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The influence of the “environmental-friendly” character through asymmetries on market crash price of risk in major stock sectors
Abstract
This paper investigates the impact that the ‛green character’ of stocks generates on their market price of risk. The
study covers a spectrum of international large-cap stocks in influential sectors since 1 January 2013 up to 5 July
2022. Focusing on the asymmetric and shape features of price risk, econometric outcomes reveal that wellestablished
(Industrials) or highly profitable (Technology) sectors are less affected by the skewness/kurtosis
price of risk or the “environmental-friendly” features. On the contrary, sectors vulnerable to alterations in income
or wealth, such as Financials and Real Estate are more sizably influenced by negative asymmetry, kurtosis, and
the higher volatility in bear markets. The ‛green character’ lowers the market price of stocks, increases risk
premia due to higher probability for downwards movements and renders investors more risk-averse and
nevertheless, this impact only reflects the initial period of green innovation with high anti-polluting costs as the
green label could prove to be greatly beneficial in the future.
... [26,27] . The research analyzes how emerging technologies combine with employee participation and Green HRM to solve organizational challenges in practice integration according to Dimitriadis et al. [28] . This study develops a complete framework that strengthens existing Green HRM understanding through participatory workplace strategies while offering operational guidance to implement practices [29] . ...
... By combining Green HRM practices with current green technologies firms can create sustainable operations that promote both responsibility and creative behaviors within their organizations. The combination of holistic operations management with employee sustainability initiatives creates market competition by engaging workers in sustainability goals to drive operational success [11,28] . Primary adoption of green technologies by organizations drives their sustainability commit-ment to permeate throughout their employee base building both resilience and innovation within their business model. ...
This study delves into Green Human Resource Management (GHRM) as well as its link with organizational sustainability. Through a review of literature, this research hopes to fathom the place of eco-sustainability in employee involvement and enhancing environmental responsibility via Green HRM. The main aim of this study is to thoroughly examine the literature on GHRM and its contribution to improving organizational sustainability through employee engagement and diminished environmental impact. The PRISMA guidelines were utilized in this conceptual review in order to investigate the impact that Green HRM has on the sustainability of organizations. The results show that Green HRM is vital for organizational sustainability since it reduces their ecological footprint, raises employee morale, and improves reputation. Additionally, the study emphasizes the significance of including Green HRM practices into organizational strategies and operations so as to achieve long-term sustainability. Findings indicate that through implementing green HRM practices organizations can record increased employee engagement levels, reduced turnover rates and improved customer satisfaction. Moreover, there are lower costs associated with green human resource management practices because of less waste and a better image for the brand. Nonetheless, some challenges and constraints towards change have been observed in relation to the implementation of these practices by companies. This study therefore gives an overview of how green HRM enhances organizational sustainability, thereby urging firms to adopt sustainable HR practices for successful operations in the future. These findings are important for human resource professionals, policymakers, or organizations who want to embed sustainability into their business strategy.
... This research is built on earlier studies such as Koop, Pesaran, and Potter (1996), Shin (1998), Gabauer (2020), Stenfors, Chatziantoniou, and Gabauer (2022), Dimitriadis, Koursaros, and Savva (2024a, 2024b, 2024c, Hong et al. (2024) and Kyriazis et al. (2024). This work is connected with earlier academic papers that investigate how geopolitical risk is linked with the real economy (Balestra, Caruso, and Di Domizio 2024;Kyriazis and Economou 2023;Li et al. 2024;Yang et al. 2023) and financial markets (Baur and Smales 2020;Chiang 2022;Fang et al. 2024;Ngo et al. 2024;Salisu, Lasisi, and Tchankam 2022;Selmi, Bouoiyour, and Wohar 2022;Wang, Wu, and Xu 2019;Zheng et al. 2023). ...
