Amine LAHIANI

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Purpose Departing from the expansionary austerity literature, this study aims at examining how fiscal consolidation affects the economic growth in Tunisia using annual data over the period 1970–2018. Design/methodology/approach To revisit the fiscal consolidation-economic growth nexus, the ambiguous empirical findings in previous literature make useful the adoption of alternative econometric techniques. The authors use an extended nonlinear autoregressive distributed lag (ARDL) cointegration approach developed by Shin et al . (2014) and the Diks and Panchenko's (2006) nonlinear Granger causality test. Furthermore, a traditional approach based on changes in cyclically-adjusted primary balance was applied to define the fiscal consolidation episodes in Tunisia. Findings The empirical evidence reveal that fiscal adjustment in Tunisia may hurt the economy, both in the short- and long-run, through its contractionary effect on economic growth. Another important finding concerns the unidirectional nonlinear Granger causality running from fiscal consolidation to economic growth. Practical implications Fiscal adjustment in Tunisia is found to play a prominent role in reducing public debt; but at the same time, it may be costly and not beneficial to the economy. This view corroborates with the fact that fiscal consolidation is more likely to end successfully only under specific conditions. This calls for a deeper reflection upon new insights regarding the design of fiscal adjustment in Tunisia. To reach this end, it is suggested to combine the defensive consolidation strategy with offensive components such as investment, infrastructure, education and health. Originality/value The existing economic analysis on fiscal policy-growth nexus in Tunisia has often identified fiscal consolidation through the use of the actual fiscal balance. With the goal of more accurate estimation, this study bridges the gap by using the cyclically-adjusted primary balance (CAPB) as a much suitable indicator to investigate the non-Keynesian effect of fiscal consolidation in Tunisia. This indicator eliminates the influence of cyclical fluctuations and many other fixed expenditures such as the interest paid on the public debt.

Green energy is a crucial component in addressing expanding energy demands and combating climate change, but the possible negative repercussions of these technologies are frequently disregarded. Green energy’s deployment is tied to environmentally sustainable development goals (SDGs). It can only be achieved by scaling up the finance of investment that provides environmental benefits through new financial instruments and new policies, such as green banks, green bonds, community-based green funds, green central banking, etc. In an effort to address the issues with IPAT and ImPACT, this study employed the STIRPAT model approach, which is a proven framework for energy economics analysis. The author gathers yearly data spanning 2002–2018 for six ASEAN member countries with the aim of investigating the relationship between CO2 emissions, green finance, energy efficiency, and the green energy index (GEX). After preliminary tests, the study employed the Westerlund test and Johansen Fisher test for long-term equilibrium and estimated the Granger causal links between variables using the generalized method of moments (GMM). The results indicate that green bonds are an effective technique for promoting green energy projects and considerably reducing CO2 emissions. Therefore, governments should establish supporting policies with a long-term perspective to increase the investment of green energy projects related investment from private participants to ensure sustainable growth and address environmental challenges. This strategy may be appropriate during and after the COVID-19 period.

Purpose The paper analyzes downside and upside risk spillovers between stock markets of G7 countries and China before and during the COVID-19 pandemic. Design/methodology/approach By using VAR-ADCC models and conditional value at risk (CoVaR) techniques, downside and upside risk spillovers between stock markets of G7 countries and China are analyzed before and during the COVID-19 pandemic. Findings The results suggested existence of a significant and asymmetrical two-way risk transmission between majority of pair markets, but the degree of asymmetry differs according to the use of the entire cumulative distributions or distribution tails. Downside and upside risk spillovers are significantly larger before the COVID-19 pandemic in all cases except between CAC 40/DAX and S&P/SSE pairs. Originality/value The paper used CoVaR and delta-CoVaR to investigate the downside and upside spillovers between stock markets of G7 countries and China before and during the COVID-19 pandemic.

