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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.

The article aims to establish whether the degree of aversion to inflation and the responsiveness to deviations from potential output have changed over time.

This paper assesses time variation in monetary policy rules by applying a time-varying parameter generalised methods of moments (TVP-GMM) framework.

Using monthly data until December 2022 for five inflation targeting countries (the UK, Canada, Australia, New Zealand, Sweden) and five countries with alternative monetary regimes (the US, Japan, Denmark, the Euro Area, Switzerland), we find that monetary policy has become more averse to inflation and more responsive to the output gap in both sets of countries over time. In particular, there has been a clear shift in inflation targeting countries towards a more hawkish stance on inflation since the adoption of this regime and a greater response to both inflation and the output gap in most countries after the global financial crisis, which indicates a stronger reliance on monetary rules to stabilise the economy in recent years. It also appears that inflation targeting countries pay greater attention to the exchange rate pass-through channel when setting interest rates. Finally, monetary surprises do not seem to be an important determinant of the evolution over time of the Taylor rule parameters, which suggests a high degree of monetary policy transparency in the countries under examination.

It provides new evidence on changes over time in monetary policy rules.

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 aims to evaluate linear mixed data sampling (MIDAS), nonlinear artificial neural networks (ANNs) and a hybrid approach for exploiting high-frequency information to improve low-frequency gross domestic product (GDP) forecasting. Their capabilities are assessed through direct forecasting comparisons.

This study compares quarterly GDP forecasts from unrestricted MIDAS (UMIDAS), standalone ANN and ANN-enhanced MIDAS models using five monthly predictors. Rigorous empirical analysis of recent US data is supplemented by Monte Carlo simulations to validate findings.

The empirical results and simulations demonstrate that the hybrid ANN-MIDAS performs best for short-term predictions, whereas UMIDAS is more robust for long-term forecasts. The integration of ANNs into MIDAS provides modeling flexibility and accuracy gains for near-term forecasts.

The model comparisons are limited to five selected monthly indicators. Expanding the variables and alternative data processing techniques may reveal further insights. Longer analysis horizons could identify structural breaks in relationships.

The findings guide researchers and policymakers in leveraging mixed frequencies amidst data complexity. Appropriate modeling choices based on context and forecast horizon can maximize accuracy.

Enhanced GDP forecasting supports improved policy and business decisions, benefiting economic performance and societal welfare. More accurate predictions build stakeholder confidence and trust in statistics underlying critical choices.

This direct forecasting comparison offers unique large-scale simulation evidence on harnessing mixed frequencies with leading statistical and machine learning techniques. The results elucidate their complementarity for short-term versus long-term modeling.

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

This paper aims to investigate the immediate effect of the outbreak of the COVID-19 pandemic by investigating volatility transmission and dynamic correlation between stock (conventional and Islamic) markets, bitcoin and major commodities such as gold, oil and silver at different investment horizons before and after 161 trading days of the outbreak of the COVID-19 pandemic.

The MGARCH-DCC and maximum overlap discrete wavelet transform -based cross-correlation were used in the estimation of the volatility spillover and continuous wavelet transform in the estimation of the time-varying volatility and correlation between the assets at different investment horizons.

The authors observed a sudden correlation breakdown following the COVID-19 shock. Oil (Bitcoin) was a major volatility transmitter before (during) COVID-19. Digital gold (Bitcoin), gold and silver became highly correlated during COVID-19. The highest co-movement between the assets was observed at medium and long-term investment horizons.

The study findings have a financial implication for day traders, investors and policymakers in the understanding of volatility transmission and intercorrelation in a bid to actively manage stylized and well-diversified asset portfolios.

This study is unique for its employment in estimating the time-varying conditional volatility of the investable assets and cross-correlations between them at different investment horizons, particularly before and after COVID-19 outbreak.

This paper aims to focus on exploring and understanding the practice of analyzing the determinants of the Moroccan Dirham by foreign exchange professionals in trading rooms in the context of transitioning to a more flexible regime initiated by Moroccan authorities. The objective of this study is to highlight how foreign exchange operators analyze the determinants of the Moroccan Dirham in the context of exchange rate liberalization, focusing primarily on qualitative data rather than quantitative data.

Therefore, this paper opted for a methodological approach using interview surveys to understand the underlying behavior of Moroccan foreign exchange operators, conducting a content analysis. This paper targeted six foreign exchange operators from nine Moroccan banks authorized as market makers by Bank Al-Maghrib.

The results indicate that the fluctuations of the Moroccan Dirham are closely linked to two main factors: the analysis of the EUR/USD exchange rate and market liquidity analysis. Furthermore, content analysis revealed five essential aspects regarding the practice of analyzing the determinants of the Dirham: “Dirham determinants,” “complementarity between technical analysis and fundamental analysis,” “trends and reversals,” “utility of macroeconomic models” and “psychological factors.”

Certainly, this methodology allows for exploring and understanding the underlying behavior of currency operators but inherently generates a certain degree of subjectivity that can affect the research validity. Indeed, the subjectivity can arise from the responses of the currency operators themselves. They may present the phenomenon coherently or selectively choose the elements they remember to respond to. On the other hand, the validity of this type of research relies on the researcher's ability to cultivate empathy throughout the knowledge creation process. The empathetic stance adopted in this study proved to be complex due to the uniqueness of operators and interaction, sometimes making it challenging to combine empathy, respect and critical thinking (Olivier De Sardan, 2004). Furthermore, the researcher is often faced with an interpretation bias, which can manifest not only during the coding of collected data but also during the analysis of the constructed content. To mitigate this interpretation bias, this paper subjected the collected data to a double coding procedure.

