Search results

This study examines the impact of the Russo-Ukrainian War on the commodity markets of oil, wheat, and natural gas, in which these two countries play/have a dominant role. The author examines if during these extreme events an anti-leverage effect emerges and indicates the abnormal conditions of the markets.

The author uses daily returns from the future prices of brent oil, wheat, and natural gas for the period 31.3.2020–31.3.2022 and applies the exponential and the threshold asymmetry GARCH models.

The empirical findings confirm the existence of anti-leverage effect in the wheat and natural gas commodity markets and point to the existence of the leverage effect in the Brent market.

The existence of the anti-leverage effect means that stock prices and volatility present a positive relationship which is in contrast to the normal/conventional stylized facts which suggest the opposite. These findings could be useful for scholars and practitioners because they enable them to examine market abnormalities using the anti-leverage effect as a criterion and explore its relationship with extreme conditions and strategies that lead to skyrocketing increases in prices.

This study investigates the anti-leverage effect in commodity markets under extreme conditions, which has received little attention in the literature. Moreover, this paper links the existence of the anti-leverage effect with abnormal growth in asset prices, which shows a new direction for the behavior of assets when extreme conditions emerge.

This study aims to assess the contemporaneous dependence between euro, crude oil and gold returns and their respective implied volatility changes.

The empirical analysis relies on daily data for the period 2015–2022 and the local Gaussian correlation (LGC) approach that is suitable for estimating dependence between two stochastic processes at any point of their joint distribution.

(a) The global correlation coefficients are negative for the euro and crude oil and positive for gold, implying that in the first two markets’ traders are more concerned with sudden price downswings while in the third with sudden upswings. (b) The detailed local analysis, however, shows that traders 2019 attitudes may change with the underlying state of the market and that risk reversals are more likely to occur at the upper extremes of the joint distributions. (c) The pattern of dependence between price returns and implied volatility changes is asymmetric.

To the best of the author’s knowledge, this is the first work that uses the highly flexible LGC approach to analyze the link between price returns and implied volatility changes either in stock or in commodities futures markets. The empirical results provide useful insights into traders’ risk attitudes in different market states.

This study aims to investigate the relationship between oil prices and stock returns of renewable energy firms in India under different market conditions.

The authors use the panel quantile framework with Fama–French–Carhart’s (1997) four-factor asset pricing model. All renewable energy firms listed in the National Stock Exchange of India are considered in this study. Three oil prices, such as West Texas Intermediate spot price, Europe Brent oil price and Indian basket oil price, are used in the regression. The analysis is done for the whole sample and its subgroups.

In the whole sample, stock returns of renewable energy firms respond positively to oil price changes in extreme market conditions only. In the subgroups of the renewable energy firms, the relationship between stock returns and oil price is positive and more robust in higher quantiles across all subgroup firms.

The contribution of the study is explained as follows. First, this study helps to explore the relationship between oil and stock returns of the renewable energy sector under different market conditions in the Indian context. Second, existing studies explore the effect of oil prices on stock returns of the renewable energy sector at the industry level, and most of the studies are in developed countries. To the best of the authors’ knowledge, this is the first study in the context of India. Third, this is a firm-level study

This paper aims to examine the applicable legal framework, various concerns regarding the price support methodology and the merits and pitfalls of the Bali peace clause that currently shields India’s public stockholding (PSH) policy, to provide suggestions on moving forward in the World Trade Organization (WTO) negotiations.

At the WTO, PSH for food security is considered a trade-distorting price support measure when food stocks are purchased from farmers at administered prices instead of market prices. The price support is measured by a specific methodology stipulated under the Agreement on Agriculture. India is concerned that the price support methodology overestimates the actual support provided to the farmers and makes India susceptible to violating the WTO law.

The paper explains the domestic implications for India arising due to the applicable WTO law. Given the centrality of India’s PSH programmes in ensuring food security, India must seek more clarity on the interpretation of Article 18.4 of the AoA and the ambiguous provisions of the Bali peace clause.

The paper provides a timely and updated analysis of the contentious issue of PSH for food security, exclusively from the Indian perspective, highlighting the persisting challenges for India in WTO agriculture negotiations. This is timely as many WTO members have raised serious concerns about India’s market price support policies recently and this analysis informs the reader about the underlying issues.

Although the effects of both news sentiment and expectations on price in financial markets have now been extensively demonstrated, the jointness that these predictors can have in their effects on price has not been well-defined. Investigating causal ordering in their effects on price can further our understanding of both direct and indirect effects in their relationship to market price.

We use autoregressive distributed lag (ARDL) methodology to examine the relationship between agent expectations and news sentiment in predicting price in a financial market. The ARDL estimation is supplemented by Grainger causality testing.

