The main aim of this paper is to investigate the volatility determinants of crude oil and foreign exchange markets and jump spillover between them. We consider currencies of two major oil-importing countries (India and China) over the sample period of January 1.2013 to October 31, 2019. We find evidence of positive return spillover from the oil to the foreign exchange market; however, there is a lack of return spillover in the other direction. Oil jumps appear to have a negative impact on exchange rate conditional volatility, and the latter responds asymmetrically to disentangled (positive and negative) oil price jumps. We also report disentangled exchange rate jumps significant impact on conditional oil price volatility. These results, however, are asymmetric based on the nature of jumps and alternative oil price series. Finally, we do not find evidence of co-jump between the oil and foreign exchange markets. These results have important implications for investors and policymakers. (C) 2020 Elsevier B.V. All rights reserved.
This paper studies the connectedness among energy equity indices of oil-exporting and oil-importing countries around the world. For each country, we construct time-varying measures of how much shocks this country transmits to other countries and how much shocks this country receives from other countries. We analyze the network of countries and find that, on average, oil-exporting countries are mainly transmitting shocks, and oil-importing countries are mainly receiving shocks. Furthermore, we use panel data regressions to evaluate whether the connectedness among countries is influenced by economic sentiment, uncertainty, and the global COVID-19 pandemic. We find that the connectedness among countries increases significantly in periods of uncertainty, low economic sentiment, and COVID-19 problems. This implies that diversification benefits across countries are severely reduced exactly during crises, that is, during the times when diversification benefits are most important.
Previous studies indicate a substantial time-variation in the co-movement of commodity futures markets and economic fundamentals. This paper examines the connectedness and directional spillovers for both the agricultural commodity futures markets and the corresponding sentiment indices. We first construct dynamic time-varying connectedness measures both for the agricultural commodity returns and sentiments. Then, we use panel data regressions and time-varying Granger causality tests to evaluate whether the spillovers between these returns and sentiments are influenced by the economic and financial uncertainties, including the global COVID-19 pandemic. In particular, we document that the COVID-19 induced uncertainty influences agricultural commodity returns and sentiments significantly around the first cycle of the pandemic in 2020. Last but not least, economic policy and financial market uncertainty are also found to be significant determinants of the connectedness between agricultural commodity returns and sentiment spillovers.
This paper provides a thorough analysis on multiscale dependence schemes between equity markets, commodity futures and uncertainty indexes. Based on decomposed return series, we provide an exhaustive survey on time varying dependence, before and after the outbreak of financial crisis. Although daily returns of equity markets and commodity futures are described by weak dependence, our results indicate a stronger dependence between the long-run trends of both asset classes.
We estimate the income and fuel price elasticities of private car vehicle kilometres travelled (VKT) using fixed effects on registry micro panel data covering all Swedish households from 1999 to 2018. Such registry data, covering all individuals and cars in the country, are unique to Nordic countries and are comprehensive enough to allow fine segmentation of the population by both income groups and several municipality types. To address potential endogeneity arising if employees receive a wage compensation for long commutes, we apply the temporal changes in earned income tax credits as an instrumental variable. We find lower income and price elasticities (in absolute value) in the large cities, and larger elasticities in suburbs, other cities and in rural areas. We also find that the elasticities decrease with income, excluding the lowest income quartile, having the lowest elasticities. Specifically, we show theoretically and empirically that because the income elasticity varies considerably along the income distribution, the resulting income elasticity depends heavily on how the estimator assigns weight to different income groups, unless the specification explicitly allows for variation in the impact of income on VKT. Moreover, the impact of an income increase depends on to whom the income increase accrues to. For a uniform income increase, 0.2 is the preferred income elasticity. Our preferred long-run fuel price elasticity is -0.53. The short-run elasticities are lower. These elasticities apply to the full population and not only to car owners or drivers.