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This paper estimates the energy demand function to examine the asymmetric relationship between the shadow economy and energy consumption in the case of Bolivia during the period of 1960–2015. The ambiguous empirical findings on shadow economy-energy demand nexus has inclined us to apply the nonlinear ARDL cointegration approach developed by Shin et al. (2014) and the Hatemi-J (2012) asymmetric causality test. The empirical evidence confirms the presence of an asymmetric relationship between the variables of interest. Positive and negative shocks to official GDP (true GDP) and the shadow economy have positive impacts on energy consumption. Energy consumption is positively and negatively affected by positive and negative shocks in financial development, respectively. A positive (negative) shock to capital decreases energy consumption. Another important finding concerns the complex causal direction between economic growth and energy consumption. This study provides new insights regarding to the use of official GDP (true GDP) and the shadow economy as economic tools to maintain energy demand for sustainable economic development.

This article aims at studying the relationship between oil prices and stock indexes in four major European countries, i.e. United Kingdom (UK), Germany, France, and Italy using monthly data over the 1999–2016 period. We employ the Quantile Autoregressive Distributed Lags model of Cho et al. (2015) that accounts for distributional asymmetry in the relationship between stock prices and energy prices in the long and short run. Findings show that the distinction between short-run and long-run, between quantiles, and between countries are of particular importance. For the UK, only the long-run relationship between oil and stock prices is significant at medium and high quantiles. For Italy, this is true only at high quantiles. However, for France and Germany, the relationship is significant only in the short run, at low and medium quantiles for France and only at low quantiles for Germany. The results of the quantile Granger causality test of Troster (2018) confirm the importance of distinguishing between quantiles and between countries while investigating the causal relationship between oil prices and stock indexes. These results contribute to understand inconclusive results in previous studies. They also provide important information for investors, portfolio managers, and policymakers.

This paper investigates the relationship between S&P 500 prices, viewed as a US economic barometer, and a set of energy prices, including WTI, gasoline, heating, diesel and natural gas prices, using the Quantile Autoregressive Distributed Lags (QARDL) model recently developed by Cho et al. (2015). The empirical results show a negative long-and short-run relationship between WTI crude oil and Henry Hub natural gas prices on the one side and S&P 500 stock prices on the other side, only for medium and high quantiles. The findings of Wald tests indicate a nonlinear and asymmetric pass-through from energy price shocks to aggregate US stock market prices. These results show that crude oil and natural gas are key economic variables to explain short run and long run stock market dynamics. They provide further insights into how energy price shocks are transmitted to stock market prices.

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Using a sample of quarterly data, we investigate the effect of electricity consumption, capital formation and financial development on economic growth in Portugal. A positive (negative) shock of electricity consumption is estimated to have increased (decreased) economic growth. Economic growth is positively affected by positive shock stems in capital. A positive (negative) shock in financial development declines (increases) economic growth. These findings reveal that (a) Portugal is still an energy-dependent economy; (b) energy is one of the major inputs for economic growth and development; (c) a conservation energy policy should not be implemented because energy is an important driver of growth; (d) economic growth enhances capital formation and not the opposite. Hence, it appears more relevant to boost economic growth before enhancing capital formation; (e) financial development does not appear to be an important catalyst for economic growth. Findings also highlight (f) the relevance of the recent energy policy implemented in Portugal and (g) the need to limit energy imports by means of producing electricity through renewable energy to reduce the external debt level in Portugal, especially after the 2008 crisis.

Financial distress prediction is a central issue in empirical finance that has drawn a lot of research interests in the literature. This paper aims to predict the financial distress of French small and medium firms using Logit model, Artificial Neural Networks, Support Vector Machine techniques, Partial Least Squares, and a hybrid model integrating Support Vector Machine with Partial Least Squares. Empirical results indicate that for one year prior to financial distress, Support Vector Machine is the best classifier with an overall accuracy of 88.57%. Meanwhile, in the case of two years prior to financial distress, the hybrid model outperforms Support Vector Machine, Logit model, Partial Least Squares, andArtificial Neural Networks with an overall accuracy of 94.28%. Distressed firms are found to be smaller, more leveraged and with lower repayment capacity. Moreover, they have lower liquidity, profitability, and solvency ratios. Besides the academic research contribution, our findings can be useful for managers, investors, and creditors. With respect to managers, our findings provide them with early warnings signals of performance deterioration in order to take corrective actions and reduce the financial distress risk. For investors, understanding the main factors leading to financial distress allows them to avoid investing in risky firms. Creditors should correctly evaluate the firm financial situation and be vigilant to signs of impending financial distress to avoid capital loss and costs related to counterpart risk.

