House prices and COVID-19 pandemic shocks in India: a nonlinear ARDL analysis

PurposeIndia is one of those countries that are severely affected by the COVID-19 pandemic. With the upsurge in the cases, the country recorded high unemployment rates, economic uncertainties and slugging growth rates. This adversely affected the real estate sector in India. As the relation of the housing market with the gross domestic product is quite lasting thus, the decline in housing prices has severely impacted the economic growth of the nation. Hence, the purpose of this paper is to gauge the asymmetric impact of COVID-19 shocks on housing prices in India.Design/methodology/approachStudies revealed the symmetric impact of macroeconomic variables, and contingencies on housing prices dominate the literature. However, the assumption of linearity fails to apprehend the asymmetric dynamics of the housing sector. Thus, the author uses a nonlinear autoregressive distributed lag model to address this limitation and test the existence of short- and long-run asymmetry.FindingsThe findings revealed the long- and short-run asymmetric impact of the COVID-19 outbreak and the peak of the COVID-19 on housing prices. The results indicate that the peak of COVID-19 had a greater impact on housing prices in comparison to the outbreak of COVID-19. This can be explained as prices will revert to normal at a speed of 0.978% with the decline in the number of COVID-19 cases. Whereas the housing prices rise at a rate of 0.714 as a result of government intervention to deal with the ill effects of the COVID-19 outbreak. Moreover, it can be inferred that both the outbreak and peak of COVID-19 will lead to a minimal decline in housing prices, while with the decline in the number of cases and reduction in the impact of the outbreak of COVID, the housing prices will rise at an increasing rate.Originality/valueTo the best of the authors' knowledge, this is the first study to understand the impact of the outbreak and peak of COVID-19 on the housing prices separately.

CCTV News' Asymmetric Impact on the Chinese Stock Market during COVID-19: A Combination Analysis Based on the SVAR and NARDL Models

This study uses the structural vector autoregression (SVAR) and nonlinear autoregressive distributed lag (NARDL) models to examine the long- and short-term asymmetric effects of structural state media shocks on the Chinese stock market. The findings, obtained using Xinwen Lianbo as a stand-in for state media, indicate that attention shocks on Xinwen Lianbo have an asymmetrical impact on the aggregate stock market returns in both the short and long run. The sectoral and overall stock market results are similar, with CCTV having a stronger impact in the first half of the pandemic. Employing other COVID-19 news measurements, we validated our primary findings and discovered that the price function differs among various state media's attention to COVID-19.

Did weekly economic index and volatility index impact US food sales during the first year of the pandemic?

We explore the impacts of economic and financial dislocations caused by COVID-19 pandemic shocks on food sales in the United States from January 2020 to January 2021. We use the US weekly economic index (WEI) to measure economic dislocations and the Chicago Board Options Exchange volatility index (VIX) to capture the broader stock market dislocations. We validate the NARDL model by testing a battery of models using the autoregressive distributed lags (ARDL) methodology (ARDL, NARDL, and QARDL specifications). Our study postulates that an increase in WEI has a significant negative long-term effect on food sales, whereas a decrease in WEI has no statistically significant (long-run) effect. Thus, policy responses that ignore asymmetric effects and hidden cointegration may fail to promote food security during pandemics.

The asymmetric Impact of COVID-19 Pandemic on the Crude Oil-stock Markets Nexus in KSA: Evidence from a NARDL Model

This paper estimates the asymmetric relationship between the crude oil market, stock market and COVID-19 pandemic in the case of KSA during the period of March 15, 2020–February 03, 2021. Nonlinear and long-run asymmetric cointegration were utilized for comprehensive research on this topic. Our findings are as follows: positive and negative shocks to the COVID-19 pandemic reduce stock market. Moreover, positive shock to crude oil market increases stock market, but negative shock has a negative and insignificant effect. Based on the results, this study concludes with suitable policy prescription. © 2022, Econjournals. All rights reserved.

