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With the launch of the Shanghai-Hong Kong and the Shenzhen-Hong Kong Stock Connect programs, the correlations among the three stock markets have been more complex. Therefore, this paper applies the Minimum Spanning Tree and Sliding Window methods to analyze the complex correlations of the 38 financial institutions in the three markets. Based on closing prices ranging from January 1, 2011 to December 31, 2021, three conclusions are obtained. The top 20 institutions in the network were mainly banking institutions from 2011 to 2013, while the node strength of non-banking financial institutions tended to increase from 2014 to 2021. The network shows different structures over time, even the changes in the three correlation coefficients generally show the same trend. The degree correlation between any two layers is positive, but the similarity shows different characteristics. These results would help to enrich multi-layer network theory and reveal the evolutionary characteristics of the correlation between financial institutions. [ABSTRACT FROM AUTHOR]

Based on the theoretical framework of expected utility with uncertain probabilities, this paper uses actual prices of CSI300 and Hang Seng index to empirically measure ambiguity degrees in the Chinese mainland and Hong Kong stock markets. A Markov regime-switching model is proposed to divide the stock market into bear and bull states, and then test whether there exist significant differences in the ambiguity degrees under different states. An ambiguity factor and a risk factor are then proposed to analyze the time-varying relationship among risk, ambiguity, and return under different states. In addition to the mean and variance, the high-order moments, including skewness and kurtosis, are used to test whether they affect the relationship among them. The results show that the ambiguity degrees in the Chinese mainland stock market are significantly higher than those in the Hong Kong stock market, and there are significant differences between bear and bull states for the two markets. Moreover, the regression results among risk, ambiguity, and return indicate that with ambiguity, the effects of risk factors on excess returns is significantly negative under bear state, and significantly positive under bull state, while it is significantly negative under the two states without ambiguity. [ABSTRACT FROM AUTHOR]

This paper evaluates the relationships between weather variables like humidity, temperature, air pressure, wind speed and status (clear, cloud, rain, haze and snow) with stock returns and volatility in the Greater China region's stock market. The study focuses on four stock exchanges; Shanghai and Shenzhen stock exchanges from mainland China, Hong Kong stock exchange and Taiwan stock exchange from Hong Kong and Taiwan respectively. Using the robust approach the best peer econometric model for each market was chosen. The results of the study depict that weather variables have a significant effect on Greater China's capital markets. We are able to analyze that in some cases the variable affects returns as well as volatility • Prior studies have proved that weather has emotional effects. • Finance scholars have shown that emotions related to investor decisions. • I provide evidence that local weather affects stock returns. • I also explore which weather variable most liable for said effect. • I find that said effect is at least fall in every stock market of Greater China region. [ABSTRACT FROM AUTHOR]

Stock networks, which are constructed from stock price time series, are useful tools for analyzing complex behaviors in stock markets. Following former researches, the epidemic model has been usually used to detect dynamic characteristics in a stock price complex systems. Recently, multilayer networks have been demonstrated well when working on heterogeneous nodes rather than integrated networks. In this paper, we proposed a two-layer SIR propagation model with an infective medium to analyze the spread of financial shocks. In consideration of strict financial regulation in the A shares, the model assumed that capital cannot flow directly between layers but through the Hong Kong stock market. By applying the model to constituent stocks included in three prominent indices, Standard & Poor 500, Shanghai and Shenzhen 300, and Hang Seng(medium), we established a two-layer Granger networks. Betweenness showed that the Hong Kong stock market had a promoting transition function of financial shocks between the US stock markets and the mainland China stock markets. In addition, with a big basic reproduction number, stock markets system appeared to be vulnerable during extreme financial shock such as the outbreak of COVID-19 epidemic and the meltdown of stock markets. Furthermore, sensitivity analysis and the spreading simulation indicated that the US stock markets were much more robust to financial shocks than the mainland China stock markets. • New two-layer SIR propagation model with an infected medium for financial shocks. • Two-layer Granger networks based on the US stock markets, the mainland China stock markets, and the Hong Kong stock market. • Focusing on economic influence caused by COVID-19 epidemic. • Monte Carlo algorithm simulates the model in different conditions. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we investigate the cross-correlations between the stock market in China and markets in Japan, South Korea and Hong Kong. We use not only the qualitative analysis of the cross-correlation test, but also the quantitative analysis of the MF-X-DFA. Our findings confirm the existence of cross-correlations between the stock market in China and markets in Japan, South Korea and Hong Kong, which have strongly multifractal features. We find that the cross-correlations display the characteristic of multifractality in the short term. Moreover, the cross-correlations of small fluctuations are persistent and those of large fluctuations are anti-persistent in the short term, while the cross-correlations of all kinds of fluctuations are persistent in the long term. Furthermore, based on the multifractal spectrum, we also find that the multifractality of cross-correlation between stock markets in China and Japan are stronger than those between China and South Korea, as well as between China and Hong Kong. [Copyright &y& Elsevier]

