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The stock market price time series can be divided into two processes: continuously rising and continuously falling. The authors can effectively prevent the stock market from crashing by accurately estimating the risk on continuously rising returns (CRR) and continuously falling returns (CFR).

The authors add an exogenous variable into Log-autoregressive conditional duration (Log-ACD) model, and then apply our extended Log-ACD model and Archimedean copula to estimate the marginal distribution and conditional distribution of CRR and CFR. Plus, the authors analyze the conditional value at risk (CVaR) and present back-test results of the CVaR. The back-test shows that our proposed risk estimation method has a good estimation power for the risk of the CRR and CFR, especially the downside risk. In addition, the authors detect whether the dependent structure between the CRR and CFR changes using the change point test method.

The empirical results indicate that there is no change point here, suggesting that the results on the dependent structure and risk analysis mentioned above are stable. Therefore, major financial events will not affect the dependent structure here. This is consistent with the point that the CRR and CFR can be analyzed to obtain the trend of stock returns from a more macro perspective than daily stock returns scholars usually study.

The risk estimation method of this paper is of great significance in understanding stock market risk and can provide corresponding valuable information for investment advisors and public policy regulators.

The authors defined a new stock returns, CRR and CFR, since it is difficult to analyze and predict the trend of stock returns according to daily stock returns because of the small autocorrelation among daily stock returns.

The purpose of this study is to identify appropriate autoregressive conditional duration (ACD) models that can capture the dynamics of tick-by-tick mid-cap exchange traded funds (ETFs) for the period July 2017 to December 2017 and accurately predict future trade duration values. The forecasted durations are then used to demonstrate the practical usefulness of the ACD models in quantifying an intraday time-based risk measure.

Through six functional forms and six error distributions, 36 ACD models are estimated for eight mid-cap ETFs. The Akaike information criterion and Bayesian information criterion and the Ljung-Box test are used to evaluate goodness-of-fit while root mean square error and the Superior predictive ability test are applied to assess forecast accuracy.

The Box-Cox ACD (BACD), augmented Box-Cox ACD (ABACD) and additive and multiplicative ACD (AMACD) extensions are among the best fits. The results obtained prove that higher degrees of flexibility do not necessarily enhance goodness of fit and forecast accuracy does not always depend on model adequacy. BACD and AMACD models based on the generalised-F distribution generate the best forecasts, irrespective of the trading frequencies of the ETFs.

To the best of the authors’ knowledge, this is the first study that analyses the empirical performance of ACD models for high-frequency ETF data. Additionally, in comparison to previous works, a wider range of ACD models is considered on a reasonably longer sample period. The paper will be of interest to researchers in the area of market microstructure and to practitioners engaged in high-frequency trading.

This paper is aimed to investigate the impact of different categories of traders on price and volume durations at Euronext Paris. The two series are respectively related to the instantaneous volatility and the market liquidity; hence, they are particularly suited to test microstructure hypotheses.

A Log-autoregressive conditional duration model was adopted to include the information on the traders’ identity at the transaction level. High-frequency data were used and how the informed traders and the liquidity provider affect the arrival of market events was studied. The robustness of our results was also checked by testing different distributions and controlling for microstructure effects.

It was found that informed traders and the liquidity provider exert a dominant role in accelerating the market activity. This result depends on the state of the market, i.e. it is effective only during periods of high frequency of transactions. The estimates for price durations show that a high instantaneous volatility can be mainly ascribed to a great concentration of informed traders. Informed traders are also found to shorten volume durations by clustering small-size orders to disguise their private signal. For both durations, the liquidity provider is also found to foster the market activity, likely because of his contractual duties.

The article is of interest for researchers in the field of market microstructure, as well as for specialists in the high-frequency trading. Results provide an empirical confirmation of information models which theorize an accelerating effect for informed trading. To the best of the authors’ knowledge, this is the first contribution to study the impact of traders’categories at the transaction level and with different definitions of durations.

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 study is to employ a duration-based approach to model the inter-arrival times of bank failures in the US banking system for the period of 1934-2014, in line with the suggestions of Focardi and Fabozzi (2005), who used a similar model for explaining contagion in credit portfolios.

Conditional duration models that allow duration between bank failures to depend linearly or nonlinearly on its past history are estimated and evaluated.

The authors find evidence of strong persistence along with nonmonotonic hazard rates, which imply a financial contagion pattern, according to which a high frequency of bank failures generates turbulence, which shortly after leads to additional fails, whereas prolonged periods without abnormal events signify the absence of contagious dependence, which increases the relative periods between bank failure appearance. Further, the authors obtain statistically significant results when they allow duration to depend linearly on past information variables that capture systemic bank crisis factors along with stock and bond market effects.

The originality of this study consists in proposing a new time series approach for the prediction of bank probability of default by incorporating a default-risk contagion mechanism. As contagious bank failures are a key topic in macroprudential supervision, this study could be of value for supervisory authorities in setting pro-active actions and tightening regulatory measures.

The purpose of this paper is to explain what information is contained in mutual funds' trading behaviors and to try to further assess the impact on the stock market.

The objective is achieved by an empirical examination using the high‐frequency intraday data. The main methods used for the research are the autoregressive conditional duration model and the UHF‐GARCH model.

This paper gives an empirical study of mutual funds' behavior on two aspects. The first aspect is the direct impact on micro variables. The results show that mutual funds changing their positions will have different influences to the spread, adding position broadens the spread, while decreasing position makes the spread narrow; behaviors of funds change the clustering characteristic of the duration. The second aspect is the impact on the relationships among micro variables. The results indicate that trading started by liquidity buyers will make volatility larger.

This paper supposes funds as informed traders and individual investors as liquidity traders in China's stock market. If it is not true, some interpretations of empirical results would be wrong. The authors' results may help researchers to understand the information content of funds' trading behaviors in the microstructure aspect.

The paper is an original work, which will be interesting to scholars in market microstructure and to practitioners in the Chinese stock market. The main contributions of the paper are: the use of high‐frequency data to study funds' behaviors and combine the trading duration and investors' trading behavior to analyze the information content of trading behaviors; second, the use of 14 stock samples in the Shanghai Stock Exchange to do the empirical study, which ensures the reliability of the results.