The relationship between energy consumption and output is still ambiguous in the existing literature. The economy of Bangladesh, having spectacular output growth and rising energy demand as well as energy efficiency in recent decades, can be an ideal case for examining energy-output dynamics. We find that while fluctuations in energy consumption do not affect output fluctuations, movements in output inversely affect movements in energy use. The results of Granger causality tests in this respect are consistent with those of innovative accounting that includes variance decompositions and impulse responses. Autoregressive distributed lag models also suggest a role of output in Bangladesh's energy use. Hence, the findings of this study have policy implications for other developing nations where measures for energy conservation and efficiency can be relevant in policymaking.
This study examines the time-scale connectedness between returns on nine African stock markets and commodities markets across energy, agriculture, metals, and beverage. First, we examine multi-scale (short-, medium-, and long-run) wavelet structural relationships between African stocks and commodities using the bivariate wavelet coherence. We establish that commodities and African stock returns co-move across multiple scales and co-integrate in the long run, albeit sparse. Second, we analyze the portfolio performance of the African stock markets with other commodities using wavelet-based diversified and undiversified portfolios in a translation-invariant manner to calculate the scale-specific Sharpe ratios over different sub-periods rather than giving a one-shot look for the entire sample. This enables us to examine how risk-adjusted returns vary across different periods. The results confirm that having a combined portfolio of commodities and equities improves performance over different investment horizons. Specifically, we observe that in non-crisis periods, particularly from 2001-2006 the equally weighted and optimally weighted portfolios show the greatest performances. However, as we enter into the crisis zones such as the Asian crisis of 1997-2000 and the global financial and Eurozone debt crisis the risk-aversion of investors become prominent as the risk-minimizing portfolios record the highest performances. (C) 2020 Elsevier B.V. All rights reserved.
The objective of this study is to examine the effect of various uncertainty measures on the realized volatility of agricultural futures markets. In doing so, we use a range of uncertainty indicators in our analysis to investigate whether news-based uncertainty measures (e.g., geopolitical risk and economic policy uncertainty) have better predictive contents than the market-based uncertainty measures (e.g., crude oil volatility index, the US equity market VIX and exchange rate VIX). This comparison is important given that employing both measures has some specific benefits. Methodologically, we consider the application of the LASSO (least absolute shrinkage and selection operator) method as well as the heterogenous autoregressive (HAR) process. The in-sample estimates indicate that among the various news-based and market-based risk measures the latter provide better forecasts for the realized volatility of agricultural futures markets. The out-of-sample forecasts also confirm the same with the LASSO method outperforming the HAR process.
This study examines frequency volatility spillovers, connectedness and the nonlinear dependence between the European emission allowance (EUA) prices and renewable energy indices. For this purpose, we use a time-scale spillover index and different copula functions. The results show a dominance of short-term volatility spillovers between carbon prices and renewable energy indices over their long-term counterpart. More importantly, the spillover strength is high between carbon prices and both S&P clean energy and wind energy indices in the short term. Meanwhile, a strong spillover is most pronounced between the clean energy indices and the carbon price in the long term. Furthermore, the carbon price is predominantly the receiver of spillovers from the clean energy indices irrespective of the time horizon. Using dynamic copula, we show positive and dynamic dependence between the carbon prices and both clean and solar indices, whereas an asymmetric tail dependence between carbon prices and renewables, technology and wind indices.
There is a growing literature studying return spillovers between similar assets and assets of different classes during crisis periods. However, less is known about return spillovers across stock sectors under high and low volatility regimes and whether they are affected by oil price volatility. Using daily data from May 10th, 2007 to February 28th, 2020, we first study the return spillovers between US stock sectors under low and high volatility regimes by implementing a Markov regime-switching vector autoregression with exogenous variables model, while considering the Fama-French factors as conditioning variables. Return spillovers under low and high volatility regimes show that the energy sector is the largest transmitter and receiver of spillovers to/from other US equity sectors. Rolling window analysis shows that spillovers intensified since the outbreak of the COVID19 pandemic. Second, we apply linear and non-linear Granger causality tests from oil price volatility to the spillover indices. The results show evidence that oil volatility has a causal impact on the spillover dynamics of US stock sectors and that the impact is particularly strong in the high volatility regime. Although the energy sector is one of the smallest sectors of the US stock market, it plays a large role in the network connectedness of stock sectors. The results are of interest to individual and institutional investors who consider US equity investments and to policymakers.