This paper examines the dynamic interplay between the global geopolitical risk and eleven decentralized finance (DeFi) digital currencies during the inflationary burden caused by the Russia-Ukraine war episodes. Daily data spanning from 13 October 2021 to 29 October 2024 and the innovative Quantile-Vector Autoregressive (Q-VAR) methodology are employed for estimating the pairwise, joint and network linkages at the lower, middle and upper quantiles. High levels of geopolitical risk are more connected with bull markets of the DeFi assets and new war episodes strengthen this relation. Geopolitical tensions combined with high inflation lead to the GPR becoming major determinant of DeFi markets so contributing to the transition to the digital decentralized cashless financial system. Maker is the leading DeFi asset in this transition and constitutes a promising successor of fiat currencies that suffer from devaluation generated by conflicts.
Using a flexible statistical framework that accounts for time-varying skewness and leptokurtosis, we examine the stochastic behavior of Bitcoin in comparison to five major currencies. The empirical findings reveal that the distribution of all series is leptokurtic. Once the effect of skewness-kurtosis is considered, the true price of risk is obtained, with implications on policymakers’ and investors’ strategies.
This paper uses weekly data from July 01, 2011 to July 09, 2021 to examine the dynamic nonlinear connectedness between the green bonds, clean energy, and stock price around the COVID-19 outbreak in the global markets. By building a time-varying parameter vector autoregression model (TVP-VAR), the comparison analyses of pre- and during the COVID-19 sample groups verify the existence of nonlinear and dynamic correlation among the three variables. First, prior to the COVID-19 pandemic, the simultaneous impacts of clean energy on stock price increased over time. Second, the results of impulse responses at different horizons indicate that green bonds lead to a short-term increase of clean energy, and it exerts an increasingly positive impacts after the COVID-19 outbreak. The COVID-19 has weakened the negative impacts of green bonds on stock price in the medium term. Finally, through the analysis of impulse responses at different points, we find that stock prices will rise when clean energy is subjected to a positive shock, and this positive effect is stronger during economic recovery period than in the other two periods.
This chapter presents a historical overview of the emergence and evolution of ESG assets and, in particular , analyses the main market trends that have been observed in recent years in relation to these assets. The authors intend to present a summary of the main moments and phases that these assets have gone through, from the moment of their appearance in 2007, the year in which the European Investment Bank carried out its Climate Awareness Bond as a test issuance. The movement associated with the issue of these assets is initiated by supranational entities with little homogeneity and no fixed conventions. To overcome this impasse, the green bond principles emerged and a process of defining the characteristics of these assets began, with a particular focus on transparency and the governance process. From this stage onwards, the market showed interest in these financial products and hence the emergence of a har-monising movement regarding green bond standards in which Europe seems to have taken a leading role.
The study aims to empirically estimate the nexus of green bond financing with renewable energy index OECD countries. Using the OECD countries data over the period of the 2011–2019, the study estimated the nexus between constructs. To justify the study findings and present widespread policy implications on recent topicality Padroni unit root test, FMOLS and DOLS technique is applied. For robustness analysis, long-run sensitivity analysis using FMOLS extension is used, and a comparative picture of green bond financing nexus with renewable energy index is presented. The study presented the consistent effects of green bond financing on renewable energy index indicators. This asymmetrical role of green bonds is confirmed on renewable energy indicators over the sample period. OECD countries injected 31% role of green bond financing on renewable energy index constructs, and it raised 9.4% of per unit energy efficiency in renewable energy systems; by this, the study findings warrant maximum support through public office, energy ministries, and departments for energy efficiency optimization. The study presents multiple policy implications to enhance renewable energy generation for energy efficiency through different alternative sources. Despite growing literature, the empirical discussion on this topicality is still shattered and less studied, which is extended and contributed by recent research. Furthermore, efficient regulation in the renewable energy sector may convert financial uncertainty into a huge opportunity. Investing in renewable energy stocks might help investors diversify their portfolios.
We study an equilibrium risk and return model to explore the effects of the coronavirus crisis and associated skewness on the market price of risk. We derive the moment and equilibrium equations, specifying skewness price of risk as an additive component of the effect of variance on mean ex-pected return. We estimate our model using the flexible skewed generalized error distribution, for which we derive the distribution of returns and the likelihood function. Using S&P 500 Index re-turns from January 1980 to mid-October 2020, our results show that the coronavirus crisis generat-ed a deeply negative reaction in the skewness and total market price of risk, more negative even than the subprime and the October 1987 crises.
