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The purpose of this paper is to examine the difference between the daytime (open-to-close) and overnight (close-to-open) returns of CSI 300 index and its derivative futures.

The paper explores the difference between the daytime and overnight time returns by using nonparametric techniques. Moreover, investigation on some factors such as short selling, trading rules, risks are made to seek the sources of the day and night effects based on a large number of empirical analysis. In the end, further analyses on daytime and overnight returns are given by the use of high-frequency data and linear regression technique.

The authors show that the daytime returns of CSI 300 index are no less than its overnight returns, while the daytime returns of CSI 300 index futures are no more than its overnight returns, even after removing the heteroscedasticity of the researched time series. Specifically, the PM returns (13:05 to close) play a quite important role in the intra-day time. The findings also suggest that the unique “T+1 trading rule” in China may be a reason that incurs the lower opening price in the morning and the higher closing price in the afternoon, resulting in the statistically significant differences between the daytime and overnight returns.

The findings are of great importance for investors to decide when to buy and sell stock and futures portfolios in Chinese financial markets.

This study empirically analyzes why there the higher daytime returns and the lower overnight returns exist in the Chinese stock markets from different aspects and contributes the existing literature on day and night effects because of periodic market closures.

To capture the last hour momentum over the intraday session, the authors develop a trading strategy for the exchange-traded fund (ETF) that is effective because of the T+0 trading rule. This strategy generates annualized excess return of 9.673%.

In this study, the authors identify a last hour momentum pattern in which the sixth (seventh) half-hour return predicts the next half-hour return by employing high frequency 2012–2017 data from the China Securities Index (CSI) 300 and its ETF.

Overall, both the predictability and the trading strategy are statistically and economically significant. In addition, the strategy performs more strongly on high volatility days, high trading volume days, high order-imbalance days and days without economic news releases than on other days.

Noise trading, late-information trading, infrequent rebalancing and disposition effects from retail investors may account for this phenomenon.

The purpose of this paper is to investigate how the selection of return distribution impacts estimated volatility in China’s stock market.

The authors use a Bayesian analysis of fat-tailed stochastic volatility (SV) model with Student’s t-distribution, and conduct an out-of-sample test with realized volatility.

Empirical analysis results indicate that fat-tailed SV model performs better in capturing the dynamics of daily returns. The authors find that asymmetry, holiday and day of the week effects are detected in estimated volatility. However, the out-of-sample comparison shows that fat-tailed SV models fail to outperform SV models with normal distribution in fitting and predicting realized volatility.

The contribution of this paper to existing literature is twofold. First, it proves that fat-tailed SV models with Student’s t-distribution perform better than normally distributed SV models in fitting daily returns of China’s stock market. Second, this paper takes asymmetry, holiday and day of the week effects into consideration at the same time in the fat-tailed SV model.

The purpose of this paper is to examine the impact of privatized initial public offerings (IPOs) on flipping activity in the Pakistan IPO market.

This study sampled 95 IPOs listed on the Pakistan stock exchange over the period of 2000 to 2019. The ordinary least square technique and quantile regression were used to examine the impact of privatized IPO on flipping activity.

The present study finds that privatization affects flipping activity and creates a quality signal in Pakistan’s IPO market. The findings of this study also show that privatized IPOs were subjected to high levels of flipping activity compared to non-privatized IPOs. Additionally, investors’ demand has been found to moderate the relationship between privatized IPOs and flipping activity in Pakistan’s IPO market.

Based on the fact that the sample consists of a combination of privatized and non-privatized IPOs, the results provide valuable insight into factors that may lead to unusual trading behavior/flipping during the first day of listing.

Despite several studies on events (e.g. short- and long-term price performance) around IPO, there is little evidence on how privatized IPOs affect flipping activity, which is a high volume of trading immediately after listing.

The focus of the current research is to examine whether mixed-frequency investor sentiment affects stock volatility in the China A-shares stock market.

Mixed-frequency sampling models are employed to find the relationship between stock market volatility and mixed-frequency investor sentiment. Principal analysis and MIDAS-GARCH model are used to calibrate the impact of investor sentiment on the large-horizon components of volatility of Shanghai composite stocks.