This paper employs the Quantile Autoregressive Distributed Lags (QARDL) model developed recently by Cho et al. (2015) to investigate the pass-through of oil prices to a set of energy prices. This approach allows analyzing simultaneously short-term connections and long-run cointegrating relationships across a range of quantiles. It also provides insights on the short-run predictive power of oil prices in predicting energy prices while accounting for the cointegration between oil prices and each of the considered energy prices in low, medium and high quantiles. Two key findings emerge from this paper. First, all considered energy prices are shown to be cointegrated with oil price across quantiles meaning that a stationaryequilibriumrelationship exists between single energy price and oil price. Second, we find evidence that oil price is a significant predictor of individual petroleum products prices and natural gas in the short run. This paper has important policy implications for forecasters, energy policy-makers and portfolio managers.

This article analyses the impact of the global crisis on the relationship between firm-related factors (size, tangible and intangible assets, growth, and profitability) and the capital structure of French micro-enterprises. A panel of 4945 firms are studied comparatively over two periods: before (2003-2007) and during (2008-2013) the global crisis. During the global crisis, microenterprises survive by relying mostly on internal sources of financing. External leverage is reduced, as the increased information asymmetry and default risk raise the cost of debt. When necessary, micro-enterprises sell the underused or unnecessary tangible assets, as they focus on their main competence and develop their intangible assets: human skills, advertising, networking, brand name, and awareness. In addition, we show that the pecking order is the most relevant theory for predicting the financial decisions and situation of French MEs. These results provide interesting insights into the financial strategy of French micro-enterprises, facilitating understanding and action at academic and policy levels. Keywords Micro-enterprises. Capital structure. Firmrelated factors. Global crisis JEL classifications G32. L26 1 Following Benkraiem (2016), we refer to these crises as the global crisis hereafter. Furthermore, for simplicity reasons, Bcrisis^and Bglobal crisis^are used interchangeably.

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This paper aims to study the role of gold as a hedge against inflation based on local monthly gold prices in China, India, Japan, France, the United Kingdom and the United States of America in periods ranging from 1955 to 2015. We extend the literature by using a novel approach with the nonlinear autoregressive distributed lags (NARDL) model (Shin et al., 2014). The main advantage of this model relies on its ability to simultaneously capture the short- and long-run asymmetries through positive and negative partial sum decompositions of changes in the independent variable(s). Moreover, we rely on local gold prices instead of those from London converted into local currencies like in most of previous studies. The results show that gold is not a hedge against inflation in the long run in all cases. In the short run, gold is an inflation hedge only in the UK, USA, and India. Furthermore, there is no long-run equilibrium between gold prices and the CPI in China, India and France. This difference may be due to traditional aspects of gold and custom controls for gold trade in these countries. Our robustness check suggests that the data time-frequency does not change the specification of the NARDL model but can change conclusions regarding the role of gold as a hedge against inflation in certain countries.

The paper applies an event study methodologyaims to investigate the macroeconomic announcements effects on Standard&Poor’s500 and oil prices. Our results provide evidence for a significant impact of the US macroeconomic news on oil prices. This impact is split into two components, namely the direct effect (common response) and indirect effect (volatility transmission). Altogether our results show that the volatility transmission is bidirectional. Not only a significant volatility transmission from the oil market to the US stock market is revealed, but also a high volatility transmission is recorded from the oil market to the stock market especially after the release of consumption indicators.

In this article,we use the recently developed nonlinear autoregressive distributedlags (NARDL) model to examine the pass-through of crude oil prices into gasoline and natural gas prices. Our approach allowsus to simultaneously test the short-and long-run nonlinearities through positive and negative partial sum decompositions of the predetermined explanatory variables. It also offers the possibility to quantify the respective responses of gasoline and natural gas prices to positive and negative oil price shocks from the asymmetric dynamic multipliers. The obtained results indicate that oil prices affect gasoline prices and natural gas prices in an asymmetric and nonlinear manner, but the price transmission mechanism is not the same. Important policy implications can be learned from the empirical findings.

