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The paper tries to analyse empirically the impact of India's economic policy uncertainty (EPU) index on different macro-economic variables of India, like import, export, interest rate, exchange rate, inflation rate and stock market during pre-COVID-19 and COVID-19 era.

Although there exist several works where relationship and volatility among the stock markets and macro-economic indicators during the COVID-19 pandemic have been estimated, but till now none of the studies examined the effect of EPU index on different macro-economic variables in the Indian context along with the stock market due to the outbreak of COVID-19 pandemic. This is considered a noteworthy gap and hence opens up a new dimension for examination. To get a clear picture, monthly data from January, 2012 to September, 2021 have been considered where January, 2012–February, 2020 is taken as the pre-COVID-19 period and March, 2020–September, 2021 as COVID-19 period. All the data are converted into log natural. The authors applied DCC-GARCH model to investigate the impact of EPU index on volatility of selected variables over the study period across a multivariate framework and Markov regime-switching model to examine the switching over of the variables.

The results of dynamic conditional correlation - multivariate generalized autoregressive conditional heteroskedasticity (DCC-MGARCH) model indicates the presence of volatility in the dependent variables arising out of economic policy uncertainty considering the segmentation of the study period into pre-COVID-19 and COVID-19. The results of Markov regime-switching model show the variables make a significant move from low-volatility regime to high-volatility regime due to the presence of COVID-19.

It can be implied that impact of EPU in terms of volatility on the Indian Stock Market will lead to unfavourable investment conditions for the prospective investors. Even, the different macro-economic variables are to suffer from the volatility arising out of EPU across a long time horizon as confirmed from the DCC-MGARCH model.

The study is original in nature. It adds superior values from the new and significant findings from the study empirically. Application of DCC-MGARCH model and Markov regime switching model makes the study an innovative one in terms of methodology and findings.

Start-ups are successful in receiving valuation in billions of US dollars prior to initial public offering (IPO). However, to sustain higher valuation, the stocks need to perform consistently after the IPO. Short-run stock performance of India-based start-ups during the first year of IPO listing from March 2021 to March 2022 is analysed.

The paper deals with the new generation start-ups' stock performance in emerging market in terms of total and abnormal return generated in comparison to the market (NIFTY-200). Further, the volatility of returns during bear and bull regimes is analysed through a family of Markov-switching GARCH models using both normal and skewed distributions.

The results suggest that start-up stocks are more volatile during bear regime than in the bull run in market-based economies where price limit policy does not apply. Besides, the cumulative abnormal return over the market return was lower for majority of start-up IPO stocks.

Though negative returns of the start-up stocks during the first year of IPO need not be surprising, higher volatility during bear regime is a matter of concern as it could severely impact retail investors and founders. The results hold implication for IPO regulation in emerging markets and for retail investors desirous of investing in start-up stocks.

Volatility of return is examined using a state-space model during the first year of the start-up IPO listing. The study contributes to the emerging market IPO literature by examining IPO performance in market-based economy. Previous IPO performance studies in emerging markets are predominantly based on ecosystems where start-ups are subjected to price limit policy, and it does not reflect the true nature of IPO performance across emerging markets.

We present an approach for pricing American put options with a regime-switching volatility. Our method reveals that the option price can be expressed as the sum of two components: the price of a European put option and the premium associated with the early exercise privilege. Our analysis demonstrates that, under these conditions, the perpetual put option consistently commands a higher price during periods of high volatility compared to those of low volatility. Moreover, we establish that the optimal exercise boundary is lower in high-volatility regimes than in low-volatility regimes. Additionally, we develop an analytical framework to describe American puts with an Erlang-distributed random-time horizon, which allows us to propose a numerical technique for approximating the value of American puts with finite expiry. We also show that a combined approach involving randomization and Richardson extrapolation can be a robust numerical algorithm for estimating American put prices with finite expiry.

The crude oil market plays a key role in addressing the issue of energy economics. This paper aims to detect the causality relationship between the crude oil market and economy based on the financial system.

This paper used the static and dynamic Hatemi-J Bootstrap Toda–Yamamoto and Diebold–Yilmaz connectedness index. The Hatemi-J Bootstrap Toda-Yamamoto approach allows researchers to use nonstationary data and that method is robust to nonnormal distribution and heteroscedasticity. The Diebold–Yilmaz connectedness index model provides researchers to detect the power of connectedness besides linkage direction. The analyzed period is the span from January 3, 2005 to October 3, 2022.