This study aims to narrow the gap in opinions between academics and practitioners by providing a practical overview for change novices.

This study is the pioneering inquiry exploring the process of determining the Moroccan dirham within the transition to a flexible exchange rate regime, using an exploratory methodological approach.

This study aims to investigate the impact of financial and behavioural factors on investment decisions in the cryptocurrency market within the Gulf Cooperation Council (GCC).

The study uses the cross-sectional absolute deviation methodology developed by Chang et al. (2000) to determine the existence of herding behaviour during extreme conditions in the cryptocurrency market of four GCC countries: Bahrain, Saudi Arabia, Kuwait and UAE. In addition, a questionnaire survey was distributed to 322 investors from the GCC cryptocurrency markets to gather data on their investment decisions.

The study finds that the herding theory, prospect theory and heuristics theory account for 16.5% of the variance in investors' choices in the GCC cryptocurrency market. The regression analysis results show no multicollinearity problems, and a high F-statistic indicates the general model's acceptability in the results.

The study's findings suggest that behavioural and financial factors play a significant role in investors' choices in the GCC cryptocurrency market. The study's results can be used by investors to better understand the impact of these factors on their investment decisions and to develop more effective investment strategies. In addition, the study's findings can be used by policymakers to develop regulations that consider the impact of behavioural and financial factors on the GCC cryptocurrency market.

This study adds to the body of literature in two different ways. Initially, motivated by earlier research examining the impact of behaviour finance factors on investment decisions, the authors look at how the behaviour finance factors affect investment decisions of the GCC cryptocurrency market. To extend most of these studies, this study uses a regime-switching model that accounts for two different market states. Second, by considering the recent crisis and more recent periods involving more cryptocurrencies, the authors have contributed to several studies examining the impact of behavioural financial factors on investment decisions in cryptocurrency markets. In fact, very few studies have examined the impact of behavioural finance on cryptocurrency markets. Therefore, to the best of the authors’ knowledge, this study is the first of its kind to investigate how behavioural finance factors influence investment decisions in the GCC cryptocurrency market. This allows to better illuminate the factors driving herd behaviour in the GCC cryptocurrency market.

This survey explores the application of real options theory to the field of health economics. The integration of options theory offers a valuable framework to address these challenges, providing insights into healthcare investments, policy analysis and patient care pathways.

This research employs the real options theory, a financial concept, to delve into health economics challenges. Through a systematic approach, three distinct models rooted in this theory are crafted and analyzed. Firstly, the study examines the value of investing in emerging health technology, factoring in future advantages, associated costs and unpredictability. The second model is patient-centric, evaluating the choice between immediate treatment switch and waiting for more clarity, while also weighing the associated risks. Lastly, the research assesses pandemic-related government policies, emphasizing the importance of delaying decisions in the face of uncertainties, thereby promoting data-driven policymaking.

Three different real options models are presented in this study to illustrate their applicability and value in aiding decision-makers. (1) The first evaluates investments in new technology, analyzing future benefits, discount rates and benefit volatility to determine investment value. (2) In the second model, a patient has the option of switching treatments now or waiting for more information before optimally switching treatments. However, waiting has its risks, such as disease progression. By modeling the potential benefits and risks of both options, and factoring in the time value, this model aids doctors and patients in making informed decisions based on a quantified assessment of potential outcomes. (3) The third model concerns pandemic policy: governments can end or prolong lockdowns. While awaiting more data on the virus might lead to economic and societal strain, the model emphasizes the economic value of deferring decisions under uncertainty.

This research provides a quantified perspective on various decisions in healthcare, from investments in new technology to treatment choices for patients to government decisions regarding pandemics. By applying real options theory, stakeholders can make more evidence-driven decisions.

Decisions about patient care pathways and pandemic policies have direct societal implications. For instance, choices regarding the prolongation or ending of lockdowns can lead to economic and societal strain.

The originality of this study lies in its application of real options theory, a concept from finance, to the realm of health economics, offering novel insights and analytical tools for decision-makers in the healthcare sector.

This study aims to investigate the causal relationship between external debt and inflation in Jordan over the period 1970 to 2020.

The external debt–inflation nexus is examined within a multivariate framework by including other determinants of inflation, including money supply and the nominal effective exchange rate. This study uses an ARDL bounds testing approach to cointegration to test the existence of a long-run relationship between the inflation rate and its drivers. An error correction model is estimated to reveal the short-run dynamics of the series. The direction of causality among the variables is examined using a modified version of the Granger non-causality test due to Toda and Yamamoto (1995). The analyses control for the presence of structural breaks in the underlying time series.

The empirical results show that external debt and money supply have a statistically significant positive effect on inflation in the long run. The authors also find that a nominal depreciation of the Jordanian Dinar raises inflation rates in the long run. The Toda–Yamamoto Granger non-causality test findings reveal a statistically significant bi-directional positive causality between inflation and external debt, between the nominal effective exchange rate and inflation and between money supply and inflation.

Proper management of the exchange rate policy, money supply and external debt levels is crucial to control inflation rates in Jordan.

To date, the authors are unaware of any empirical study that examines the impact of external debt on inflation in Jordan, and the current study aims to fill this gap in the literature.