In the ARDL models we implement, measures of expectations and news sentiment and their lags were confirmed to be significantly related to market price in separate estimates. Our results further indicate that in models of relationships between these predictors, news sentiment is a significant predictor of agent expectations, but agent expectations are not significant predictors of news sentiment. Granger-causality estimates confirmed the causal inferences from ARDL results.

Taken together, the results extend our understanding of the dynamics of expectations and sentiment as exogenous information sources that relate to price in financial markets. They suggest that the extensively cited predictor of news sentiment can have both a direct effect on market price and an indirect effect on price through agent expectations.

Even traditional financial management firms now commonly track behavioral measures of expectations and market sentiment. More complete understanding of the relationship between these predictors of market price can further their representation in predictive models.

This article extends the frequently reported bivariate relationship of expectations and sentiment to market price to examine jointness in the relationship between these variables in predicting price. Inference from ARDL estimates is supported by Grainger-causality estimates.

Recent studies on the securities market's differential pricing of earnings components indicate that cash flows from operations are valued more highly than extreme total accruals. However, no previous study has examined whether cash flows from operations have a higher valuation than moderate total accruals. Therefore, this study examines the securities market's differential pricing of cash flows from operations and both moderate and extreme total accruals.

The study's sample is divided into two sub-samples: a moderate total accruals sub-sample; and an extreme total accruals sub-sample. To evaluate whether cash flows have a higher valuation when compared to total accruals, for the entire sample and for each of the two sub-samples, the study examines the statistical significance of the difference between slope coefficients of cash flows and total accruals for regression of returns on both unexpected cash flows from operations and unexpected total accruals.

Consistent with prior research, results from the entire sample show a differential higher valuation of cash flows when compared to total accruals. Another finding, consistent with recent studies, is that cash flows from operations have a higher valuation when compared to extreme total accruals. However, there is no higher differential valuation of cash flows over moderate total accruals. These findings support the decomposition of earnings into the components of cash flows from operations and total accruals only when total accruals are extreme (rather than moderate).

A possible explanation for these results is that since accruals predict cash flows, total accruals – when moderate (i.e. not extreme) – are priced similarly to cash flows. These results reveal that when total accruals are moderate, earnings are a better proxy for the underlying cash flows (over the entire future horizon, not just the current period) than is cash flows. However, since total accruals are unlikely to persist in a permanent way over the years, these results indicate that the decomposition of earnings into the components of cash flows from operations and total accruals is consistent with the information set used to value equity securities. Therefore, separate disclosure of cash flows is value relevant. In addition, users of financial statements certainly need the cash flows information as an ex-post validation of the prior earnings.

This study's contribution stems from its determination of the preferred level of disaggregation of earnings components (i.e. operating cash flows and total accruals). This is expected to help investors in their attempt to enhance the outcome of their informed investment and credit decisions.

This study aims to uncover the main predictors of financial distress in the Gulf Cooperation Council (GCC) countries using a wide range of global factors and asset classes.

This study uses novel approaches that take into account extreme events as well as the nonlinear behavior of time series over various time intervals (i.e. short, medium and long term) and during boom and bust episodes. This study primarily uses the conditional value at risk (CoVaR), the quantile multivariate causality test and the partial wavelet coherence method. The data collection period ranges from March 2014 to September 2022.

US T-bills and gold are the primary factors that can increase financial stability in the GCC region, according to VaRs and CoVaRs. More proof of the predictive value of the oil, gold and wheat markets, as well as geopolitical tensions, uncertainty over US policy and volatility in the oil and US equities markets, is provided by the multivariate causality test. When low extreme quantiles or cross extreme quantiles are taken into account, these results are substantial and sturdy. Lastly, after adjusting for the effect of crude oil prices, this study’s wavelet coherence results indicate diminished long-run connections between the GCC stock market and the chosen global determinants.

Despite the implications of the author’s research for decision makers, there are some limitations mainly related to the selection of Morgan Stanley Capital International (MSCI) GCC ex-Saudi Arabia. Considering the economic importance of the Kingdom of Saudi Arabia (KSA) in the region, the author believes that it would be better to include this country in the data to obtain more robust results. In addition, there is evidence in the literature of the existence of heterogeneous responses to global shocks; some markets are more vulnerable than others. This is another limitation of this study, as this study considers the GCC as a bloc rather than each country individually. These limitations could open up further research opportunities.

These findings are important for investors seeking to manage their portfolios under extreme market conditions. They are also important for government policies aimed at mitigating the impact of external shocks.

This paper aims to examine the cross-quantile correlation and causality-in-quantiles between green investments and energy commodities during the outbreak of COVID-19. To be specific, the authors aim to address the following questions: Is there any distributional predictability among green bonds and energy commodities during COVID-19? Is there exist any directional predictability between green investments and energy commodities during the global pandemic? Can green bonds hedge the risk of energy commodities during a period of the financial crisis.