Abstract: In this paper, firstly a modified particle swarm optimization algorithm (MPSO) is developed, in which the mean value of past optimal positions for each particle and the mutation operation are considered for avoiding premature. In the optimization test, MPSO performs better than particle swarm optimization algorithm (PSO). Then MPSO is applied to solve four portfolio optimization models with the real data from the Hong Kong Stock Market, and optimal values are obtained when the number of swarm , respectively. Finally, actual return rates of these models are calculated in numerical experiments, and it is illustrated from these graphs of actual return rates that when considering higher return, Cai’s model performs better in short-term investment. [Copyright &y& Elsevier]

Abstract: Hong Kong not only has one of the most institutionally-involved housing markets, but also one of the most developed stock markets in the world. In the meantime, the function of real estate has become increasingly important, yet increasingly vague at the same time. This paper attempts to explore the significant factors in the price adjustments of residential properties. It is found that while most market fundamentals are not significant in explaining property price movements, the roles of investment concerns and of government policy changes in assisted homeownership (HOS) are much more critical in this regard. On the one hand, real estate prices are driven more by investor demand, rather than by user demand; and residential properties are used to hedge against price risks in the stock market, instead of against inflation. On the other hand, while the upgrading hypothesis is confirmed in this study, the situation of Hong Kong turns out to be a bit different from the Singapore experience. As the production of HOS flats is suspended until further notice, the resale HOS market has managed to pull a fraction of homebuyers from the private sector. This particular finding shows that the government’s decision to suspend HOS flat production and sale in Fall 2002 has not accomplished what was intended to achieve. Instead, this leads to several implications, which are then discussed. [Copyright &y& Elsevier]

Abstract: The research on volatility spillover effects between many stock markets is an important subject of finance in recent years. This paper investigates the volatility spillover between the stock markets in Shanghai and Hong Kong. Granger causality model and GARCH (1, 1)-in-mean model are adopted to test and analyze the relevance of the return and volatility between the two markets. The empirical results suggest that Hong Kong equity market has one-way significant effects on Shanghai stock market after the reform of the shareholder structure of listed companies. [Copyright &y& Elsevier]