The results show bidirectional causality in the full sample but unidirectional causality before and after the 2008 financial crisis. During the 2008 financial crisis period and the COVID-19 period, there was a bidirectional and unidirectional causality, respectively. The connectedness approach indicates that the crude oil market affects financial stress through investors’ risk preferences.

The Diebold–Yilmaz spillover index model is based on vector autoregression methods with a stationarity precondition. However, some of the five dimensions that constitute the financial stress index (FSI) are nonstationary in level. Therefore, the authors takes the first difference of the nonstationary data.

The linkage between the crude oil market and the FSI provides useful information for investors and policymakers. For instance, this paper indicates that an investor wanted to forecast future value of the crude oil (financial stress) should consider the current and past values of financial stress (crude oil). Moreover, policymaker should consider the crude oil market (FSI) to make a policy proposal for financial system (crude oil market).

Recently, indicators of economic activity levels (economic policy uncertainty, implied volatility index) have begun to be considered to analyze the relationship between energy and the economy but very little is known in the literature about the leading and lagging roles of data in subsample periods and the linkage channel. The other originality of this research is using the new econometric approaches.

This study addresses the empirical results of the spillover effect with export as the primary economic activity that enhances local businesses' total factor productivity (TFP). A learning mechanism is expected to be generated and used as the basis for the policy implication.

This study adopted the Cobb–Douglas function and multiple estimation approaches, including the generalized method of moments, the Olley–Pakes and the Levinsohn–Petrin estimation techniques. The findings were estimated based on the panel data of a Vietnamese local businesses survey conducted by the General Statistics Office of Vietnam (GSO) from 2010 to 2019.

The results showed that the highest TFP belongs to the businesses in the Southeast region, the Mekong Delta region, the mining industry and the foreign-invested enterprises. The lowest impacted TFP are businesses in the Northwest region and agricultural, forestry and fishery sectors. In addition, the estimated results also show that the positive spillover effect on TFP is shown through forward and backward linkage. The negative spillover effect is expressed through the backward and horizontal channels.

This study offers original empirical evidence on the learning mechanisms via which exports contribute to productivity improvement in a developing Asian economy, so making a valuable contribution to the existing academic literature in this domain. The findings of this research make a valuable contribution to the advancement of understanding on the many ways via which spillover effects manifest such as horizontal, forward, backward and supplied-backward linkage.

The study's findings indicate that it is advisable for governments to give priority to the development and improvement of forward and supply chain linkages between exporters and local suppliers. This approach is recommended in order to optimize the advantages derived from export spillovers. At the organizational level, it is imperative for enterprises to strengthen their technological and managerial skills in order to efficiently incorporate knowledge spillovers that originate from overseas partners and trade counterparts.

This study sheds new evidence on the export spillover effect on productivity in emerging economies, with Vietnam as the case study. The paper contributes to the research's originality by adopting novel methodological aspects to estimate local businesses' impact on total factor productivity.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-05-2023-0373

This study aims to examine connections between five variables, including innovation in environment-related technology (EI), trade openness (TRADE), CO₂ emissions (CO₂) and foreign direct investment (FDI) from 1994 to 2019.

This study used an extended joint connectedness technique and the time-varying parameter vector autoregression (TVP-VAR) method. The analysis focuses on the variables of innovation in environment-related technology (EI), trade openness (TRADE), CO₂ emissions (CO₂) and foreign direct investment (FDI) using data from 1994 to 2019.

The results demonstrate that innovation in environment-related technology and an openness to the global network captured by FDI are identified as crucial net transmitters of shocks. In addition, an openness to the global trade network captured by TRADE turns from a transmitter to a receiver of shocks and vice versa. Moreover, it can be seen that the impact of EI was significant in the first five years of the observed period, and it transmitted the largest shock in 1997.

With regard to policy implications, the findings offer valuable insights for investors and policymakers. As the tradeoff between business efficiency and environmental sustainability diminishes, it is essential for Vietnam’s economy and enterprises to embrace green and sustainable growth in line with global trends. In a world characterized by uncertainties and risks, enterprises need to develop strategies to manage risks and shocks arising from geopolitical tensions, input material supply, financial–monetary instability and natural disasters.

This study contributes to the existing literature in two significant ways. First, as previously emphasized, this paper represents the first attempt to investigate the relationship between economic globalization and environmental innovation. Second, this study proposes a novel methodology that is better suited for analyzing volatility interlinkages across different market types.

The present study empirically examines the impact of coronavirus disease 2019 (COVID-19) and policy uncertainty on stock prices in India during the COVID-19 pandemic.

To this end, the authors use the daily data by applying the autoregressive distributed lag (ARDL) model, which tests the short- and long-run relationship between stock price and its covariates.