We present an empirical study of renewable energy stock returns and their relation to four major investment asset classes stocks, currency, US Treasury bonds, and oil and several sources of uncertainty. Applying nonlinear causality and connectedness network analysis on data covering the period 2004-2016, we investigate the directionality and connectedness among different asset classes, as well as between uncertainties. First, from the results of the estimation of directionality and network spillovers, it can be concluded that the European stock market has a strong market dependence on renewable energy stock prices. Second, uncertainties have an economically significant impact on both return and volatility spillover in energy investments. Third, most of the uncertainties are net transmitters of volatility connectedness during the global financial crisis (GFC) and European sovereign debt crisis (ESDC): (C) 2018 Elsevier B.V. All rights reserved.
This study investigates the volatility dynamics of oil and gas prices in an environment characterized by postcoronavirus disease 2019 recovery, uncertainty, high inflation, and geopolitical tensions. Unlike previous studies, we examine a long-run series of high-frequency data on gas and oil prices from July 2007 to May 2022, which provides more than one million observations with which to analyze volatility. We compute realized volatility (RV) and decompose it into continuous volatility and jumps. We then investigate the relationship between uncertainty, investor sentiment, and RV, as well as its main components. Econometrically, we extend the heterogeneous autoregressive model of Corsi (2009) while considering not only disaggregate proxies for volatility (jumps and continuous volatility) and introducing uncertainty and heterogeneous investor sentiment, but also by allowing the model to include asymmetry, nonlinearity, and time variation according to the regime under consideration. Our results present three main findings. First, we find significant evidence of volatility decomposition, suggesting that both markets are characterized by significant jumps. Second, we show that trading volume, extra-financial news (uncertainty, investor sentiment), and jumps appear to drive commodity price volatility. Third, we find evidence of nonlinearity and threshold effects on energy price volatility. These findings are relevant for policymakers, regulators, investors, and portfolio managers, as they enable them to better characterize and forecast changes in commodity prices.
In this paper, we explore the impact of uncertainties on energy prices by measuring four types of Delta Conditional Value-at-Risk (Delta CoVaR) using six time-varying copulas. Three different measures of uncertainty (economic policy, financial markets and energy markets) are considered, and the magnitude and asymmetric effects of their influence are investigated. Our results suggest that there generally exists negative dependence between energy returns and changes in uncertainty. The risks of clean energy and crude oil returns are more sensitive to uncertainties in the financial and energy markets, while the impact of economic policy uncertainty is relatively weak. The upside and downside CoVaRs and Delta CoVaRs demonstrate significant asymmetric effects in response to extreme uncertainty movement. Our findings therefore have important implications for energy portfolio investment. (C) 2018 Elsevier B.V. All rights reserved.
In this paper, we investigate the hedging versus the financialization nature of commodity futures vis-avis the equity market using a ARMA filter-based correlation approach. Our results suggest that while gold futures are typically seen as a hedge against unfavorable fluctuations in the stock market, the majority of commodity futures appears to be treated as a separate asset class in line with their increasing financialization. Our results are robust to the presence of inflation, highlight the hedging role played by fuel (energy) commodity futures in the nineties, and reveal that the commodity financialization boosted since the 2000s. We also show that gold futures are partially a safe haven for equity investments in the short-term, but not in the mid-term. Finally, we uncover some hedging (financialization) of crude oil futures associated to global demand (oil supply) shocks. (C) 2019 Elsevier B.V. All rights reserved.
