The effect of COVID-19 on stock market performance has important implications for both financial theory and practice. This paper examines the relationship between COVID-19 and the instability of both stock return predictability and price volatility in the U.S over the period January 1st, 2019 to June 30th, 2020 by using the methodologies of Bai and Perron (Econometrica 66:47–78, 1998. 10.2307/2998540; J Appl Econo 18:1–22, 2003. 10.1002/jae.659), Elliot and Muller (Optimal testing general breaking processes in linear time series models. University of California at San Diego Economic Working Paper, 2004), and Xu (J Econ 173:126–142, 2013. 10.1016/j.jeconom.2012.11.001). The results highlight a single break in return predictability and price volatility of both S&P 500 and DJIA. The timing of the break is consistent with the COVID-19 outbreak, or more specifically the stock selling-offs by the U.S. senate committee members before COVID-19 crashed the market. Furthermore, return predictability and price volatility significantly increased following the derived break. The findings suggest that the pandemic crisis was associated with market inefficiency, creating profitable opportunities for traders and speculators. Furthermore, it also induced income and wealth inequality between market participants with plenty of liquidity at hand and those short of funds.

Highlights  This paper examines the predictability of the Shanghai Composite, Shenzhen Composite and the Hang Seng China Enterprise index returns, with emphasis on whether considering structural breaks in model parameters improves the stock return predictability.  Results are important for investors who are interested in investing in Mainland China and Hong Kong stock markets.  Results indicate higher linear stock return predictability for the Hong Kong market than for the Chinese markets.  Results differ when model instability is taken into consideration: the Shenzhen market is detected with structural breaks and its predictability varies across different subsamples defined by the breaks.

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PurposeThe purpose of this paper is to propose a new dynamic margin setting method for margin buying in China and evaluate the validity of its performance with the current margin system adopted by stock exchanges in extreme episodes.Design/methodology/approachThis paper adopts the dynamic conceptual model of Huang et al. (2012) (which is based on Figlewski (1984)) but incorporates Markov chain to describe the data generation process of stock price changes. By applying the model to margin buying contracts for the period of March 16, 2018, to May 2, 2018 (baseline study) and June 15, 2015, to July 27, 2015 (robustness test), the model’s superiority to the current margin system adopted by stock exchanges is also tested.FindingsThe paper has several important findings. First, the margins derived by this system vary with market conditions, rising (declining) when stock prices go down (up), and are generally lower than the requirements imposed by stock exchanges. Second, this margin system induces lower overall percentage of costs than that adopted by stock exchanges. Third, parameter estimation plays an important role on shaping empirical results.Research limitations/implicationsThe primary limitation of this paper lies in the fact that it does not solve the issue of determining optimal parameters of the Markov chain model. On the implication of findings, policy-makers and regulators on supervising margin buying activities may need a tune-up on the current margin system which features static margin requirements. Dynamic margins that incorporate market factors are virtually useful to balance the trade-off between liquidity and prudence.Originality/valueTo the best of the authors’ knowledge, this study is the first of its kind to develop a dynamic margin setting method for margin buying in China, aiming to balance the trade-off between liquidity and prudence. It not only takes into account the uniqueness of Chinese markets but also allows for time variations in both initial and maintenance margins.

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Purpose This paper aims to examine the impact of interest rate liberalisation on the constancy of mean interest rates in China to test the effect of financial reforms and provide strategies for future practices. Design/methodology/approach Bai and Perron’s (1998, 2003) methodology is used to test for structural breaks in the mean of different interest rates using Chinese data, and break dates are measured against the exact dates of the interest rate liberalisation. The performance of mean interest rates across the regimes defined by liberalisation dates is also investigated. Findings The main results show that interest rates generally increase (decrease) after deregulations on lending (deposit) rates, but these changes are not significant to induce a negative impact on the domestic economy. Instead, the infrequent but important shifts (structural breaks) in mean interest rates are caused by factors other than liberalisation such as economic shocks, inflationary expectation and liquidity crunch in China. Originality/value To the best of the author’s knowledge, this paper provides unprecedented evidence on significant changes in interest rates attributable to the liberalisation within the Chinese context.

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