The relationship between risk and expected returns has been investigated extensively in the financial economics literature. Theoretical models generally predict a positive relation between the two. Nevertheless, the empirical findings so far have been inconclusive. Using a generalization of the analytical framework developed by Theodossiou and Savva (2016) along with time-varying asymmetry, linked to the upside and downside uncertainty, the risk-return puzzle is investigated across international stock markets. The investigation reveals that the contradictory findings are the result of ignoring the impact of skewness on the total price of risk. That is, in the absence of skewness the relationship between risk and return is positive as depicted by finance theory. However, negative skewness results in lowering the total price of risk and in some cases reverting its sign from positive to negative.
The relationship between risk and return has been one of the most important and extensively investigated issues in the financial economics literature. The theoretical results predict a positive relation between the two. Nevertheless, the empirical findings so far have been contradictory. Evidence presented in this paper shows that these contradictions are the result of negative skewness in the distribution of portfolio excess return and the fact that the estimation of intertemporal asset pricing models are based on symmetric log-likelihood specifications.
Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2015.2201 .
This paper was accepted by Jerome Detemple, finance.
As responsible corporate citizens, all businesses should contribute to social development of the country. However, from a business perspective, the shareholders of a company are interested in the return for their investment. They would prefer to have a quantifiable method to know the exact impact of the investment on CSR on company profitability. If there are no proper information on the benefits of CSR, it is difficult to persuade the corporate to pump in scarce resources to CSR initiatives, which otherwise could have been used to generate definite benefits. This research attempted to fill this gap by developing a set of data on the financial benefits of CSR initiatives. A questionnaire-based survey was carried out among the listed companies in the Colombo Stock Exchange. On average the companies spend about 6.6% of their turnover on CSR related activities, including training, research and community work. In the case of relationships between CSR activities and financial performance, only in employee relations and customer/supplier relations show significant positive correlations with financial performance. In addition to these two, community relations also show a significant positive correlation with perceived benefits of CSR. Community relations and environment is not significantly correlated with any of the financial indices.
This study investigates the dynamic nexus that major international currencies (US dollar, Euro, Japanese yen)
exhibit with cryptocurrencies and highly innovative digital money (DeFi and NFT assets) during inflationary
periods such as the Russia-Ukraine conflict (from 14 December 2021 until 1 March 2024). The Quantile Vector
Autoregressive methodology as in Cunado et al. (2023) and daily data are adopted to investigate the net joint
extended dynamic connectedness and network connectedness at lower and upper quantiles. Conventional international
currencies act as hedgers against shocks while major cryptocurrencies are only modest generators
with Ripple being an influential absorber of effects. DeFi mainly serve for counteracting losses from conventional
investments in bear or bull markets and Maker is the most prominent generator of spillovers while NFTs mostly
rely on a few very strong leaders –Gala being by far the strongest- to have an impact, imitating Bitcoin in the
early cryptocurrency era.
This study investigates whether representative sectoral stock indices, gold, oil, Bitcoin, and wheat canmitigate risk and improve portfolio performance during normal times versus crises. The cutting-edgeQuantile Vector Autoregressive model and the Generalized Dynamic Conditional Correlations(Generalized-DCC) framework are adopted covering from 9 January 2017 until 30 August 2022.Econometric findings by the Q-VAR reveal that oil presents the strongest connection with commod-ities and stock indices and that Bitcoin and wheat despite their significant linkages with financialmarkets fail to act as safe havens. Moreover, GDCC-GARCH indicates that the returns of sectoralindices are weakly related but display powerful volatility co-movements. Gold serves efficiently asa hedger and oil follows and both act as better shelters during crises. Nevertheless, Bitcoin partlyabides by conventional markets in stressed periods. Notably, wheat reliably works as a hedger overallbut does not become a safe haven during crises.