This paper is the first to examine the dynamic impact of temperature anomaly and macroeconomic fundamentals on agricultural commodity futures returns. Using a quantile regression approach, we report that temperature anomaly exerts a significant negative (positive) impact on returns of soybean, corn, cotton, and coffee (soybean, corn, and cocoa) futures in extreme bearish and bullish market conditions. Moreover, agricultural commodity futures returns appear to positively respond to aggregate stock returns and negatively influenced by exchange rate changes. However, such results vary across quantiles. We also report that economic activities and various uncertainty measures do not have a strong impact on the returns of agricultural commodity futures. Our study suggests that an adjustment in risk evaluation encompassing weather shocks would benefit portfolio management.
The urgency surrounding environmental sustainability has triggered an innovation of financing channels for climate and environmental projects. Green bond as one such channel has garnered immense interest from investors, with an implicit view that this fixed-income instrument is a relatively safer choice as an investment portfolio. Yet, the uncomfortable spread of greenwashing as a marketing spin has subjected green bonds to significant market volatility, at least as much as other financial assets or sectoral indices if not more. Whether green bonds as a financial instrument may incur losses to the extent of the loss in various sector indices, can be gauged by studying the nature of their contemporaneous growth. In this paper, we use daily data on green bonds and several S&P sectoral indices and a fractionally cointegrated vector autoregression framework (FCVAR) to study the extent to which green bonds dynamically co-move with various sectoral indices. Such a co-movement, if any, would elicit the extent to which a variation of uncertainty would determine an investors inclination to the diversification of a portfolio between an investment in a sectoral index and a green bond. The identifying mechanism is the shock-dissipation speed, which also informs a policymaker before choosing the right instrument to stabilise the system. We show that the system-wide shocks indeed dissipate slower than could be predicted by a conventional cointegrated VAR system. Further, the property of the slow error correction within the dynamic system of Green Bond and sectoral S&P indices, for instance, may demonstrate the speed of adjustment of the global economy to sudden shocks. Rigorous predictions exercises complement our baseline conclusions.
Australian National Electricity Market (NEM) provides efficient and smooth electricity transmission via a unique integration mechanism of different Australian regional electricity markets. We, therefore, examined the asymmetric time-frequency connectedness across five physically interconnected Australian regional electricity markets in the NEM using daily wholesale price data from 17 May 2005 to 31 December 2020. Due to direct physical interconnections and close geographical vicinities, Australian regional electricity markets have more connectedness within the region than across regions. The results of deseasonalized data revealed high spillovers among electricity markets during a crisis event, periods of abnormal weather, and regulatory sanctions implied on the NEM, which the findings from previous studies have not fully captured. Meanwhile, seasonality-adjusted results using X 13-ARIMA method highlighted dominance of negative spillovers both in the short- and long-run. Moreover, reforms in electricity policies by the Australian government played a significant role in shaping the total spillover index. Our study offers fresh insights on the NEM and stipulates significant policy implications for NEM, which the Australian state and federal government, policymakers, investors, retailers, and power suppliers must consider.
In light of the COVID-19 outbreak and the recent Russian war in Ukraine, this paper explores the asymmetric and nonlinear interconnectedness between financial and commodity markets using high-frequency intraday data. We employ cross-quantilograms (CQ), paired vine-based copulas, and copula vine-based regression analysis to examine the heterogeneous and asymmetrical connectedness among various assets. Our study presents several key findings: (1) connectedness among assets increases sharply during the COVID-19 pandemic and intensifies with the Russia-Ukraine war; (2) stronger tail dependence is observed in the lower tail, indicating asymmetric connectedness among the assets; (3) the S & P 500 and natural gas have a predictive influence on the crude oil market; and (4) increased uncertainty and volatility in global markets due to these events impact the interconnectedness of the assets in our study, particularly the dependence between crude oil and the other assets in the sample. These results have important implications for governmental agencies, policymakers, investors, and portfolio managers, emphasizing the need for a non-linear framework to capture heterogeneous and asymmetric connectedness dynamics under extreme market conditions.
