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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.

This paper aims to illustrate the potential of high-frequency data for tourism and hospitality analysis, through two research objectives: First, this study describes and test a novel high-frequency forecasting methodology applied on big data characterized by fine-grained time and spatial resolution; Second, this paper elaborates on those estimates’ usefulness for visitors and tourism public and private stakeholders, whose decisions are increasingly focusing on short-time horizons.

This study uses the technical communications between mobile devices and WiFi networks to build a high frequency and precise geolocation of big data. The empirical section compares the forecasting accuracy of several artificial intelligence and time series models.

The results robustly indicate the long short-term memory networks model superiority, both for in-sample and out-of-sample forecasting. Hence, the proposed methodology provides estimates which are remarkably better than making short-time decision considering the current number of residents and visitors (Naïve I model).

A discussion section exemplifies how high-frequency forecasts can be incorporated into tourism information and management tools to improve visitors’ experience and tourism stakeholders’ decision-making. Particularly, the paper details its applicability to managing overtourism and Covid-19 mitigating measures.

High-frequency forecast is new in tourism studies and the discussion sheds light on the relevance of this time horizon for dealing with some current tourism challenges. For many tourism-related issues, what to do next is not anymore what to do tomorrow or the next week.

This research initiates high-frequency forecasting in tourism and hospitality studies. Additionally, we detail several examples of how anticipating urban crowdedness requires high-frequency data and can improve visitors’ experience and public and private decision-making.

This paper aims to investigate the quickness, and test the accuracy, of liquidity taking high-frequency traders (HFT). This gives us important insights into a class of market participant who has come to be very influential in present markets.

The authors use the weekly natural gas (NG) storage report for the test because the information contained in the release often has a large effect on prices. Moreover, the NG market is heavily traded and liquid, and prone to high volatility. These factors make trading in this market attractive to HFT. The authors test for the profitability of those who trade in the first milliseconds after the report’s release; and for information leakage prior to the report.

The authors find those who trade within the first 50 ms accurately incorporate the information contained in the storage report into prices, and earn the majority of profits. In fact, HFT profits are decreasing in the time it takes them to trade after the announcement (measured to 200 ms). Further tests find no evidence of informed trading prior to the release of the report, and so the HFT reaction to the report incorporates the information contained therein into prices.

This is one of the few analyzes of the profitability of liquidity-taking HFT, and the only analysis that uses millisecond NG data. The data used is the exchanges original FIX/FAST messages.

Few of the established risk identification methods refer to low-severity yet high-frequency safety risks data that may lead to several safety risks being ignored, thus reducing the potential of learning from a considerable number of cases. The purpose of this study is to explore a new valid method based on preaccident safety supervision data to identify these minor construction safety risks during routine construction operations.

A total of 329 official construction safety supervision reports containing 5,159 safety problem records from Shanghai between 2016 and 2018 served as raw material for in-depth analysis. Given the characteristics of the data collected, text mining integrated with natural language processing was applied to review the supervision reports and group safety risks automatically.

This study clarifies the way in which the supervision data should be employed to analyze high-frequency–low-severity safety risks. From these data, seven unsafe-act-related and nine unsafe-condition-related risks are identified. Regarding unsafe-act-related risks, inappropriate human behaviors could usually occur in personnel management, contract management, expense management, material management and acceptance work. For unsafe-condition-related risks, hoisting, scaffolding and reinforcement works are the main generators of onsite safety hazards during construction operations.

The study includes implications for project managers and supervisors to facilitate more effective proactive risk management by paying more attention to collecting and employing the supervision data established in each routine inspection.

Whereas previous research focused on analyzing severe accidents, this study seeks to identify the high-frequency–low-severity construction safety risks using the preaccident supervision data. The findings could provide a new thought and research direction for construction safety risk management.

The purpose of this paper is to analyze the mechanism and filter efficacy of accumulation generation operator (AGO)/inverse accumulation generation operator (IAGO) in the frequency domain.

The AGO/IAGO in time domain will be transferred to the frequency domain by the Fourier transform. Based on the consistency of the mathematical expressions of the AGO/IAGO in the gray system and the digital filter in digital signal processing, the equivalent filter model of the AGO/IAGO is established. The unique methods in digital signal processing systems “spectrum analysis” of AGO/IAGO are carried out in the frequency domain.

Through the theoretical study and practical example, benefit of spectrum analysis is explained, and the mechanism and filter efficacy of AGO/IAGO are quantitatively analyzed. The study indicated that the AGO is particularly suitable to act on the system's behavior time series in which the long period parts is the main factor. The acted sequence has good effect of noise immunity.

The AGO/IAGO has a wonderful effect on the processing of some statistical data, e.g. most of the statistical data related to economic growth, crop production, climate and atmospheric changes are mainly affected by long period factors (i.e. low-frequency data), and most of the disturbances are short-period factors (high-frequency data). After processing by the 1-AGO, its high frequency content is suppressed, and its low frequency content is amplified. In terms of information theory, this two-way effect improves the signal-to-noise ratio greatly and reduces the proportion of noise/interference in the new sequence. Based on 1-AGO acting, the information mining and extrapolation prediction will have a good effect.

The authors find that 1-AGO has a wonderful effect on the processing of data sequence. When the 1-AGO acts on a data sequence X, its low-pass filtering effect will benefit the information fluctuations removing and high-frequency noise/interference reduction, so the data shows a clear exponential change trends. However, it is not suitable for excessive use because its equivalent filter has poles at the non-periodic content. But, because of pol effect at zero frequency, the 1-AGO will greatly amplify the low-frequency information parts and suppress the high-frequency parts in the information at the same time.

