Narrow Results

As the most influential regulation in 2016, China launched circuit breakers in the financial markets. However, the circuit breaker mechanism was implemented for only four days and then suspended. Many criticisms then stated that circuit breakers impeded trading behavior in Chinese financial markets. This study explores this short-life circuit breaker mechanism in China, and examines whether circuit breakers impede trading behavior in Chinese financial markets as many criticisms stated. We use an intraday dataset and investigate the circuit breakers. Contrary to those criticisms, we find that circuit breakers are not easily reachable and have no “magnet effect” between two thresholds of breakers. We also find that without protection of circuit breakers, potential large market fluctuations will have negative impacts on individual stocks’ liquidity and value. As the major contribution, our study indicates that Chinese financial markets still need a circuit breaker mechanism to protect investors’ benefits and maintain the market liquidity and stability.

Diffusion models have been widely used to describe the stochastic dynamics of the underlying economic variables. Renò (2008) introduced a nonparametric estimator of the volatility function, which is based on the estimation of quadratic variation between observations by means of realized variance. However, they may be misleading when one uses intraday data to implement directly the estimator, because intraday data display microstructure effects that could seriously distort the estimation. To filter out the impact of microstructure noise on the estimation of the volatility function, in this paper we propose an improved estimator when there is microstructure noise in the observed price. Also, we show that the proposed estimator has the same asymptotic properties as the Renò estimator when the step of discretization inclines to zero. Some simulations and empirical applications on Shanghai Stock Exchange data from March 3, 2002 to December 31, 2008 are used to illustrate the finite sample performance of the proposed estimator.

Based on high-frequency data, we study the difference in cryptocurrency market before and during the COVID-19. We analyze the multifractality of three major cryptocurrencies via the multifractal detrended fluctuation analysis (MFDFA). To investigate the source of multifractality, we construct shuffled, surrogated and truncate data. The results show that market efficiency of cryptocurrency has decreased during COVID-19. The cryptocurrency multifractal characteristics mainly come from non-Gaussian distribution. Additionally, the components of multifractal nature have changed during the pandemic. The results provide evidence for the impact of COVID-19 on cryptocurrency market.

S&P 500 index data sampled at one-minute intervals over the course of 11.5 years (January 1989–May 2000) is analyzed, and in particular the Hurst parameter over segments of stationarity (the time period over which the Hurst parameter is almost constant) is estimated. An asymptotically unbiased and efficient estimator using the log-scale spectrum is employed. The estimator is asymptotically Gaussian and the variance of the estimate that is obtained from a data segment of N points is of order . Wavelet analysis is tailor-made for the high frequency data set, since it has low computational complexity due to the pyramidal algorithm for computing the detail coefficients. This estimator is robust to additive non-stationarities, and here it is shown to exhibit some degree of robustness to multiplicative non-stationarities, such as seasonalities and volatility persistence, as well. This analysis suggests that the market became more efficient in the period 1997–2000.

A general model for intraday stock price movements is studied. The asset price dynamics is described by a marked point process Y, whose local characteristics (in particular the jump-intensity) depend on some unobservable hidden state variable X. The dynamics of Y and X may be strongly dependent. In particular the two processes may have common jump times, which means that the actual trading activity may affect the law of X and could be also related to the possibility of catastrophic events. The agents, in this model, are restricted to observing past asset prices. This leads to a filtering problem with marked point process observations. The conditional law of X given the past asset prices (the filter) is characterized as the unique weak solution of the Kushner–Stratonovich equation. An explicit representation of the filter is obtained by the Feyman–Kac formula using a linearization method. This representation allows us to provide a recursive algorithm for the filter computation.

Though the high-frequency volatility approaches are increasingly introduced to forecast financial risk in recent years, whether they can improve the accuracies of risk forecasts remains controversial. This paper compares the risk forecasting abilities of four pairs of low- and high-frequency volatility models, by calculating and evaluating the downside and upside value-at-risk and expected shortfall of stock indexes and portfolio. The empirical results show that, first, all the volatility models can well filter the serial dependence in the extremes, and the conditional standard deviation obtained from the GARCH model performs best in filtering the dependence. Secondly, the backtesting results of stock index and portfolio risk forecasts are consistent. More specifically, the traditional low-frequency volatility models produce more accurate risk forecasts in most cases, whereas the high-frequency volatility methods also manifest some advantages in the upside extreme risk forecasting.

The cross-correlation matrix between equities comprises multiple interactions between traders with varying strategies and time horizons. In this paper, we use the Maximum Overlap Discrete Wavelet Transform to calculate correlation matrices over different time–scales and then explore the eigenvalue spectrum over sliding time-windows. The dynamics of the eigenvalue spectrum at different times and scales provides insight into the interactions between the numerous constituents involved.

Eigenvalue dynamics are examined for both medium, and high-frequency equity returns, with the associated correlation structure shown to be dependent on both time and scale. Additionally, the Epps effect is established using this multivariate method and analyzed at longer scales than previously studied. A partition of the eigenvalue time-series demonstrates, at very short scales, the emergence of negative returns when the largest eigenvalue is greatest. Finally, a portfolio optimization shows the importance of time–scale information in the context of risk management.