Natural gas is an important source of energy in the global economy, hence understanding the drivers of its prices is of significant interest for economic agents. This paper investigates the role of structural shocks for the dynamics of the U.S. natural gas market within the Bayesian Structural Vector Autoregression framework applied by Baumeister and Hamilton (2019a, AER), to the crude oil market. This approach provides clear intuition for the identification strategy and allows us to correctly estimate the short-term price elasticity of natural gas supply and demand. Our results indicate that the former is low, whereas the latter is higher than the average estimate in the literature. We also show that market specific demand shocks explain a dominant fraction of natural gas prices variability, while the contribution of supply, aggregate economic activity and inventory shocks is important only during specific market events such as the recent outbreak of the COVID-19 pandemic. Finally, we illustrate how changes in supply in the era of shale gas revolution contributed to the dynamics of natural gas prices.
Natural gas is an important source of energy in the global economy, hence understanding the drivers of its prices is of significant interest for the many economic agents. This paper investigates the role of inventories for the dynamics of the U.S. natural gas market. Our contribution is twofold. First, within the threshold structural VAR framework we demonstrate that in a low inventory regime spot prices are more responsive to economic fundamentals in comparison to situation in which the inventories are high. Second, we present evidence that the level of natural gas inventories have a significant effect on the relationship between spot and futures prices. (C) 2020 Elsevier B.V. All rights reserved.
This study examines the impact of climate policy uncertainty (CPU) on fossil-based, as well as renewable and low-carbon-based energy markets. Leveraging advanced techniques such as Wavelet Coherence, Quantile CrossSpectral, and Quantile-on-Quantile analyses, our study uncovers that CPU is a significant driving force for both types of energy assets, though the effect of CPU on energy assets is time-varying. Noticeably, our findings reveal that CPU exerts a substantial adverse effect on most fossil-based energy commodity futures' returns. However, in normal or bearish markets, fossil energy assets exhibit a relatively stronger performance and display favorable associations with CPU at specific frequencies. Overall, fossil energy assets offer limited hedge opportunities against CPU, depending on quantiles and frequencies. Conversely, CPU favors the renewable and low-emission assets' returns, underscoring their resilience and robust hedging characteristics against CPU. These findings hold significant implications for environmentalists, investors, and policymakers, equipping them with valuable insights to make informed decisions and craft appropriate strategies amidst the backdrop of CPU-induced fluctuations in energy markets.
This paper examines the quantile dependence between energy commodities (oil, coal, and natural gas) and the real housing returns of the nine US census divisions for the period 1991-2019. In contrast to the literature on the association between oil and housing markets, we contribute by studying the effect of additional commodities on the housing market returns. We use a cross-quantilogram and quantile regression approach and find regional variation in the impact of energy commodities on housing returns. The effect within the same region varies over the quantile distributions. In general, we observe that all energy commodities are negatively associated with real housing returns. Significant correlations are found more often when the oil and housing returns are in similar quantiles. Coal and natural gas show a stronger relationship with higher quantiles of housing returns. Further, the results for coal and natural gas remains relatively stable after controlling for macroeconomic variables.
We quantify intraday volatility connectedness between oil and key financial assets and assess how it is related to uncertainty and sentiment measures. For that purpose, we integrate the well-known spillover methodology with a TVP VAR model estimated on a unique, vast dataset of roughly 300 thousand 5 min quotations for most heavily traded financial assets: crude oil, the US dollar, S&P 500 index, gold and US treasury bonds. This distinguishes our investigation from previous studies, which usually employ relatively short samples of daily or weekly data and focus on connectedness between two asset classes. We contribute to the literature across three margins. First, we document that market connectedness at intraday frequency presents a different picture on markets co-movement compared to the estimates obtained using daily data. Second, we show that at 5 min frequency volatility is mostly transmitted from the stock market and absorbed by the bond and dollar markets, with oil and gold markets being occasionally important for volatility transmission. Third, we present evidence that daily averages of intraday connectedness measures respond to changes in sentiment and market- specific uncertainty. Interestingly, our results contrast with earlier findings, as they show that connectedness among markets decreases in periods of high volatility owing to market-specific factors. Our study points to the importance of using high-frequency data in order to better understand financial and commodity markets dynamics.