Considering nonlinearities in the exchange rate pass-through to domesticprices, this paper estimates exchange rate pass-through in Mexico. We examine responses of domestic prices to a positive one unit exchange rate shock by estimating a threshold vector autoregression (TVAR) model. A monthly rate of inflation of 0.79% acts as a threshold. The exchange rate pass-through to domestic prices is statistically significant above the threshold level of the inflation rate and statistically insignificant below it.

This paper extends previous studies by investigating the relevance of structural breaks and long memory in modeling and forecasting the conditional volatility of oil spot and futures prices using a variety of GARCH-type models. Our results can be summarized as follows. First, we provide evidence of parameter instability in five out of nine GARCH-based conditional volatility processes for energy prices. Second, long memory is effectively present in all the series considered and a FIGARCH model seems to better fit the data, but the degree of volatility persistence diminishes significantly after adjusting for structural breaks. Finally, the out-of-sample analysis shows that volatility models accommodating instability and long memory characteristics of the data provide the best volatility forecasts for most cases.

In this paper we make use of several multivariate GARCH models (CCC-, DCC-, BEKK-, diagonal BEKK-, and VAR-GARCH) to investigate both return and volatility spillovers between world gold prices and stock market in China over the period from March 22, 2004 through March 31, 2011. We also analyze the optimal weights and hedge ratios for gold-stock portfolio holdings and show how empirical results can be used to build effective diversification and hedging strategy. Our results show evidence of significant return and volatility cross effects between gold prices and stock prices in China. In particular, past gold returns play a crucial role in explaining the dynamics of conditional return and volatility of Chinese stock market and should thus be accounted for when forecasting future stock returns. Our portfolio analysis suggests that adding gold to a portfolio of Chinese stocks improves its risk-adjusted return and that gold risk exposures can be effectively hedged in portfolios of stocks over time. Finally, we show that the VAR-GARCH model performs better than the other multivariate GARCH models.

This article contributes to the related literature by empirically investigating the efficiency of nine energy and precious metal markets over the last decades, employing several pronounced models. We test for both the short- and the long-run efficiency using, in addition to linear cointegration models, nonlinear cointegration and error-correction models (ECM) which allow the efficiency intensity to change per regime. Our findings can be summarized as follows: i) futures prices are found to be cointegrated with spot prices, but they do not constitute unbiased predictors of future spot prices; ii) the hypothesis of risk neutrality is rejected and there is some evidence of time-varying risk premia; iii) the short-run efficiency hypothesis is rejected, suggesting that using past futures price returns improves the modeling and forecasting of future spot prices; and iv) the nonlinear modeling suggests the presence of two distinct regimes where in the first regime the efficiency hypothesis is supported, whereas in the second it is rejected. The empirical findings have important implications for producers, hedgers, speculators and policymakers.

We investigate the potential of structural changes and long memory (LM) properties in returns and volatility of the four major precious metal commodities traded on the COMEX markets (gold, silver, platinum and palladium). Broadly speaking, a random variable is said to exhibit long memory behavior if its autocorrelation function is not integrable, while structural changes can induce sudden and significant shifts in the time-series behavior of that variable. The results from implementing several parametric and semiparametric methods indicate strong evidence of long range dependence in the daily conditional return and volatility processes for the precious metals. Moreover, for most of the precious metals considered, this dual long memory is found to be adequately captured by an ARFIMA-FIGARCH model, which also provides better out-of-sample forecast accuracy than several popular volatility models. Finally, evidence shows that conditional volatility of precious metals is better explained by long memory than by structural breaks.

This paper investigates whether structural breaks and long memory are relevant features in modeling and forecasting the conditional volatility of oil spot and futures prices using three GARCH-type models, i.e., linear GARCH, GARCH with structural breaks and FIGARCH. By relying on a modified version of Inclan and Tiao (1994)'s iterated cumulative sum of squares (ICSS) algorithm, our results can be summarized as follows. First, we provide evidence of parameter instability in five out of twelve GARCH-based conditional volatility processes for energy prices. Second, long memory is effectively present in all the series considered and a FIGARCH model seems to better fit the data, but the degree of volatility persistence diminishes significantly after adjusting for structural breaks. Finally, the out-of-sample analysis shows that forecasting models accommodating for structural break characteristics of the data often outperform the commonly used short-memory linear volatility models. It is however worth noting that the long memory evidence found in the in-sample period is not strongly supported by the out-of-sample forecasting exercise.

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