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The purpose of this paper is to analyse the dependence structure in volatility between Shanghai and Shenzhen stock market in China based on high-frequency data.

Using a multiplicative error model (hereinafter MEM) to describe the margins in volatility of China’s Shanghai and Shenzhen stock market, this study adopts static and time-varying copulas, respectively, estimated by maximum likelihood estimation method to describe the dependence structure in volatility between Shanghai and Shenzhen stock market in China.

This paper has identified the asymmetrical dependence structure in financial market volatility more precisely. Gumbel copula could best fit the empirical distribution as it can capture the relatively high dependence degree in the upper tail part corresponding to the period of volatile price fluctuation in both static and dynamic view.

Previous scholars mostly use GARCH model to describe the margins for price volatility. As MEM can efficiently characterize the volatility estimators, this paper uses MEM to model the margins for the market volatility directly based on high-frequency data, and proposes a proper distribution for the innovation in the marginal models. Then we could use copula-MEM other than copula-GARCH model to study on the dependence structure in volatility between Shanghai and Shenzhen stock market in China from a microstructural perspective.

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 propose a new method for estimating continuous‐time stochastic volatility (SV) models for the S&P 500 stock index process using intraday high‐frequency observations of both the S&P 500 index and the Chicago Board Options Exchange (CBOE) implied (or expected) volatility index (VIX).

A primary purpose of the paper is to provide a framework for using intraday high‐frequency data of both the indices' estimates, in particular, for improving the estimation accuracy of the leverage parameter, that is, the correlation between the two Brownian motions driving the diffusive components of the price process and its spot variance process, respectively.

Finite sample simulation results show that the proposed estimator delivers more accurate estimates of the leverage parameter than do existing methods.

The focus of the paper is on the Heston and non‐Heston leverage parameters.

Finite sample simulation results show that the proposed estimator delivers more accurate estimates of the leverage parameter than do existing methods.

The research findings are important for the analysis of ultra high‐frequency financial data.

The paper provides a framework for using intraday high‐frequency data of both indices' estimates, in particular, for improving the estimation accuracy of the leverage parameter, that is, the correlation between the two Brownian motions driving the diffusive components of the price process and its spot variance process, respectively.

Filter trading is a technical trading rule that has been used extensively to test the efficient market hypothesis in the context of long-term trading. In this paper, the authors adopt the rule to analyze intraday trading, in which an open position is not left overnight. This paper aims to explore the relationship between intraday filter trading profitability and intraday realized volatilities. The bivariate thin plate spline (TPS) model is chosen to fit the predictor-response surface for high frequency data from the Hang Seng index futures (HSIF) market. The hypotheses follow the adaptive market hypothesis, arguing that intraday filter trading differs in profitability under different market conditions as measured by realized volatility, and furthermore, the optimal filter size for trading on each day is related to the realized volatility. The empirical results furnish new evidence that range-based realized volatilities (RaV) are more efficient in identifying trading profit than return-based volatilities (ReV). These results shed light on the efficiency of intraday high frequency trading in the HSIF market. Some trading suggestions are given based on the findings.

Among all the factors that affect the profit of filter trading, intraday realized volatility stands out as an important predictor. The authors explore several intraday volatilities measures using range-based or return-based methods of estimation. The authors then study how the filter trading profit will depend on realized volatility and how the optimal filter size is related to the realized volatility. The bivariate TPS model is used to model the predictor-response relationship.

The empirical results show that range-based realized volatility has a higher predictive power on filter rule trading profit than the return-based realized volatility.

First, the authors contribute to the literature by investigating the profitability of the filter trading rule on high frequency tick-by-tick data of HSIF market. Second, the authors test the assumption that the magnitude of the intraday momentum trading profit depends on the realized volatilities and aims to identify a relationship between them. Furthermore, the authors consider several intraday realized volatilities and find the RaV have the higher prediction power than ReV. Finally, the authors find some relationship between the optimal filter size and the realized volatilities. Based on the observations, the authors also give some trading suggestions to the intraday filter traders.

Microstructure noise contaminates high-frequency estimates of asset price volatility. Recent work has determined a preferred sampling frequency under the assumption that the properties of noise are constant. Given the sampling frequency, the high-frequency observations are given equal weight. While convenient, constant weights are not necessarily efficient. We use the Kalman filter to derive more efficient weights, for any given sampling frequency. We demonstrate the efficacy of the procedure through an extensive simulation exercise, showing that our filter compares favorably to more traditional methods.

An agent-based market simulation is utilized to examine the impact of high frequency trading (HFT) on various aspects of the stock market. This study aims to provide a baseline understanding of the effect of HFT on markets by using a paradigm of zero-intelligence traders and examining the resulting structural changes.

A continuous double auction setting with zero-intelligence traders is used by adapting the model of Gode and Sunder (1993) to include algorithmic high frequency (HF) traders who retrade by marking up their shares by a fixed percentage. The simulation examines the effects of two independent factors, the number of HF traders and their markup percentage, on several dependent variables, principally volume, market efficiency, trader surplus and volatility. Results of the simulations are tested with two-way ANOVA and Tukey’s post hoc tests.

In the simulation results, trading volume, efficiency and total surplus vary directly with the number of traders employing HFT. Results also reveal that market volatility increased with the number of HF traders.

Increases in volume, efficiency and total surplus represent market improvements due to the trading activities of HF traders. However, the increase in volatility is worrisome, and some of the surplus increase appears to come at the expense of long-term-oriented investors. However, the relatively recent development of HFT and dearth of appropriate data make direct calibration of any model difficult.

The simulation study focuses on the structural impact of HF traders on several aspects of the simulated market, with the effects isolated from other noise and problems with empirical data. A baseline for comparison and suggestions for future research are established.

This paper examines dynamic return spillovers and connectedness networks among international stock exchange markets. The authors account for asymmetry by distinguishing between positive and negative returns.

This paper employs the spillover index of Diebold and Yilmaz (2012) to measure the volatility spillover index for total, positive and negative volatility.

The results show time-varying and asymmetric volatility spillovers among the stock markets under investigation. During the coronavirus disease 2019 (COVID-19) pandemic, bad volatility spillovers are more pronounced and dominated over good volatility spillovers, indicating contagion effects.

The presence of confirmed COVID-19 cases positively (negatively) affects the good and bad spillovers under low and intermediate (upper) quantiles. Both types of spillovers at various quantiles agree also influenced by the number of COVID-19 deaths.

This study aims to show that the best-performing realized measures vary across markets when it comes to forecast real estate investment trust (REIT) volatility. This finding provides little guidance for practitioners on which one to use when facing a new market. The authors attempt to fill the hole by seeking a common estimator, which can study for different markets.

The authors do so by drawing upon the general forecasting literature, which finds that combinations of individual forecasts often outperform even the best individual forecast. The authors carry out the study by first introducing a number of commonly used realized measures and then considering several different combination strategies. The authors apply all of the individual measures and their different combinations to three major global REIT markets (Australia, UK and US).

The findings show that both unconstrained and constrained versions of the regression-based combinations consistently rank among the group of best forecasters across the three markets under study. None of their peers can do it including the three simple combinations and all of the individual measures. The conclusions are robust to the choice of evaluation metrics and of the out-of-sample evaluation periods.

The study provides practitioners with easy-to-follow insights on how to forecast REIT volatility, that is, use a regression-based combination of individual realized measures. The study has also extended the thin real estate literature on using high-frequency data to examine REIT volatility.