Impact of COVID-19 and economic policy uncertainty on China's stock market returns: evidence from quantile-on-quantile and causality-in-quantiles approaches

Impact of coronavirus pandemic on stock index: A polynomial regression with time delay

Motivation

Under contemporary market conditions in China, the stock index has been volatile and highly reflect trends in the coronavirus pandemic, but rare scientific research has been conducted to model the possible nonlinear relations between the two indicators. Added, on the advent that covid-related news in one time period impacts the stock market in another period, time delay can be an equally good predictor of the stock index but rarely investigated.

Objectives

To contribute to filling the gaps identified in existing research, this study models relationship between the stock market index and coronavirus pandemic by leveraging volatility in the stock market and covid data through time delay and best degree in a polynomial environment. The resultant optimal time delay and best degree model is used to derive a high-accuracy prediction of stock market index.

Novelty

In line with the possible relations, the novelty of this study is that it proposes, validates and implements polynomial regression with time delay to model nonlinear relationship between the stock index and covid.

Methods

This study utilizes high-frequency data from January 2020 to the first week of July 2022 to model the nonlinear relationship between the stock index, new covid cases and time delay under polynomial regression environment.

Findings

The empirical results show that time delay and new covid cases, when modelled in a polynomial environment with optimal degree and delay, do present better representation of the nonlinear relationship such predictors have with stock index for China. Relative to results from the polynomial regression without delay, the empirical evidence from the model with delay show that an optimal time delay of 17 weeks makes it possible to predict the stock index at high accuracy and record improvements of 16-fold or higher. The representative delay model is used to project for up to 17 weeks for future trends in the stock index.

Implication

The implication of the findings herein is that the prowess of the time delay polynomial regression is heavily dependent on instability in covid-related time trends and that researchers and decision-makers should consider modeling to cover for the unsteadiness in coronavirus cases to achieve better results.

Geopolitical risk, economic policy uncertainty, and dynamic connectedness between clean energy, conventional energy, and food markets

The global financial markets suffered unprecedented shocks, leading to significantly increased uncertainty in the markets due to various economic and financial recessions and geopolitical tensions, resulting in substantial fluctuations in market prices. Therefore, this paper aims to identify the response of the clean energy, conventional energy, and food markets to economic uncertainty and political tension while considering the influence of numerous crises and political conflicts. To achieve this, we employ the DCC-GARCH-based connectedness approach and the quantile-on-quantile model on monthly data spanning from May 2008 to June 2023. The results provide evidence of the sensitivity of dynamic volatility spillovers between financial assets to GEPU and GPR during major economic and financial crises and geopolitical events. Notably, this sensitivity increases significantly during the global financial crisis (GFC), the European debt crisis, Brexit, the US presidential election, the COVID-19 pandemic, and the Russian-Ukrainian war. However, the investigation of the tail dependence structure reveals that the relationship between uncertainties and total volatility connectedness across various market conditions appears to be asymmetric and heterogeneous. Our findings assist policymakers and green investors in designing the most effective policies to mitigate the impact of uncertainties on both conventional and green investments. This is achieved through insightful knowledge about the primary drivers of contagion among these indices, all while not compromising sustainability goals.

Uncovering the asymmetric impacts of economic policy uncertainty on green financial markets in China

Green finance is considered a novel tool of financing to promote the development of the green economy, which has huge investment attractiveness. Previous studies detected the impacts of economic policy uncertainty (EPU) on the green financial markets are mixed. To this end, this study deeply investigates the asymmetric and heterogeneous impacts of EPU on the green bond and green stock markets based on the quantile-on-quantile (QQ) method. Using the data of China Economic Policy Uncertainty Index (CNEPU) and green financial indices, we get some interesting results. (1) EPU has an overall negative effect on the green financial markets, and the green stock market reacts more strongly than the green bond market. (2) For green bond market, the higher quantiles of EPU on a bear market have more significant effect than that on a bull market. (3) For green stock market, a negative effect of EPU on green stock market is observed for the lower quantiles of EPU, while a positive effect is noted at the highest quantiles of EPU. This paper could provide a reference for investors making green investment strategies and for policymakers making policies to promote green development.