediction of the volatility of the stock price. The exchange market volume information affects the stock returns and the price-volume relationship, which is achieved indirectly through the net capital into stock market. The current exchange devaluation and fluctuation are not conducive to the restoration and recovery of the stock market.

It is still in the exploratory stage that whether the inclusion of volume information really contributes to the prediction of the volatility of the stock price, and how to incorporate the exchange market volume information. This paper tries to determine the information weight of the exchange market volume according to the direct and indirect channels from the perspective of causality. The relevant practices and conclusions need to be tested and perfected.

Previous studies have neglected the influence of the information contained in the exchange market volume on the volatility of stock prices. To a certain extent, this research makes a useful supplement to the existing research, especially in the aspects of research problems, research paradigms, research methods and research conclusion.

SV model with volume information can not only effectively solve the inefficiency of information use problem contained in volume in traditional practice, but also further improve the estimation accuracy of the model by introducing the exchange market volume information into the model through weighted processing, which is a useful supplement to the existing literature. The SMC algorithm realized by programming is helpful to the further advancement and development of non-parametric algorithms. And this paper has made a useful attempt to determine the weight of the exchange market volume information, and some useful conclusions are drawn.

The purpose of this paper is to research and analyze the influence of institutional investors in the present securities market due to behavior alienation with “running after rising and falling” and “herd behavior”.

A DeLong, Shleifer, Summers, and Waldmann (DSSW) model with positive feedback trading is established first to show the trading process, and these securities prices are calculated considering the investors' emotion. Through numerical analysis, the influence of institutional investors on securities price fluctuation is simulated. Further, the analysis of institutional investors' incomes is processed based on this model.

Through these analyses, the following conclusions are drawn: it lies on the scale of positive feedback traders and their sensitivity to past market performances whether the institutional investors can stabilize the market, and it is not necessary for the institutional investors to benefit from manipulating the market due to the existence of noise trader risk, so the positive feedback traders may survive in the security market over the long term.

The DSSW model considering positive feedback trading, presented in the paper, is more effective in analyzing the relation among the behavior of institutional investors, securities pricing and securities price fluctuation. The paper proposes some advice for policy decisions, which is helpful for government and institutions to maintain the stability of securities markets.

Using capital asset pricing model (CAPM) and the Z-risk index based on weekly data, this study aims to estimate yearly unsystematic, total, three systematic and insolvency risks in the Gulf Cooperation Council (GCC) countries for the period 2010–2018. The findings of CAPM show positive systematic market risk exposure in all GCC countries for all years, which support the contribution of stock markets to bank prices and returns. The mixed signs of systematic interest rate and exchange rate risks in GCC countries provide hedging opportunities, diversification strategies and regional cooperation, which help risk managers to hedge and stabilize their portfolios against interest rate and exchange rate fluctuations. Therefore, it is necessary that managers and policymakers develop a monitoring system on factors affecting bank insolvency risks to avoid bankruptcies and insolvencies.

This study uses the three-factor CAPM and Z-risk index to measure six types of risks. The CAPM uses market information to estimate the sensitivity of banks to the fluctuations of equity markets, debt markets and foreign exchange markets. Sharpe (1964), Lintner (1965) and Treynor (1965) developed a single-factor CAPM and the coefficient of the model was called systematic market risk. The single-factor CAPM highlights stock markets as the only non-diversifiable source of systematic risks, whereas Stone (1974) and Jorion (1990) highlighted interest rate and exchange rate fluctuations as the other types of non-diversifiable systematic risks. The following functional form in equation (1) estimates five types of risks using CAPM.

The findings of CAPM show positive systematic market risk exposure in all GCC countries for all years, which support the contribution of stock markets to bank prices and returns based on CAPM theory. The mixed signs of systematic interest rate and exchange rate risks in GCC countries support hedging opportunities and diversification strategies which may help risk managers to hedge and stabilize their portfolios against the fluctuations of interest rate and exchange rate. Although, this policy may decrease the profits of banking sectors but at the same time it would stabilize the portfolios and prevent bankruptcies and big losses because of the fluctuations of interest rate. Moreover, a bank has a better chance to have more liquidity position during financial crises because of the diversifications into different regional markets.

Therefore, this study contributes to the existing literature by using risk measurement by a three-factor CAPM and the Z-risk index as discussed further in methodology.

It is necessary that managers and policymakers develop a monitoring system on factors affecting bank insolvency risks to avoid bankruptcies and insolvencies.