The authors use the nonparametric causality in quantile and cross-quantilogram (CQ) correlation approaches as the estimation techniques to investigate the distributional and directional predictability between green investments and energy commodities respectively using daily spot prices from January 1, 2020, to March 26, 2021. The study uses daily closing price indices S&P Green Bond Index as a representative of the green bond market. In the case of energy commodities, the authors use S&P GSCI Natural Gas Spot, S&P GSCI Biofuel Spot, S&P GSCI Unleaded Gasoline Spot, S&P GSCI Gas Oil Spot, S&P GSCI Brent Crude Spot, S&P GSCI WTI, OPEC Oil Basket Price, Crude Oil Oman, Crude Oil Dubai Cash, S&P GSCI Heating Oil Spot, S&P Global Clean Energy, US Gulf Coast Kerosene and Los Angeles Low Sulfur CARB Diesel Spot.

From the CQ correlation results, there exists an overall negative directional predictability between green bonds and natural gas. The authors find that the directional predictability between green bonds and S&P GSCI Biofuel Spot, S&P GSCI Gas Oil Spot, S&P GSCI Brent Crude Spot, S&P GSCI WTI Spot, OPEC Oil Basket Spot, Crude Oil Oman Spot, Crude Oil Dubai Cash Spot, S&P GSCI Heating Oil Spot, US Gulf Coast Kerosene-Type Jet Fuel Spot Price and Los Angeles Low Sulfur CARB Diesel Spot Price is negative during normal market conditions and positive during extreme market conditions. Results from the non-parametric causality in the quantile approach show strong evidence of asymmetry in causality across quantiles and strong variations across markets.

The quantile time-varying dependence and predictability results documented in this paper can help market participants with different investment targets and horizons adopt better hedging strategies and portfolio diversification to aid optimal policy measures during volatile market conditions.

The outcome of this study will promote awareness regarding the environment and also increase investor’s participation in the green bond market. Further, it allows corporate institutions to fulfill their social commitment through the issuance of green bonds.

This paper differs from these previous studies in several aspects. First, the authors have included a wide range of energy commodities, comprising three green bond indices and 14 energy commodity indices. Second, the authors have explored the dependency between the two markets, particularly during COVID-19 pandemic. Third, the authors have applied CQ and causality-in-quantile methods on the given data set. Since the market of green and sustainable finance is growing drastically and the world is transmitting toward environment-friendly practices, it is essential and vital to understand the impact of green bonds on other financial markets. In this regard, the study contributes to the literature by documenting an in-depth connectedness between green bonds and crude oil, natural gas, petrol, kerosene, diesel, crude, heating oil, biofuels and other energy commodities.

Using a GED-GARCH model to estimate monthly data from January 1990 to February 2022, we test whether gold acts as a hedge or safe haven asset in 10 countries. With a downturn of the stock market, gold can be viewed as a hedge and safe haven asset in the G7 countries. In the case of inflation, gold acts as a hedge and safe haven asset in the United States, United Kingdom, Canada, China, and Indonesia. For currency depreciation, oil price shock, economic policy uncertainty, and US volatility spillover, evidence finds that gold acts as a hedge and safe haven for all countries.

This paper aims to empirically investigate time–frequency linkages between Euramerican mature and Asian emerging crude oil futures markets in terms of correlation and risk spillovers.

With daily data, the authors first undertake the MODWT method to decompose yield series into four different timescales, and then use the R-Vine Copula-CoVaR to analyze correlation and risk spillovers between Euramerican mature and Asian emerging crude oil futures markets.

The empirical results are as follows: (a) short-term trading is the primary driver of price volatility in crude oil futures markets. (b) The crude oil futures markets exhibit certain regional aggregation characteristics, with the Indian crude oil futures market playing an important role in connecting Euramerican mature and Asian emerging crude oil futures markets. What’s more, Oman crude oil serves as a bridge to link Asian emerging crude oil futures markets. (c) There are significant tail correlations among different futures markets, making them susceptible to “same fall but different rise” scenarios. The volatility behavior of the Indian and Euramerican markets is highly correlated in extreme incidents. (d) Those markets exhibit asymmetric bidirectional risk spillovers. Specifically, the Euramerican mature crude oil futures markets demonstrate significant risk spillovers in the extreme short term, with a relatively larger spillover effect observed on the Indian crude oil futures market. Compared with India and Japan in Asian emerging crude oil futures markets, China's crude oil futures market places more emphasis on changes in market fundamentals and prefers to hold long-term positions rather than short-term technical factors.

The MODWT model is utilized to capture the multiscale coordinated motion characteristics of the data in the time–frequency perspective. What’s more, compared to traditional methods, the R-Vine Copula model exhibits greater flexibility and higher measurement accuracy, enabling it to more accurately capture correlation structures among multiple markets. The proposed methodology can provide evidence for whether crude oil futures markets exhibit integration characteristics and can deepen our understanding of connections among crude oil futures prices.