This paper investigates the non-linear causal nexus at lower, medium, and upper quantiles that cryptocurrencies can generate as a hedging mechanism for international benchmark indices, as represented by the MSCI World index and its sectoral sub-indices. Significant causality-in-mean between each of the assets examined is detected at lower quantiles only in a small number of sectors, whereas all analysed assets are found to trigger important non-linear causal impacts on the volatility of the selected benchmark indices, particularly during periods denoted to possess significant asset price fluctuations. From a financial perspective, this result suggests that cryptocurrencies are developing to possess a very similar ability to that of long-standing traditional financial assets, serving as a hedging mechanism against extreme fluctuations within investor portfolios that include MSCI constituents during a variety of very different market conditions.
The COVID-19 pandemic disrupted almost all spares of global social, psychological, and economic life. The emergence of various variants and corresponding variations in daily infection asymmetrically influenced economic indicators. This study extends the existing literature by exploring the hedging potential of crude oil, carbon efficiency index of green firms, and bitcoin during this pandemic. This objective is realized by employing the recently advanced rolling window multiple correlation of Polanco-Martínez (2020). This approach is based on the new p-value corrected method, which has advantages over other correlation methods. The sample observations are based on daily data from 1/22/2020 to 12/20/2021. In the bivariate case, we find a significant positive correlation between COVID-19 and CEI, while a negative impact is observed between COVID-19 and WTI. Similarly, we observe a significant and nonlinear association between COVID-19 and BTC. However, our findings show positive and significant correlations among variables in the multivariate case. The overall findings show that CEI and BTC can be safe havens for investors during this worse pandemic. The study's robust findings can be used to derive important policy implications worldwide during the COVID-19 pandemic.
Green assets delivered high returns in recent years. This performance reflects unexpectedly strong increases in environmental concerns, not high expected returns. German green bonds outperformed their higher-yielding non-green twins as the “greenium” widened, and U.S. green stocks outperformed brown as climate concerns strengthened. Despite that outperformance, we estimate lower expected returns for green stocks than for brown, consistent with theory. We estimate expected returns in two ways: ex ante, using implied costs of capital, and ex post, using realized returns purged of shocks from climate concerns and earnings. A theoretically motivated green factor explains much of value stocks’ recent underperformance.
Vigorously developing the clean energy industry, improving the carbon allowance trading scheme, and issuing green bonds can effectively reduce emissions. To this end, this study aims to investigate the time-varying connec- tions among clean energy, carbon, and green bonds through the DCC-MIDAS model, thus providing a bird's-eye view of their dynamic nexus. A non-parametric causality-in-quantile method is also employed to adequately cap- ture the asymmetric causation of economic policy uncertainty (EPU) and the oil volatility index (OVX) on cross- asset correlations under different market conditions. The primary results imply complicated links among these three assets, with alternating positive and negative trends throughout the sample period. Notably, turbulence in financial markets can exacerbate network connectivity, particularly during the COVID-19 pandemic. Moreover, EPU and OVX can serve as strong predictors across various distributions of cross-market connections, which indi- cates that co-movement between assets is vulnerable to exogenous risks, especially under normal market condi- tions. Our findings have broader implications for market participants and policymakers.
In this paper, we comprehensively investigate the multifractal scaling behavior and efficiency of green finance markets, conventional equity indices and crude oil by index-based asymmetric multifractal detrended fluctuation analysis. The overall empirical findings revealed that upward and downward trends of green finance market indices exist significant multifractality and considerable asymmetry. And they’re far from efficient no matter small or large fluctuations. In the study, the informational inefficiency levels are ranked by several multifractality degree measures. The green finance markets show relatively efficiency, compared to equity and crude oil markets. We also analyze the dynamics of the multifractality and inefficiency of green financial market indices by the rolling window approach. At last, some relevant discussions and implications of the empirical results are presented.
This paper explores the pricing of green bonds in the Italian primary and secondary market and their implications for sustainability by investigating the effect that a green label has on a bond's spread through propensity score matching. From a sample of 420 fixed-rate, euro-denominated bonds priced at on the euro mid-swap from 2014 to the first half of 2021, bonds with a green label are matched with conventional bonds, to assess whether there is a significant difference in spreads between them. The results on the primary market show that green bonds' issuance spread is, on average, 35 to 40 basis points lower than that of comparable conventional bonds at a highly significance level.