Can gold and bitcoin hedge against the COVID-19 related news sentiment risk? New evidence from a NARDL approach.

The COVID-19 pandemic has led to extensive news coverage, causing investor sentiment to swing, which has further increased financial market price volatility. There is an increasing need to find a hedge against sentiment risk. This paper examines the hedge capabilities of gold and Bitcoin against COVID-19-related news sentiment (CNS) risk under a nonlinear autoregressive distributed lag (NARDL) model. Our empirical results reveal that there is an obvious asymmetric effect from the CNS on gold prices in the short run and that the decrease in the COVID-19-related news index would have a greater impact on gold prices than when it increases. The impact of CNS on Bitcoin prices is asymmetric in the long and short term, especially in the long term. In addition, we conclude that gold is a hedge against CNS risk in the long term, and the hedging effect of Bitcoin is mainly reflected in the short-term.

The hedge and safe haven properties of non-fungible tokens (NFTs): Evidence from the nonlinear autoregressive distributed lag (NARDL) model

In this paper, we employ the NARDL model to examine whether non-fungible tokens (NFTs) can act as hedges and safe havens for stocks, bonds, US dollar, gold, crude oil and Bitcoin. In addition to examining whether NFTs can act as hedges for other assets during the full period (January 1, 2018–March 31, 2022), we also study the hedging properties of NFTs during the pre-COVID-19 period and the safe haven properties of NFTs in times of stress after the COVID-19 outbreak. The empirical results show that in the full period, NFTs are hedges for bonds, US dollar and gold on average;in the pre-COVID-19 period, NFTs are hedges for stocks and US dollar on average;in the COVID-19 period, NFTs can act as safe havens for US dollar. Our empirical findings have important implications for investors looking for hedging and safe haven instruments for major asset classes.

House prices and COVID-19 pandemic shocks in India: a nonlinear ARDL analysis

Purpose: India is one of those countries that are severely affected by the COVID-19 pandemic. With the upsurge in the cases, the country recorded high unemployment rates, economic uncertainties and slugging growth rates. This adversely affected the real estate sector in India. As the relation of the housing market with the gross domestic product is quite lasting thus, the decline in housing prices has severely impacted the economic growth of the nation. Hence, the purpose of this paper is to gauge the asymmetric impact of COVID-19 shocks on housing prices in India. Design/methodology/approach: Studies revealed the symmetric impact of macroeconomic variables, and contingencies on housing prices dominate the literature. However, the assumption of linearity fails to apprehend the asymmetric dynamics of the housing sector. Thus, the author uses a nonlinear autoregressive distributed lag model to address this limitation and test the existence of short- and long-run asymmetry. Findings: The findings revealed the long- and short-run asymmetric impact of the COVID-19 outbreak and the peak of the COVID-19 on housing prices. The results indicate that the peak of COVID-19 had a greater impact on housing prices in comparison to the outbreak of COVID-19. This can be explained as prices will revert to normal at a speed of 0.978% with the decline in the number of COVID-19 cases. Whereas the housing prices rise at a rate of 0.714 as a result of government intervention to deal with the ill effects of the COVID-19 outbreak. Moreover, it can be inferred that both the outbreak and peak of COVID-19 will lead to a minimal decline in housing prices, while with the decline in the number of cases and reduction in the impact of the outbreak of COVID, the housing prices will rise at an increasing rate. Originality/value: To the best of the authors’ knowledge, this is the first study to understand the impact of the outbreak and peak of COVID-19 on the housing prices separately. © 2020, Emerald Publishing Limited.

