Perceived climate risk and stock prices: An empirical analysis of pricing effects.

Increasing awareness of climate change and its potential consequences on financial markets has led to interest in the impact of climate risk on stock returns and portfolio composition, but few studies have focused on perceived climate risk pricing. This study is the first to introduce perceived climate risk as an additional factor in asset pricing models. The perceived climate risk is measured based on the climate change sentiment of the Twitter dataset with 16 million unique tweets in the years 2010-2019.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.