This study aims to explore the oil–stock market nexus from a novel angle by examining the predictive role of oil prices over the excess returns associated with the market, size, book-to-market and momentum factors via bivariate cross-quantilograms.

This study makes use of the bivariate cross-quantilogram methodology recently developed by Han et al. (2016) to analyze the predictability patterns across the oil and stock markets by focusing on various quantiles that formally distinguish between normal, bull and bear as well as extreme market states.

The study analysis of systematic risk premia across the four regions shows that crude oil returns indeed capture predictive information regarding excess factor returns in stock markets, particularly those associated with market, size and momentum factors. However, the predictive power of oil return over excess factor returns is asymmetric and primarily concentrated on extreme quantiles, suggesting that large fluctuations in oil prices capture markedly different predictive information over stock market risk premia during up and down states of the oil market.

The findings have significant implications for the profitability of factor- or style-based active portfolio strategies and suggest that the predictive information contained in oil market fluctuations could be used to enhance returns via conditional strategies based on these predictability patterns.

This study contributes to the vast literature on the oil–stock market nexus from a novel perspective by exploring the effect of oil price fluctuations on the risk premia associated with the systematic risk factors including market, size, value and momentum.

The purpose of this paper is to test whether the volatility of regional stock markets’ is common or country-specific for 46 international markets of the Asian, European, African and Latin American regions using the Morgan Stanley Capital International daily prices in the period from January 1998 to December 2009. Further, the study has been divided into two sub-periods to distinguish the effects of the current sub-prime financial crisis and to determine whether the crisis has an impact on the fluctuations of common component of stock market volatility.

The paper applies the time-varying weighting methodology of Lumsdaine and Prasad (2003) to determine whether the volatility fluctuation is country-specific or common across the countries.

The results evidence that the volatility of stock returns is due to common factors, rather than country-specific ones, but this is not always the case. However, this common component is more stable in European and Latin American countries than in the Asia-Pacific and African regions. Furthermore, the results suggest that the influence of a common component has been enhanced significantly during the current sub-prime financial crisis.

The study has implication for domestic and international investors, portfolio managers, as well as policy-makers to implement economic and financial policy that promote stability, reduce vulnerability to crises and encourage sustained growth and living standards.

To the best of the authors’ knowledge, this is the first study to include four regional samples and test the common component of fluctuations of regional stock markets volatility.

This study aimed to examine the efficient market hypothesis (EMH) for the stock markets of 12 member countries of the Organization of Islamic Cooperation (OIC), such as Egypt, Indonesia, Jordan, Kuwait, Malaysia, Morocco, Pakistan, Saudi Arabia, Tunisia, Turkey and the United Arab Emirates (UAE), during the global financial crisis (GFC) and the COVID-19 (CV-19) epidemic. The objective was to classify the effects on individual indices.

The study employed the multifractal detrended fluctuation analysis (MF-DFA) on daily returns. After calculation and analysis, the data were then divided into two significant events: the GFC and the CV-19 pandemic. Additionally, the market deficiency measure (MDM) was utilized to assess and rank market efficiency.

The findings indicate that the average returns series exhibited persistent and non-persistent patterns during the GFC and the CV-19 pandemic, respectively. The study employed MF-DFA to analyze the sequence of normal returns. The results suggest that the average returns series displayed persistent and non-persistent patterns during the GFC and the CV-19 pandemic, respectively. Furthermore, all markets demonstrated efficiency during the two crisis periods, with Turkey and Tunisia exhibiting the highest and deepest levels of efficiency, respectively. The multifractal properties were influenced by long-range correlations and fat-tailed distributions, with the latter being the primary contributor. Moreover, the impact of the fat-tailed distribution on multifractality was found to be more pronounced for indices with lower market efficiency. In conclusion, this study categorizes indices with low market efficiency during both crisis periods, which subsequently affect the distribution of assets among shareholders in the stock markets of OIC member countries.

Multifractal patterns, especially the long memory property observed in stock markets, can assist investors in formulating profitable investment strategies. Additionally, this study will contribute to a better understanding of market trends during similar events should they occur in the future.

This research marks the initial effort to assess the impact of the GFC and the CV19 pandemic on the efficiency of stock markets in OIC countries. This undertaking is of paramount importance due to the potential destabilizing and harmful effects of these events on global financial markets and societal well-being. Furthermore, to the best of the authors’ knowledge, this study represents the first investigation utilizing the MFDFA method to analyze the primary stock markets of OIC countries, encompassing both the GFC and CV19 crises.