The purpose of this study is to forecast the development performance of important economies in a smart city using mixed-frequency data.

This study introduces reverse unrestricted mixed-data sampling (RUMIDAS) to support vector regression (SVR) to develop a novel RUMIDAS-SVR model. The RUMIDAS-SVR model was estimated using a quadratic programming problem. The authors then use the novel RUMIDAS-SVR model to forecast the development performance of all high-tech listed companies, an important sector of the economy reflecting the potential and dynamism of urban economic development in Shanghai using the mixed-frequency consumer price index (CPI) producer price index (PPI), and consumer confidence index (CCI) as predictors.

The empirical results show that the established RUMIDAS-SVR is superior to the competing models with regard to mean absolute error (MAE) and root-mean-squared error (RMSE) and multi-source macroeconomic predictors contribute to the development performance forecast of important economies.

Smart city policy makers should create a favourable macroeconomic environment, such as controlling inflation or stabilising prices for companies within the city, and companies within the important city economic sectors should take initiative to shoulder their responsibility to support the construction of the smart city.

This study contributes to smart city monitoring by proposing and developing a new model, RUMIDAS-SVR, to help the construction of smart cities. It also empirically provides strategic insights for smart city stakeholders.

Since illiquidity risk is one of the most important pricing factors of assets, the aim of this paper is to evaluate the suitability of proxies of illiquidity prevalent in the asset pricing literature and their explanatory power in asset pricing tests.

Using the available high‐frequency intra‐day data, the paper constructs some proxies of illiquidity as benchmarks and then evaluates proxies of illiquidity based on inter‐day data.

The empirical results provide convincing evidence that turnover is the most suitable proxy of illiquidity in the Chinese stock market. It is not only hghly related to intra‐day data‐based proxies of illiquidity but also completely superior to other measures of illiquidity in asset pricing tests.

First, the paper applies illiquidity measurements from microstructure theory and the available high‐frequency data, and examines the suitability of illiquidity proxies in asset pricing literature in the Chinese stock market. Rational basics are provided to test the applicability of illiquidity measures in the Chinese stock market. Second, the paper introduces illiquidity proxies into asset pricing models to extend their explanatory power. The paper's results may help researchers to select illiquidity proxies more cautiously.

The purpose of this paper is to help high frequency circuit designers understand how to choose the best permittivity value for a laminate material for accurate modeling.

In this paper, experimental measurements of the performance of simple circuits are compared to various mathematical and software models.

Higher permittivity values were obtained using samples with bonded copper foil compared to samples etched free of foil. These higher values yielded better agreement between measured and modelled performance using current automated design software. High profile foil on thin laminates was found to increase the surface impedance of the conductor and change the propagation constant and apparent permittivity of the laminate by 15 percent or more. It was also demonstrated that, under some circumstances, the anisotropy of the substrate could result in differences in measured and modelled performance.

Only a limited number of circuit laminate materials were closely examined. Future work should include a wider variety of laminates.

The paper details the magnitude of the effects of test method, conductor profile and substrate anisotropy on the values of a material's permittivity best suited for circuit design purposes.

The purpose of this paper is to review recent studies published from 2007 to 2015 on tourism and hotel demand modeling and forecasting with a view to identifying the emerging topics and methods studied and to pointing future research directions in the field.

Articles on tourism and hotel demand modeling and forecasting published mostly in both science citation index and social sciences citation index journals were identified and analyzed.

This review finds that the studies focused on hotel demand are relatively less than those on tourism demand. It is also observed that more and more studies have moved away from the aggregate tourism demand analysis, whereas disaggregate markets and niche products have attracted increasing attention. Some studies have gone beyond neoclassical economic theory to seek additional explanations of the dynamics of tourism and hotel demand, such as environmental factors, tourist online behavior and consumer confidence indicators, among others. More sophisticated techniques such as nonlinear smooth transition regression, mixed-frequency modeling technique and nonparametric singular spectrum analysis have also been introduced to this research area.

The main limitation of this review is that the articles included in this study only cover the English literature. Future review of this kind should also include articles published in other languages. The review provides a useful guide for researchers who are interested in future research on tourism and hotel demand modeling and forecasting.

This review provides important suggestions and recommendations for improving the efficiency of tourism and hospitality management practices.

The value of this review is that it identifies the current trends in tourism and hotel demand modeling and forecasting research and points out future research directions.

Existing empirical evidence suggests that episodes of financial stress (crises) can act as driver of growth of inequality. Consequently, in this study, the authors explore the time-varying predictive power of an index of financial stress for growth in income (and consumption) inequality in the UK. The authors focus on the UK since income (and consumption) inequality data are available at a high frequency, i.e. on a quarterly basis for over 40 years (June, 1975 to March, 2016).

The authors use Wang and Rossi's approach to analyze the time-varying impact of financial stress on inequality. Hence, the method provides a more appropriate inference of the effect rather than a constant parameter Granger causality method. Besides, understandably, the time-varying approach helps to depict the time-variation in the strength of predictability of financial stress on inequality.

This study’s findings point that financial distress correspond to subsequent increases in inequality, with the index of financial stress containing important information in predicting growth in income inequality for both in and out-of-sample periods. Interestingly, the strength of the in-sample predictive power is high post the period of the global financial crisis, as was observed in the early part of the sample. The authors believe these findings highlight an important role of financial stress for inequality – an area of investigation that has in general remained untouched.

Accurate prediction of inequality at a higher frequency should be more relevant to policymakers in designing appropriate policies to circumvent the wide-ranging negative impacts of inequality, compared to when predictions are only available at the lower annual frequency.