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.
The purpose of this paper is to understand the underlying dynamics for the share market bubbles in China during the most recent decade. By using the behavioral finance theory and the Shanghai Composite index prices during the periods from 2005 to 2008 and from 2014 to 2015 as the study samples, we find that the large volatilities in the Chinese share market are closely related to information blockage, which impedes share prices to timely respond to economic conditions as well as external shocks and increases (decreases) the demand of shares when the supply is difficult to adjust. Although the Chinese government has introduced a series of programs designed to increase more reliable information to the public, the share market still tends to confront issues of information asymmetry. The potential reason is that the reforms did not change the long-stand situation in China, where individuals or groups related to government bureaucracy who play a dominant role in the society are given priority to gain access and obtain information that benefits. By identifying the main reasons for the large volatilities in the market, policy makers are given advice as to which areas they may need to focus on to improve future market performance.
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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