The spread discount seems to persist regardless of the issuer's sector, even with a difference in the magnitude, being remarkably higher for corporates. On the secondary market, the estimation results confirm the discount impact of the green label on the bonds' market spread, even if this impact is lower than at the issuance. This study aims at giving better coverage of the advantages green bonds are able to generate also in a relatively small and illiquid financial markets. Furthermore, we show that green bonds are effective instruments for issuers to achieve a lower cost of debt to finance green projects and for investors to support the transition to a greener and more sustainable economy.
The long-term and sustainable development focuses of green bond together with its increasing popularity drives the need for a better understanding of its hedging effects against market risks. Our study investigates whether and how green bond can act as a hedging instrument against implied volatility, a measure of forward-looking market uncertainty. We find evidence of significant time-varying connectedness between green bond and implied volatilities of the stock, energy, and commodity markets. Building on this characteristic, investors are required to adopt an active portfolio management strategy to ensure the hedging effectiveness of green bond against implied volatilities. Specifically, this strategy requires frequent switches between long and short positions in the green bond market. Our simple simulation study shows evidence that applying connectedness regime-dependent trading strategies can increase the hedging effectiveness of green bond against implied volatilities in terms of risk-adjusted returns.
In this study, we propose a novel quantile frequency connectedness approach that enables the investigation of propagation mechanisms by virtue of quantile and frequency. This approach allows for the analysis of connectedness measures considering either different frequencies for a given quantile or different quantiles for a given frequency. We investigate dynamic integration and return transmission among a set of four well-established environmental financial indices, namely the S&P Green Bond Index, MSCI Global Environment, Dow Jones Sustainability Index World, and S&P Global Clean Energy over the period from November 28th, 2008 to January 12th, 2022. S&P Green Bond Index and S&P Global Clean Energy appear to be both short-term and long-term net receivers of shocks while MSCI Global Environment and Dow Jones Sustainability Index World are both short-term and long-term transmitters of shocks. We also find that Total Total Total connectedness indices (TCIs) are heterogeneous over time and economic event dependent. Furthermore, while the time-domain TCI is rather symmetric across quantiles, this is not the case for either the short-run or the long-run TCI.
While clean energy assets have become new investment vehicles for market participants, their potential for diversification of traditional investments has not been fully explored in previous studies. To fill this void, this paper investigates whether diversifying into clean energy stocks or green bonds could reduce portfolio downside risk for investors holding dirty energy stocks or international equity indices. Specifically, the risks of equity portfolios are compared before and after they are blended with clean energy assets. The incremental impact on the portfolio risk profile is evaluated through variable weightings for clean energy assets. The persistence of diversification effects is then explored by changing the assessment period. Results show that both green bonds and clean energy stocks provide risk diversification benefits for investors with dirty energy stocks. However, green bonds reduce risk while clean energy stocks generally increase the risk of the international equity index portfolio. Further analysis indicates the robustness of the results for the recent COVID-19 period. The findings broaden the limited understanding of the risk reduction capacity of clean energy assets to conventional equities and provide useful implications for investors seeking to decarbonize their equities and portfolio managers designing optimal portfolios to hedge against downside risks.
Using survey evidence from European asset managers, we provide insights into their green bond investment activities and the factors that affect their investment decisions. We find that the majority of investors are actively invested in the green bond market via a variety of investment channels. Investors prefer green bonds issued from corporate issuers and sovereigns and we find that there is strong unmet investor demand for green bonds from these issuer types, in particular from non-financial corporates in the industrials, automotive and utilities sectors. Competitive pricing and strong green credentials, both pre- and post-issuance, are the most frequently named factors impacting respondents' decision to invest in a green bond, and unclear and poor reporting on how bond proceeds are allocated to green projects induces a majority of investors to not invest in a green bond or to sell a bond if already included in the portfolio. Among policy measures to grow the green bond market, preferential capital treatment for low-carbon assets and minimum standards for green definitions receive the highest investor support, but respondents are divided whether a strict definition of ‘green’ or a less strict definition would be more beneficial for scaling up the green bond market.