The results show that the volatility in Chinese stock market is positively influenced by B–W investor sentiment index, when the sentiment index encompasses weighted mixed frequencies with different horizons. In particular, the impact of mixed-frequency investor sentiment is most significantly on the large-horizon components of volatility. Moreover, it is demonstrated that mixed-frequency sampling model has better explanatory powers than exogenous regression models when accounting for the relationship between investor sentiment and stock volatility.

Given the various unique features of Chinese stock market and its importance as the major representative of world emerging markets, the findings of the current paper are of particularly scholarly and practical significance by shedding lights to the applicableness GARCH-MIDAS in the focused frontiers.

A more accurate and insightful understanding of volatility has always been one of the core scholarly pursuits since the influential structural time series modeling of Engle (1982) and the seminal work of Engle and Rangel (2008) attempting to accommodate macroeconomic factors into volatility models. However, the studies in this regard are so far relatively scarce with mixed conclusions. The current study fills such gaps with improved MIDAS-GARCH approach and new evidence from Shanghai A-share market.

This study aims to examine the long memory as well as the effect of structural breaks in the US and the Chinese stock markets. More importantly, it further explores possible causes of the differences in long memory between these two stock markets.

The authors employ various methods to estimate the memory parameters, including the modified R/S, averaged periodogram, Lagrange multiplier, local Whittle and exact local Whittle estimations.

China's two stock markets exhibit long memory, whereas the two US markets do not. Furthermore, long memory is robust in Chinese markets even when we test break-adjusted data. The Chinese stock market does not meet the efficient market hypothesis (EMHs), including the efficiency of information disclosure, regulations and supervision, investors' behavior, and trading mechanisms. Therefore, its stock prices' sluggish response to information leads to momentum effects and long memory.

The authors elaborately illustrate how long memory develops by analyzing not only stock market indices but also typical individual stocks in both the emerging China and the developed US, which diversifies the EMH with wider international stylized facts and findings when compared with previous literature. A couple of tests conducted to analyze structural break effects and spurious long memory demonstrate the reliability of the results. The authors’ findings have significant implications for investors and policymakers worldwide.

This study investigates the impact of the enhanced continuous disclosure regime introduced in December 2002 on several measures of information risk in NZX‐listed stocks. We employ two microstructure models and an intraday data set to measure information risk in a sample of 71 stocks. Our empirical results show that the reforms enacted in December 2002 had no significant effect on either the level of information‐based trading or the adverse selection component of market spreads in our sample of NZX‐listed stocks.

This paper tests two moving average technical trading rules for four Asian markets. Our results indicate that moving average rules do indeed have predictive power and can discern recurring price patterns for profitable trading. Moreover, our results support the hypothesis that technical trading rules can outperform the buy‐and‐hold strategy. Break‐even one‐way trading costs are estimated to be high for all four markets. To confirm the test outcome, robust tests based on bootstrap and the related t‐tests among the markets are also carried out. We conclude from the statistical results that moving average rules are valid and indeed have predictive power. It is implied that the trading rules may be used to design a trading strategy that will beat the buy‐and‐hold strategy in the Hong Kong, Singapore, South Korea, and Taiwan markets. The contribution of the current study is that this is the first validation test of trading rules using four markets at a similar development stage and culture tradition; and in the tests, we use most current and longer periods than the periods used in previous literature. Our robust tests are unique and considered distribution‐free.

In this paper we conduct tests for two different trading rules, namely, the Dual Moving Average (DMA) model and the Channel Breakout (CHB) rule. These rules are tested across five futures contracts – the S&P 500, British Pound, US T‐Bonds, COMEX Gold and Corn using daily data over the period 1990 to 1998. Overwhelmingly, we find that the trading rules are unable to produce (gross or net) profits at any statistical level. While positive gross and net profits were available in four of the five markets, the profits were neither economically or statistically significant.

Aim of the present monograph is the economic analysis of the role of MNEs regarding globalisation and digital economy and in parallel there is a reference and examination of some legal aspects concerning MNEs, cyberspace and e‐commerce as the means of expression of the digital economy. The whole effort of the author is focused on the examination of various aspects of MNEs and their impact upon globalisation and vice versa and how and if we are moving towards a global digital economy.