Financing sources, green investment, and environmental performance: Cross-country evidence.

This article investigates the influence of financing sources and financial constraints on green investment, based on a study conducted with a sample of Eastern European SMEs from 2018 to 2020. We constructed a green investment proxy using principal component analysis, revealing two principal pillars: pure green investment and mixed green investment. Employing two-stage least squares regression analysis (2SLS) and instrumental probit (IV Probit), our results demonstrate that internal finance positively impacts green investment. Conversely, we find that leverage and financial constraints negatively correlate with green investment and environmental performance. The findings of this study provide compelling evidence that SMEs operating in the Eastern European region face significant financial constraints, impeding their ability to adopt responsible investments aimed at reducing their considerable environmental footprints. These results hold valuable implications for both managers and policymakers, emphasizing the importance of facilitating increased access to debt and devising green financial incentives to promote environmentally responsible investments among Eastern European SMEs, particularly during periods of conflicts.

What can we learn about the market reaction to macroeconomic surprise? Evidence from the COVID-19 crisis.

We investigate the impact of macroeconomic surprise and uncertainty on G7 financial markets around COVID-19 pandemic using two real-time, real-activity indexes recently constructed by Scotti (2016). We applies the wavelet analysis to detect the response of the stock markets to the macroeconomic surprise and an uncertainty indexes and then we use NARDL model to examine the asymmetric effect of the news surprise and uncertainty on the equity markets. We conduct our empirical analysis with the daily data from January, 2014 to September, 2020. Our findings indicate that G7 stock markets are sensitive to the macroeconomic surprise and uncertainty and the effect is more pronounced at the long term than the short term. Moreover, we show that the COVID-19 crisis supports the relationship between the macroeconomic indexes and the stock prices. The results are useful for investment decision-making for the investors on the G7 stock indices at different investment horizons.

Forecasting commodity prices: empirical evidence using deep learning tools.

Since the last two decades, financial markets have exhibited several transformations owing to recurring crises episodes that has led to the development of alternative assets. Particularly, the commodity market has attracted attention from investors and hedgers. However, the operational research stream has also developed substantially based on the growth of the artificial intelligence field, which includes machine learning and deep learning. The choice of algorithms in both machine learning and deep learning is case-sensitive. Hence, AI practitioners should first attempt solutions related to machine learning algorithms, and if such solutions are unsatisfactory, they must apply deep learning algorithms. Using this perspective, this study aims to investigate the potential of various deep learning basic algorithms for forecasting selected commodity prices. Formally, we use the Bloomberg Commodity Index (noted by the Global Aggregate Index) and its five component indices: Bloomberg Agriculture Subindex, Bloomberg Precious Metals Subindex, Bloomberg Livestock Subindex, Bloomberg Industrial Metals Subindex, and Bloomberg Energy Subindex. Based on daily data from January 2002 (the beginning wave of commodity markets' financialization) to December 2020, results show the effectiveness of the Long Short-Term Memory method as a forecasting tool and the superiority of the Bloomberg Livestock Subindex and Bloomberg Industrial Metals Subindex for assessing other commodities' indices. These findings is important in term for investors in term of risk management as well as policymakers in adjusting public policy, especially during Russian-Ukrainian war.

Spatial contagion between financial markets: new evidence of asymmetric measures.

The objective of this paper is to identify the presence, direction and time at which the pure contagion effect occurred between financial markets. In so doing, the aim is to prove the existence of both spatial and temporal asymmetries of pure contagion effects. Firstly, a new empirical framework is proposed in order to define a spatial contagion index using the conditional cumulative distribution function as a parameter to estimate a conditional copula. This methodology enables us to estimate a dynamic conditional copula, providing information about how the market sent pure contagion effects and when. Secondly, in addition to detecting the direction of contagion, the real-time contagion effect is determined, enabling us to calculate the delay of contagion effects (spillover) between financial markets. The present empirical results show the existence of both spatial and temporal asymmetry for bilateral contagion effects for 16 mature and emerging stock markets during the 2001-2018 period. This proves the importance of taking temporal asymmetry into account when we want to detect the contagion effect of every crisis and to estimate the period of pure contagion relating to investors' behaviors. Finally, these findings highlight the fact that contagion effects were more intensive during the subprime crisis than they were during the European debt crisis.

Revisiting the relationship between spot and futures markets: evidence from commodity markets and NARDL framework.

This study aims to investigate the relationship between the spot and futures commodity markets. Considering the complexity of the relationship, we use a nonlinear autoregressive distributed lag (NARDL) framework that considers the asymmetry and nonlinearity in both the long and short run. Based on the daily returns of six commodity indices reaggregated on three commodity types, our study reaches some interesting findings. Our analysis highlights a bidirectional relationship between both markets over the short and long run, with a greater lead for the futures market. This result confirms the future market's dominant contribution to price discovery in commodities. Changes in commodity prices appear first in the futures market, as informed investors and speculators prefer trading on this market that is characterized by low costs and a high-leverage effect. Then, the information is transmitted from the futures to the spot market through arbitrageurs' activity, which explains the nonlinearity of the relationship. These results are helpful to scholars, investors and policymakers.