This paper analyzes the causal relationship between renewable energy consumption, oil prices, and economic activity in the United States from July 1989 to July 2016, considering all quantiles of the distribution. Although the concept of Granger-causality is defined for the conditional distribution, the majority of papers have tested Granger-causality using conditional mean regression models in which the causal relations are linear. We apply a Granger-causality in quantiles analysis that evaluates causal relations in each quantile of the distribution. Under this approach, we can discriminate between causality affecting the median and the tails of the conditional distribution. We find evidence of bi-directional causality between changes in renewable energy consumption and economic growth at the lowest tail of the distribution; besides, changes in renewable energy consumption lead economic growth at the highest tail of the distribution. Our results also support unidirectional causality from fluctuations in oil prices to economic growth at the extreme quantiles of the distribution. Finally, we find evidence of lower-tail dependence from changes in oil prices to changes in renewable energy consumption. Our findings call for government policies aimed at developing renewable energy markets, to increase energy efficiency in the U.S. (C) 2018 Elsevier B.V. All rights reserved.
In this study, we explore the critical demand drivers of electricity consumption in Thailand based on monthly data from 2002 to 2020. Using Autoregressive Distributed Lag (ARDL), cross-quantile correlation (CQC), Generalized Method of Moments (GMM), and Granger-causality-in-quantile approaches, we find that industrial production and oil production positively contribute to next months aggregate and provincial energy consumption in Thailand, both in the short and long run. We also find that industrial production positively affects current electricity consumption, whereas electricity prices negatively affect current electricity consumption. Oil production, however, has no effect on current electricity consumption. Moreover, the CQC analysis finds evidence of cross-predictability running from industrial production and electricity prices to next months electricity consumption at the extreme and median quantiles of the distribution. Further, industrial production, electricity prices, and oil production Granger-cause energy consumption at the extreme and median quantiles of the distribution. Nevertheless, we show that the Thai governments energy policies are ineffective for reducing electricity consumption. Our findings have crucial policy implications for the electricity market efficient allocation and its reform.
In a first step, we model the multivariate tail dependence structure and spillover effects across energy commodities such as crude oil, natural gas, ethanol, heating oil, coal and gasoline using canonical vine (C-vine) copula and c-vine conditional Value-at-Risk (CoVaR). In the second step, we formulate portfolio strategies based on different performance measures to analyze the risk reduction and diversification potential of carbon assets for energy commodities. We identify greater exposure to losses arising from investments in heating oil and ethanol markets. We also find evidence of carbon asset providing diversification benefits to energy commodity investments. These findings motivate for regulatory adjustments in the trading and emission permits for the energy markets most strongly diversified by carbon assets. (C) 2018 Published by Elsevier B.V.
This study evaluates extreme uncertainty connectedness among top global energy firms. The sample comprises of 68 firms from four energy-related subsectors (oil & gas, oil & gas related equipment and services, multiline utilities, and renewable energy). To provide an overview of tail connectedness, we construct a high-dimensional network between firms by utilizing a generalized error decomposition and a sparse vector autoregression framework with a latent common factor. Our empirical results indicate that between the four subsectors, the renewable energy subsector exhibits the highest uncertainty transmission to other underlying subsectors, primarily credited to an increased within-subsector idiosyncratic uncertainty before the COVID-19 crisis. After the burst of the COVID-19 pandemic, due to the higher connectedness, the role of the renewable energy companies in the spillover network is further intensified. The uncertainty connectedness demonstrates a time-varying trait. While the oil and gas subsector exhibits greater long-term linkages with the oil and gas related equipment and services subsector, the long-run dynamics exhibit a lower interconnectedness as compared to the short-run. Finally, there is an increased connectedness among companies operating in the same subsector with similar size, attributing to similarity and competition.
