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This study reexamines the sustainability of fiscal policy in Sweden.

To test the sustainability of fiscal policy, two approaches are used; the methodology of Kejriwal and Perron (2010), testing for multiple structural changes in a cointegrated regression model and time-varying cointegration test of Bierens and Martins (2010), and Martins (2015).

Using the first approach of testing for multiple structural changes in a cointegrated regression model, the results indicate that government spending and revenue are cointegrated with two breaks. An estimation of a two-break long-run model shows that the slope coefficient increases from 0.678 to 0.892 from the first to the second regime, implying that fiscal deficits were weakly sustainable in the first two regimes, from 1800 to 1943, and from 1944 to 1974. Further, results from time-varying cointegration test indicate that cointegration between spending and revenue in Sweden is time-varying. Fiscal deficits were found to be unsustainable for the periods 1801–1811, 1831–1838, 1853–1860 , 1872–1882, 1897–1902, 1929–1940 and 1976–1982 and weakly sustainable over the rest of the study period.

A number of implications arise from this study: (1) Accounting for breaks in cointegration analysis and in the estimation of the level relationship between spending and revenue is very important because ignoring breaks may lead to an overestimated slope coefficient and hence a bias on the magnitude of fiscal deficit sustainability. (2) In testing for cointegration between spending and revenue, assuming a constant cointegrating slope when it is actually time-varying can also be misleading because deficits can be sustainable for a period of time and unsustainable over another period.

The contribution of this study is three-fold; first, the study uses a long series of annual data spanning over a period of two centuries, from 1800 to 2011. Second, because of the importance of structural change in economics, to examine the existence of a level relationship between spending and revenue, the study uses the methodology of Kejriwal and Perron (2010) to test for multiple structural changes in a cointegrated regression model, as well as time-varying cointegration of Bierens and Martins (2010) and Martins (2015).

The purpose of this paper is to summarize different methods of constructing the financial conditions index (FCI) and analyze current studies on constructing FCI for China. Due to shifts of China’s financial mechanisms in the post-crisis era, conventional ways of FCI construction have their limitations.

The paper suggests improvements in two aspects, i.e. using time-varying weights and introducing non-financial variables. In the empirical study, the author first develops an FCI with fixed weights for comparison, constructs a post-crisis FCI based on time-varying parameter vector autoregressive model and finally examines the FCI with time-varying weights concerning its explanatory and predictive power for inflation.

Results suggest that the FCI with time-varying weights performs better than one with fixed weights and the former better reflects China’s financial conditions. Furthermore, introduction of credit availability improves the FCI.

FCI constructed in this paper goes ahead of inflation by about 11 months, and it has strong explanatory and predictive power for inflation. Constructing an appropriate FCI is important for improving the effectiveness and predictive power of the post-crisis monetary policy and foe achieving both economic and financial stability.

The purpose of this paper is to investigate whether Islamic stock indexes outperform conventional stock indexes, in terms of informational efficiency and risk, during the recent financial instability period.

The paper uses a state space model combined with a standard GARCH(1,1) specification while taking into account structural breakpoints. The authors allow for efficiency and volatility spillovers to be time-varying and consider break dates to locate periods of financial instability.

Empirical results show that Islamic stock indexes are more volatile than their conventional counterparts and are not totally immune to the global financial crisis. As regards of the informational efficiency, the results show that the Islamic stock indexes are more efficient than the conventional stock indexes.

Resulting evidence of this paper has several implications for international investors who wish to invest in Islamic and/or conventional stock markets. Policy makers and even academics and Sharias researchers should as well take preventive measures in order to ensure the stability of Islamic stock markets during turmoil periods. Overall, prudent risk management and precocious financial practices are relevant and crucial for both Islamic and conventional financial markets.

The originality of this study is performed by the use of time-varying models for volatility spillovers and informational efficiency. It considers structural break dates that think about the dynamic effect of informational flows on stock markets. The study was developed in a global framework using international data. The global analysis allows avoiding country specific effects.