Abstract: Financial volatility refers to the intensity of the fluctuations in the expected return on an investment or the pricing of a financial asset due to market uncertainties. Hence, volatility modeling and forecasting is imperative to financial market investors, as such projections allow the investors to adjust their trading strategies in anticipation of the impending financial market movements. Following this, financial volatility trading is the capitalization of the uncertainties of the financial markets to realize investment profits in times of rising, falling and side-way market conditions. In this paper, an intelligent straddle trading system (framework) that consists of a volatility projection module (VPM) and a trade decision module (TDM) is proposed for financial volatility trading via the buying and selling of option straddles to help a human trader capitalizes on the underlying uncertainties of the Hong Kong stock market. Three different measures, namely: (1) the historical volatility (HV), (2) implied volatility (IV) and (3) model-based volatility (MV) of the Hang Seng Index (HSI) are employed to quantify the implicit volatility of the Hong Kong stock market. The TDM of the proposed straddle trading system combines the respective volatility measures with the well-established moving-averages convergence/divergence (MACD) principle to recommend trading actions to a human trader dealing in HSI straddles. However, the inherent limitation of the MACD trading rule is that it generates time-delayed trading signals due to the use of moving averages, which are essentially lagging trend indicators. This drawback is intuitively addressed in the proposed straddle trading system by applying the VPM to compute future projections of the volatility measures of the HSI prior to the activation of the TDM. The VPM is realized by a self-organising neural-fuzzy semantic network named the evolving fuzzy semantic memory (eFSM) model. As compared to existing statistical and computational intelligence based modeling techniques currently employed for financial volatility modeling and forecasting, eFSM possesses several desirable attributes such as: (1) an evolvable knowledge base to continuously address the non-stationary characteristics of the Hong Kong stock market; (2) highly formalized human-like information computations; and (3) a transparent structure that can be interpreted via a set of linguistic IF–THEN semantic fuzzy rules. These qualities provide added credence to the computed HSI volatility projections. The volatility modeling and forecasting performances of the eFSM, when benchmarked to several established modeling techniques, as well as the observed trading returns of the proposed straddle trading system, are encouraging. [Copyright &y& Elsevier]

The daily Hang Seng index in the Hong Kong stock market has been analyzed in this paper. Two kinds of sign sequences as given conditions have been used to predict the future price movements. One is the parameter of multifractal spectrum Δf based on the indexes recorded in every minute, and the other is the variation of the close index Δi. Results show that correlation between large fluctuations of the close price and the condition in these two methods is strong and some sign sequences of the parameter Δf can be used to predict the probability of the near future price movements. [Copyright &y& Elsevier]

With the integration of international financial markets, the links between stock markets have become closer. Firstly, based on one-minute high-frequency returns, this paper applies the thermal optimal path (TOP) method to examine the lead–lag dependence between CSI 300 index and HSI index from 2016 to 2020. Secondly, regression analysis and correlation test are applied to cross-verify the results of TOP method. Finally, the robustness is tested through analysis with different T values and various market conditions as well as the replacing of proxy variable. The empirical results show that Hong Kong stock leads Chinese mainland stock for about one minute, and this leading effect is magnified when the stock markets fall. The experimental result is robust and has passed consistency test. This research is of great significance that not only guides investors, but also provides empirical evidence and effective information for policy makers. • One-minute high-frequency market data of Chinese mainland and Hong Kong are used. • Dynamic evolution of lead-lag relationship reveals Non-parametric TOP method. • Regression analysis and correlation test show the same results as TOP method. • Hong Kong leads about 1min, and this effect magnifies when the markets fall. [ABSTRACT FROM AUTHOR]

This paper investigates the variations in dependence and risk spillover between mainland China and London stock markets before and after two Stock Connect programs launched in China, by measuring the Conditional Value-at-Risk (CoVaR) and Conditional Expected Shortfall (CoES) using realized volatility which is calculated with high-frequency data and Copula model. The empirical results show that, first, the Shanghai–Hong Kong Stock Connect program enhances the dependence between mainland China and London stock markets, while the overall dependence decreases slightly after the Shenzhen–Hong Kong Stock Connect program. More specifically, the lower-tail dependence between mainland China and London stock markets continues to increase after the second Stock Connect scheme. The upper-tail dependence, however, rises up after the first Stock Connect scheme but decreases sharply after the second Connect scheme. Second, the Stock Connect programs strength the downside risk spillovers between the mainland China and London markets in most cases. Finally, the equal tests on risk spillovers show significant asymmetric features, indicating that the mainland China stock markets are more likely affected by the London stock market. • The dependence and risk spillovers between mainland China and London stock markets are discussed from the perspectives of Shanghai–Hong Kong Stock Connect and Shenzhen–Hong Kong Stock Connect programs. • CoVaR and CoES are calculated using Copula approach. • Realized volatilities are introduced to depict the dependence and risk spillovers. [ABSTRACT FROM AUTHOR]