Asymmetrical relationship between COVID-19 global fear index and agricultural commodity prices

Grains and oilseed crops are widely used as key input elements for global food safety in food and livestock sub-sectors, as well as in other sectors such as energy, services and industry. In addition, they play an active role in the international agricultural markets. Therefore, price structure in the grain and oilseed market is important for agriculture and many other sectors. This study was designed to reveal how the fear of COVID-19 has globally affected grain prices. The study covered the one-year period from March 11, 2020, when COVID-19 was first recognized as a pandemic, to March 11, 2021. The Global Fear Index (GFI) and the price sub-indices created by the Grain Oil Council were used to determine the impact of the fear caused by COVID-19 on grain prices. Assuming an asymmetrical relationship between variables, the Nonlinear Autoregressive Distributed Lag model was used to determine this relationship. According to the model results, it was found that in the long term, agricultural commodity prices gave an increase (decrease) response to the positive (negative) effects in the GFI, and that the effect of an increase in the GFI on agricultural commodity prices was greater than the effect of a decrease. Accordingly, it is thought that the analysis and predictions which take into account the asymmetrical effect would give more realistic results and thus contribute considerably to the market regulations. It will also help policymakers make more rational decisions in their search for solutions to the problems in the cereal market. © 2022

Evidence from the nonlinear autoregressive distributed lag model on the asymmetric influence of the first wave of the COVID-19 pandemic on energy markets

The COVID-19 pandemic remained a global risk factor and integrated into various means in the functioning of companies, economies and financial markets. Therefore, this paper investigates how COVID-19 influences the energy market in the main financial markets (China, France, Germany, Italy, Spain and the United States), using time series from February 28, 2020, to November 3, 2020. The goal of this research is to investigate the asymmetric impact of COVID-19 from leading financial markets on energy commodities. In this regard, the non-linear auto-regressive distributed lag (NARDL) framework is employed to capture the long-run asymmetric reactions. The econometric design allows to explore the long-term asymmetric reactions of dependent variables through positive and negative partial sum decompositions of changes in the explanatory variables. The quantitative results show a significant long-run asymmetric interdependence between the number of new SARS-CoV-2 incidence and mortality and the daily percent change in close price of future contracts pertaining to Brent oil, crude oil WTI, carbon emissions, gasoline RBOB, heating oil, Chukyo kerosene, and natural gas. Furthermore, no asymmetry is found in the case of ethanol and fuel oil futures. The novelty of this article is the study of the impact of COVID-19 on the energy sector during the first two waves of COVID-19 by applying the NARDL model that allows to capture long-term asymmetric reactions. Certainly, further research on this topic is necessary due to the permanent shifts in the pandemic, as well as the availability of longer data periods on COVID-19.

House price index (HPI) and Covid-19 pandemic shocks: evidence from Turkey and Kazakhstan

PurposeThis paper aims to investigate asymmetric pricing behaviour and impact of coronavirus (Covid-19) pandemic shocks on house price index (HPI) of Turkey and Kazakhstan.Design/methodology/approachMonthly HPIs and consumer price index (CPI) data ranges from 2010M1 to 2020M5 are used. This study uses a nonlinear autoregressive distributed lag model for empirical analysis.FindingsThe findings of this study reveal that the Covid-19 pandemic exerted both long-run and short-run asymmetric relationship on HPI of Turkey while in Kazakhstan, the long-run impact of Covid-19 pandemic shock is symmetrical long-run positive effect is similar in both HPI markets.Research limitations/implicationsThe main limitations of this study are the study scope and data set due to data constraint. Several other macroeconomic variables may affect housing prices;however, variables used in this study satisfy the focus of this study in the presence of data constraint. HPI and CPI variables were made available on monthly basis for a considerably longer period which guaranteed the ranges of data set used in this study.Practical implicationsDespite the limitation, this study provides necessary information for authorities and prospective investors in HPI to make a sound investment decision.Originality/valueThis is the first study that rigorously and simultaneously examines the pricing behaviour of Turkey and Kazakhstan HPIs in relation to the Covid-19 pandemic shocks at the regional level. HPI of Kazakhstan is recognized in the global real estate transparency index but the study is rare. The study contributes to regional studies on housing price by bridging this gap in the real estate literature.