This paper investigates the impact of structural breaks on volatility spillovers between Asian stock markets (China, Hong Kong, India, Indonesia, Japan, Korea, Singapore, and Taiwan) and the oil futures market. To this end, we apply the bivariate DCC-GARCH model to weekly spot indices during the period 1998-2015. The results reveal significant volatility transmission for the pairs between the Asian stock and oil futures markets. Moreover, we find a significant variability in the time-varying conditional correlations between the considered markets during both bullish and bearish markets, particularly from early 2007 to the summer of 2008. Using the modified ICSS algorithm, we find several sudden changes in these markets with a common break date centred on September 15, 2008. This date corresponds to the collapse of Lehman Brothers which is considered as our breakpoint to define the global financial crisis. Also, we analyse the optimal portfolio weights and time-varying hedge ratios based on the estimates of the multivariate DCC-GARCH model. The results emphasize the importance of overweighting optimal portfolios between Asian stock and the oil futures markets.

The study aims to investigate the nexus between total factor productivity and tourism growth in Latin American countries for time series data from 1995 to 2017.

Using the extension of the Granger noncausality test in the nonlinear time-varying of Ajmi et al. (2015), the study points out the interconnectedness between the variables during the period.

The study found nonlinear causality between the variables. Particularly, studying the conclusions for the time-varying Granger causality fashion, it can be noticed that the one-way causality from total factor productivity to tourism growth is obtained for Argentina, Bolivia, Brazil, Uruguay and Venezuela, while the vice versa is confirmed for Chile, Ecuador and Nicaragua. Lastly, the study dissected the plots of the curve causality.

In view of the results, some crucial policy implications could be suggested, such as, under certain circumstances and as an exceptional case, the use of policy instruments such as targeted investment, marketing and the support of tourism organizations focused on driving a tourism-led-based productivity and/or tourism programs and projects.

The current work is distinguished from the existing body of understanding in several substantial directions. This work explores, for the first time, the linkages between the total factor productivity index and tourism growth for Latin American countries. No single attempt has been known to investigate this interaction by using nonlinear causality, and this study determines the shape of the curve between the total factor productivity index and tourism growth for each country.

In an era marked by heightened geopolitical uncertainties, such as international conflicts and economic instability, the dynamics of energy markets assume paramount importance. Our study delves into this complex backdrop, focusing on the intricate interplay the between traditional and emerging energy sectors.

This study analyzes the interconnections among green financial assets, renewable energy markets, the geopolitical risk index and cryptocurrency carbon emissions from December 19, 2017 to February 15, 2023. We investigate these relationships using a novel time-frequency connectedness approach and machine learning methodology.

Our findings reveal that green energy stocks, except the PBW, exhibit the highest net transmission of volatility, followed by COAL. In contrast, CARBON emerges as the primary net recipient of volatility, followed by fuel energy assets. The frequency decomposition results also indicate that the long-term components serve as the primary source of directional volatility spillover, suggesting that volatility transmission among green stocks and energy assets tends to occur over a more extended period. The SHapley additive exPlanations (SHAP) results show that the green and fuel energy markets are negatively connected with geopolitical risks (GPRs). The results obtained through the SHAP analysis confirm the novel time-varying parameter vector autoregressive (TVP-VAR) frequency connectedness findings. The CARBON and PBW markets consistently experience spillover shocks from other markets in short and long-term horizons. The role of crude oil as a receiver or transmitter of shocks varies over time.

Green financial assets and clean energy play significant roles in the financial markets and reduce geopolitical risk. Our study employs a time-frequency connectedness approach to assess the interconnections among four markets' families: fuel, renewable energy, green stocks and carbon markets. We utilize the novel TVP-VAR approach, which allows for flexibility and enables us to measure net pairwise connectedness in both short and long-term horizons.

The study considers time-varying risk premium in investigating the capability of technical analysis (TA) to predict and outperform a buy–hold strategy in Bitcoin exchange rate returns.