The study finds that increased uncertainty has adverse short- and long-run effects on stock prices, while the vaccine index has favorable effects on stock market recovery.

From investors' perspectives, volatility in the Indian stock market has negative repercussions. Therefore, to protect investors' sentiments, policymakers should be concerned about the uncertainty induced by the COVID-19 pandemic and similar other uncertainty prevailing in the financial markets.

This study used the news-based COVID-19 index and vaccine index to measure recent pandemic-induced uncertainty. The result carries some policy implications for an emerging economy like India.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-03-2023-0244

Owing to highly volatile and chaotic external events, predicting future movements of cryptocurrencies is a challenging task. This paper advances a granular hybrid predictive modeling framework for predicting the future figures of Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Stellar (XLM) and Tether (USDT) during normal and pandemic regimes.

Initially, the major temporal characteristics of the price series are examined. In the second stage, ensemble empirical mode decomposition (EEMD) and maximal overlap discrete wavelet transformation (MODWT) are used to decompose the original time series into two distinct sets of granular subseries. In the third stage, long- and short-term memory network (LSTM) and extreme gradient boosting (XGB) are applied to the decomposed subseries to estimate the initial forecasts. Lastly, sequential quadratic programming (SQP) is used to fetch the forecast by combining the initial forecasts.

Rigorous performance assessment and the outcome of the Diebold-Mariano’s pairwise statistical test demonstrate the efficacy of the suggested predictive framework. The framework yields commendable predictive performance during the COVID-19 pandemic timeline explicitly as well. Future trends of BTC and ETH are found to be relatively easier to predict, while USDT is relatively difficult to predict.

The robustness of the proposed framework can be leveraged for practical trading and managing investment in crypto market. Empirical properties of the temporal dynamics of chosen cryptocurrencies provide deeper insights.

This study examines the risk spillovers between Indonesian sectorial stocks (Energy, Basic Materials, Industrials, Consumer Cyclicals, Consumer Non-cyclical and Financials), the aggregate index (IDX) and two commodities (gold and West Texas Intermediate Crude Oil [WTI] futures).

The study uses two methodologies: the TVP-VAR model of Antonakakis and Gabauer (2017) and the quantile connectedness approach of Ando et al. (2022). The data cover the period from October 04, 2010, to April 5, 2022.

The results show that the IDX, industrials and materials are net transmitters, while the financials, consumer noncyclical and energy sectors are the dominant shock receivers. Using the quantile connectedness approach, the role of each sector is heterogeneous and asymmetric, and the return spillover is stronger at lower and higher quantiles. Furthermore, the portfolio hedging results show that oil offers more diversification gains than gold, and hedging oil is more effective during the pandemic.

This study provides valuable insights for investors to diversify their portfolios and for policymakers to develop policies, regulations and risk management tools to promote stability in the Indonesian stock market. The results can inform the design of market regulations and the development of risk management tools to ensure the stability and resilience of the market.

This study is the first to examine the spillovers between commodities and Indonesian sectors, recognizing the presence of heterogeneity in the relationship under different market conditions. It provides important portfolio diversification insights for equity investors interested in the Indonesian stock market and policymakers.

This paper examines the extreme dependence and asymmetric risk spillovers between crude oil futures and ten US stock sector indices (consumer discretionary, consumer staples, energy, financials, health care, industrials, information technology, materials, telecommunication and utilities) before and during COVID-19 outbreak. This study is based on the rationale that stock sectors exhibit heterogeneity in their response to oil prices depending on whether they are classified as oil-intensive or non-oil-intensive sectors and the possible time variation in the dependence and risk spillover effects.

The authors employ static and dynamic symmetric and asymmetric copula models as well as Conditional Value at Risk (VaR) (CoVaR). Finally, they use robustness tests to validate their results.

Before the COVID-19 pandemic, crude oil returns showed an asymmetric tail dependence with all stock sector returns, except health care and industrials (materials), where an average (symmetric tail) dependence is identified. During the COVID-19 pandemic, crude oil returns exhibit a lower tail dependency with the returns of all stock sectors, except financials and consumer discretionary. Furthermore, there is evidence of downside and upside risk asymmetric spillovers from crude oil to stock sectors and vice versa. Finally, the risk spillovers from stock sectors to crude oil are higher than those from crude oil to stock sectors, and they significantly increase during the pandemic.

There is heterogeneity in the linkages and the asymmetric bidirectional systemic risk between crude oil and US economic sectors during bearish and bullish market conditions; this study is the first to investigate the average and extreme tail dependence and asymmetric spillovers between crude oil and US stock sectors.