In this paper we assess the suitability of weighted-indexed semi-Markov chains (WISMC) to study risk measures as applied to high-frequency financial data. The considered measures are the drawdown of fixed level, the time to crash, the speed of crash, the recovery time and the speed of recovery; they provide valuable information in portfolio management and in the selection of investments. The results obtained by implementing the WISMC model are compared with those based on the real data and also with those achieved by GARCH and EGARCH models. Globally, the WISMC model performs much better than the other econometric models for all the considered measures unless in the cases when the percentage of censored units is more than 30% where the models behave similarly.

The presence of spikes or cusps in high-frequency return series might generate problems in terms of inference and estimation of the parameters in volatility models. For example, the presence of jumps in a time series can influence sample autocorrelations, which can cause misidentification or generate spurious ARCH effects. On the other hand, these jumps might also hide relevant heteroskedastic behavior of the dependence structure of a series, leading to identification issues and a poorer fit to a model. This paper proposes a wavelet-shrinkage method to separate out jumps in high-frequency financial series, fitting a suitable model that accounts for its stylized facts. We also perform simulation studies to assess the effectiveness of the proposed method, in addition to illustrating the effect of the jumps in time series. Lastly, we use the methodology to model real high-frequency time series of stocks traded on the Brazilian Stock Exchange and OTC and a series of cryptocurrencies trades.

With rapid development of the global market, the number of financial securities has significantly grown, which greatly challenges the measuring of financial quantities. Among others, the estimation of covariance matrix which plays an important role in risk management becomes no longer accurate. In this paper, we consider the estimation of integrated covariance matrix of semi-martingales under framework of high dimension by using high frequency data. We assume that the multivariate asset prices are observed asynchronously and all the observed prices are contaminated by microstructure noise. We employ the pre-averaging method to remove the microstructure noise and the generalized synchronization method to deal with the non-synchronicity. Moreover, to avoid the inconsistency in the high-dimensional covariance matrix estimation, we propose a regularized estimate. The consistency under matrix ${\ell }_2$-norm is established. Compared to existing results, our estimator improves the accuracy of the estimation. Finally, we assess the theoretical results via some simulation studies.

Order type plays an important role in algorithmic trading and is a key factor of price impact. In this paper, we propose a new framework for studying the discrete price change process, which focuses on the impacts of aggressive orders (market orders and aggressive limit orders) and cancelations. The price change process is driven by states and events of best quotes, and we define the event-based price change as the “natural price change” (NPC). Under the framework, we propose a heteroscedastic linear econometric model for the NPC to explore the impact of different types of orders on the price dynamics. To verify the usability of our model and explore the driving factors of price dynamics, we conduct a thorough empirical analysis for 786 large-tick stocks traded on the Shenzhen Stock Exchange. Empirical results statistically demonstrate that aggressive orders can introduce stronger impact on the NPC than cancelations. Meanwhile, splitting a big order into several small orders can lead to a larger NPC. Our framework can also be applied for the prediction of price change.

In this chapter, we review econometric methodology that is used to test for jumps and to decompose realized volatility into continuous and jump components. In order to illustrate how to implement the methods discussed, we also present the results of an empirical analysis in which we separate continuous asset return variation and finite activity jump variation from excess returns on various US market sector exchange traded funds (ETFs), during and around the Great Recession of 2008. Our objective is to characterize the financial contagion that was present during one of the greatest financial crises in US history. In particular, we study how shocks, as measured by jumps, propagate through nine different market sectors. One element of our analysis involves the investigation of causal linkages associated with jumps (via use of vector autoregressions), and another involves the examination of the predictive content of jumps for excess returns. We find that as early as 2006, jump spillover effects became more pronounced in the markets. We also observe that jumps had a significant effect on excess returns during 2008 and 2009; but not in the years before and after the recession.

Hedging strategies for contingent claims are studied in a general model for high frequency data. The dynamics of the risky asset price is described through a marked point process Y, whose local characteristics depend on some hidden state variable X. The two processes Y and X may have common jump times, which means that the trading activity may affect the law of X and could be also related to the presence of catastrophic events. Since the market considered is incomplete one has to choose some approach to hedging derivatives. We choose the local risk-minimization criterion. When the price of the risky asset is a general semimartingale, if an optimal strategy exists, the value of the portfolio is computed in the terms of the so-called minimal martingale measure and may be interpreted as a possible arbitrage-free price. In the case where the price of the risky asset is modeled directly under a martingale measure, the computation of the risk-minimizing hedging strategy is given. By using a projection result, we also obtain the risk-minimizing hedging strategy under partial information when the hedger is restricted to observing only the past asset prices and not the exogenous process X which drives their dynamics.

This paper studies impacts of rare jumps as well as an endogenous microstructure noise on Ubukata and Oya's [16] cross and auto covariance estimators of bivariate microstructure noise processes. The theoretical results show that biases of the noise covariance estimators are asymptotically negligible even when there exist the jump component and the endogeneity in the observed price process. Monte Carlo results are suggestive of robustness of the noise covariance estimators to the jumps and the endogenous noise with an empirically reasonable magnitude of the correlation in finite sample.