We study the cross-quantile dependence of renewable energy (RE) stock returns on aggregate stock returns, changes in oil and gold prices, and exchange rates. Applying a recently developed cross-quantilogram approach, we provide two novel findings. First, although prior studies show that RE stock returns have a positive dependence on changes in oil prices and in the aggregate stock index, we find that the relationship is not symmetric across quantiles and that this asymmetry is higher in longer lags. Second, while the extant literature provides evidence that exchange rates and gold returns exert a positive influence on aggregate stock returns, we report that this positive influence on RE stock returns is observed only during extreme market conditions. These results are robust, (i) even after controlling for economic policy and equity market uncertainties, as well as (ii) in both a time-static full sample and recursive subsamples. (C) 2019 Elsevier B.V. All rights reserved.
This paper examines the nonlinear effect of oil price shocks on precious metal returns using Markov regime switching regression. We use Readys (2018) approach to decompose oil price changes into supply, demand, and risk driven shocks. Results indicate a significant positive impact of demand and supply shocks and a negative impact of risk shocks on precious metal returns. Although we find evidence of switching between low and high volatility regimes, we do not find strong regime effect on supply or demand shocks contemporaneous relationship with precious metal returns. However, risk shocks influence on precious metal returns is strongly regime dependent. These results generally hold for different distributional specification of error terms. (C) 2018 Elsevier B.V. All rights reserved.
Several scholars have highlighted the idea that energy consumption in general and consumption of renewable energy (RE) in particular may be a potential driver of economic growth. In this paper, we examine the relationship between RE production and economic activity in Canada between May 1966 and December 2015. By applying quantile causality (Troster, 2018), we adopt a nonlinear approach considering all quantiles of the distribution and analysing monthly data consisting of RE production and the Canadian Industrial Production Index (IPI). We find evidence of a nonlinear relationship in Canada, an important result that widely-used linear models fail to capture. Our main findings imply a unidirectional relationship going from the IPI to RE production, which supports the Conservation hypothesis. The directionality between RE and economic growth is sensitive to the market conditions in Canada.
The study evaluates nonlinear price transmission mechanisms between clean energy stock and crude oil price in levels, mean, and error variances. We propose a novel way of combining a two-regime threshold vector error correction with the DCC-GARCH model to demonstrate a statistical coherency. The study advances the literature by examining the long-and short-term dynamics of these assets in their levels where the information of nonstationarity in the first moment of these assets is preserved, which generally disappears or becomes a random walk process in the return series. The combined model is then applied to derive a regime dependent dynamic hedging strategy, which has been complemented by a wavelet-based hedging strategy. The data spans from 2nd April 2004 to 10th July 2020 is divided into sub-periods to incorporate the financial crisis and ongoing COVID pandemic. Our findings suggest a nonlinear regime-dependent long-term connectedness among the assets in the first and second moments. The study affirms that the price transmission path between the two asset classes is nonlinear. The research indicates that the clean energy index emerges as the dominant influencer on the crude oil price over the post-crisis subsample. A nonparametric nonlinear causality further validates the theoretical rationale of an integrated model. While examining the impact of several control variables on the relationship between these assets, we find that policy uncertainty is an important thread which further demonstrates the prominence of clean energy stocks. Our findings are in accordance with the global focus of divestment in the non-fossil fuel energy sector. This study differs from previous studies in its apt application of statistical modelling techniques on the theoretical and empirical ground. The outcomes are encouraging for the global investors and traders communities as the integrated model has the potential to fetch higher returns compared to commonly used volatility-based models. (c) 2021 Elsevier B.V. All rights reserved.
We explore the effect of green credit policy on firm performance of listed firms in China. We find that green credit policy reduces firm performance in heavily polluting industries. This effect is more prominent in stateowned enterprises, firms with large size, high institutional ownership, high analyst coverage and during high economic policy uncertainty period. Moreover, we observe that green credit policy decreases heavily polluting firms performance by increasing firm financing constraints and decreasing investment level. Our results help to restrain heavily polluting enterprises and promote industrial transformation in developing markets.