Is gold a hedge or safe haven against oil and currency market movements? A revisit using multifractal approach.

We investigate gold's role as a hedge or safe haven against oil price and currency movements across calm and extreme market conditions. For the empirical analysis, we extend the intraday multifractal correlation measure developed by Madani et al. (Bankers, Markets & Investors, 163:2-13, 2020) to consider the dependence for calm and extreme movement periods across different time scales. Interestingly, we employ the rolling window method to examine the time-varying dependence between gold-oil and gold-currency in terms of calm and turmoil market conditions. Based on high frequency (5-min intervals) across the period 2017-2019, our analysis shows three interesting findings. First, gold acts as a weak (strong) hedge for oil (currency) market movements, across all agent types. Second, gold has strong safe-haven capability against extreme currency movements, and against only short time scales of oil price movements. Third, hedging strategies confirm the scale-dependent gold's role in reducing portfolio risk as a hedge or safe haven. Implications for investors, financial institutions, and policymakers are discussed.

Does it pay to go green? The environmental innovation effect on corporate financial performance.

The current global economy demands synergies between ecological responsiveness and business models. To analyse this dynamic, this study investigates the relationship between green innovation and corporate financial performance for German HDAX companies from 2008 to 2019 by constructing an green innovation measure. A two-step GMM system and penalised-spline estimation are used to test the linear relationship between green innovation and financial proxies (return on assets, return on invested capital, and the market-to-book ratio). The results indicate a linear positive effect of green innovation on different financial performance measures. This suggests that green innovation drives resource efficiency and enhances corporate reputation, which, in turn, boosts financial performance.

Cryptocurrency volatility forecasting: What can we learn from the first wave of the COVID-19 outbreak?

This study aims to examine the issue of cryptocurrency volatility modelling and forecasting based on high-frequency data. More specifically, this study assesses whether crisis periods, particularly the coronavirus disease pandemic, influence the dynamic of cryptocurrency volatility. We investigate the four main cryptocurrency markets (Bitcoin, Ethereum Classic, Ethereum, and Ripple) from April 2018 to June 2020. The realized volatility measure is computed and decomposed to various components (continuous versus discontinuous, positive and negative semi-variances, and signed jumps). A variety of heterogeneous autoregressive (HAR) models are developed including these components, thereby enabling assessment of different assumptions (including persistence and asymmetric dynamic) of modelling and volatility forecasting based on in-sample and out-of-sample forecasting strategies, respectively. Our results reveal three main findings. First, the extended HAR model that includes the positive and negative jumps appears to be the best model for predicting future volatility for both crisis and non-crisis periods. Second, during the crisis period, only the negative jump component is statistically significant. Third, in terms of volatility forecasting, the results show that the extended HAR model that includes positive and negative semi-variances outperform the other models.

Measuring the global economic impact of the coronavirus outbreak: Evidence from the main cluster countries.

This study measures the global economic impact of the coronavirus outbreak. This pandemic is characterized by demand and supply shocks, leading to restrictions on trade, product and service transactions, and capital flow mobility. We investigate its impact on currency markets, stock market performance, and investor fear sentiment. We employ an empirical, time-scale approach based on the continuous wavelet transform-appropriate for time-series characteristics during times of turmoil. Based on daily data for four main cluster countries (China, France, Italy, and the USA), our results show that the impact of the pandemic's evolution on the main economic indicators in China exhibits a different pattern from France, Italy, and the USA. For China, our results show that the pandemic evolution co-moves with the main economic indicators only in the short term (one week). The effect is more persistent in other countries. We also show that the main economic indicators are more sensitive to pandemic evolution assessed by the number of deaths rather than number of cases, and that currency and financial markets are affected in different timescales. These findings might assist policymakers in addressing the feedback loop between currency markets and capital flows and help investors find alternative assets to hedge against heath shocks.

Does non-fundamental news related to COVID-19 matter for stock returns? Evidence from Shanghai stock market.

The COVID-19 outbreak generates various types of news that affect economic and financial systems. No studies have assessed the effects of such news on financial markets. This study sheds light on the impact of non-fundamental news related to the COVID-19 pandemic on the liquidity and returns volatility. Because we examined extreme events, we performed quantile regression on daily data from December 31, 2019 to the end of lockdown restrictions in China on April 7, 2020. Results showed that the non-fundamental news, as the number of deaths and cases related to the COVID-19, raised the stock market returns volatility and reduced the level of stock market liquidity, increasing overall risk, whereas fundamental macroeconomic news remained largely immaterial for the stock market. These findings are explained by a knock-on effect because the health system's inability to manage and treat a high number of COVID-19 patients in intensive care led the country to implement a lockdown and the global economy to largely shut down.