The study tests the technical trading rule of fixed moving average (FMA) on daily actual and equilibrium returns of Bitcoin exchange rates. The equilibrium returns are computed using dynamic CAPM in conjunction with a VAR-MGARCH (1, 1) system. The empirical evaluation of the study uses a case study of four Bitcoin exchange rates (BTC/AUD, BTC/EUR, BTC/JPY and BTC/ZAR) for the period 19 June 2010 to 30 October 2020.

The findings are consistent with related studies in conventional foreign exchange markets that find TA to be profitable, especially in emerging markets. Nevertheless, the consideration of risk premium has the effect of reducing the abnormal returns. Also, further robust tests reveal that Bitcoin returns possess a momentum effect which prompts further study in efficient market hypothesis research.

The empirical findings of this study should benefit portfolio managers and active investors on the strength of TA to predict returns in a speculative market like the Bitcoin exchange rate market.

The study takes cognisance that cryptocurrency trading is speculative in nature which renders it a good candidate for TA methods. While there are studies that have explored the value of TA in Bitcoin exchange rates, these studies fail to incorporate the effects of time-varying risk premiums, the strength and focus of the current paper.

Owing to increased energy demands, China has become the world’s top CO2 emitter, with electricity generation accounting for the majority of emissions. Therefore, the Chinese Government aspires to achieve a low-carbon transformation of the electric industry by enhancing its green innovation capacity. However, little attention has been paid to the green development of electric technology. Thus, this paper aims to uncover the spatiotemporal evolution of electric technology in the context of China’s low-carbon transformation through patent analysis.

Using granted green invention patent data for China’s electric industry between 2000 and 2021, this paper conducted an exploratory, spatial autocorrelation and time-varying difference-in-differences (DID) analysis to reveal the landscape of electric technology.

Exploratory analysis shows that the average growth rate of electric technology is 8.1%, with spatial heterogeneity, as there is slower growth in the north and west and faster growth in the south and east. In addition, electric technology shows spatial clustering in local areas. Finally, the time-varying DID analysis provides positive evidence that low-carbon policies improve the green innovation capacity of electric technology.

The different effects of the low-carbon pilot policy (LCPC) on R&D subjects and the LCPC’s effectiveness in enhancing the value of patented technology were not revealed.

This paper reveals the spatiotemporal evolutionary characteristics of electric technology in mainland China. The results can help the Chinese Government clarify how to carry out innovative development in the electric industry as part of the low-carbon transformation and provide a theoretical basis and research direction for newcomers in this field.

The purpose of this paper is to investigate the adjustment of government redistributive policies in Scandinavian and Mediterranean countries following changes in income inequality over the period 1980–2021.

The authors first modelled the time-varying dynamics between income inequality and redistribution and then used a non-linear framework to test for the existence of asymmetries and cointegration in their long-run relationship. The authors used two complementary measures of inequality – the share of total income accruing to top percentile income holders and the ratio of the share of total income accruing to top decile income holders divided by that accumulated by the bottom 50% – and computed redistribution as the difference between the two inequality indicators before and after taxes and transfers.

The authors found that the sign of the relationship between income inequality and redistribution is mostly positive and time-varying. Overall, the authors also found evidence that the impact of increases in inequality on redistributive measures is higher than that of decreases. Finally, the authors obtained a significant long-run relationship between both variables in all countries except Denmark and Spain. These results hold for both Scandinavian and Mediterranean countries.

To the best of the authors’ knowledge, this is the first paper to account for the potential existence of non-linearities and to examine the asymmetries in the adjustment of redistributive policies to increases in income inequality using alternative income inequality metrics.

The purpose of this study is to examine the propagation of consumer and business confidence in the euro area with a particular focus on the global financial crisis (GFC), the European sovereign debt crisis (ESDC) and the COVID-19-induced Great Lockdown.

The authors apply Diebold and Yilmaz’s connectedness framework and the improved method based on the time-varying parameter vector autoregressive model.

The authors find that although the evolution of business confidence marked the GFC and the ESDC the role of consumer confidence (mainly in those countries with stricter containment and closure measures) increased in the COVID-19-induced crisis.

The findings are related to the different origins of the examined crisis periods, and the analysis of their interrelationship is a very relevant topic for future research.