This is the first study to empirically tests a price difference between labeled and unlabeled green bonds and to identify which factors contribute to any price difference although both bonds may eventually be used to fund environmentally beneficial projects. We study the effect of the green label, focusing on especially the yield of labeled and unlabeled green bonds after propensity score matching. We find that unlabeled green bonds are likely to have higher yields, despite unlabeled and labeled green bonds having the same characteristics.
The study in the age of the 4th industrial revolution examines the time and frequency domain connectedness and spill-over among Fintech, green bonds, and cryptocurrencies. Using daily data from November 2018 to June 2020, we use both DY (Diebold & Yilmaz, 2012) and BK (Baruník et al., 2017) to examine the volatility con- nectedness of returns series. The results of DY suggest that, first, the total connectedness of 21st century tech- nology assets and traditional common stocks is very high, and hence in the turbulent economy, there is a high probability of contemporaneous losses. Second, Bitcoin, MSCIW, MSCI US, and KFTX are net contributors of volatility shocks whereas US dollar, oil, gold, VIX, green bond and green bond select are net receivers. Therefore, Fintech and common equities are not good hedging instruments in the same portfolio. Third, the short-term witnesses higher volatility transmission than the long-term. That is, holding assets for a long-term is likely to mitigate risks whereas trading financial assets in the short-term can increase risk because of higher volatility. Fourth, the traditional assets, gold and oil, as well as modern assets, green bonds, are useful as good hedgers compared with other assets because shock transmissions from them to Fintech, KFTX are below 0.1% and, more importantly, the total volatility spill-over of all assets in the sample is moderately average, accounting for 44.39%.
Green bond markets are expanding precipitously and proceeds are increasingly being allocated to renewable energy. There is a gap in the empirical literature on the policies affecting green bond finance for the renewable energy assets critical to achieving Paris Agreement emissions reduction targets. To assess the impact that Nationally Determined Contributions (NDCs) to the Paris Agreement have on green bond finance for renewable energy, this study employed a difference-in-differences (DiD) analysis using an original panel dataset of $25 billion in green bond proceeds allocations in 66 countries between 2008 and 2017. An original normalized index of NDC robustness was constructed to measure unique NDC impacts on green bond disbursements to renewable energy. The results are the first to show that in the years following their submission in 2015, comparatively stringent NDCs demonstrated large positive impacts on green bond allocations to renewable energy with 99% statistical significance. These findings suggest that beyond conventional economic policy supports, climate commitments can drive global emissions reductions by inciting greater green bond finance for the renewable energy projects vital to achieving emissions reduction targets.
We examine the multifractal scaling behaviour and weak form market efficiency of clean energy stock indices using an asymmetric MF-DFA. We find asymmetric multifractality in the US, European, and global clean energy stock indices. Asymmetric multifractality in the clean energy stock index of the US is due to fat-tails and long-range correlation. However, for European and global clean stocks, multifractality is due only to fat-tailed distribution. We find higher efficiency in the upward trend of the European and global clean stock markets whereas, for the US, the market is less efficient when the market is upward trending. The time-varying market deficiency measure shows that US clean energy stocks are becoming relatively more efficient over time.
China has become a significant player in the green bond market globally after the issuance of the first green bond in China in 2015. Given the nascency of the green bond market in China, there are still two main questions that remain under-researched in the relevant scholarship. The first question relates to whether the multiple definitions of green bonds and its eligible use of proceeds under the Chinese regulations are consistent with the international standards. The second question centres on the degree of divergence of the transparency requirements under the Chinese regulations themselves. Using doctrinal and comparative analysis, this article discovers that, despite the generally consistent definitions of green bond under the Chinese regulations, there are still some slight differences in terms of eligible uses of proceeds and information disclosure. Potentially these differences may affect the lender’s investment assessment and their decision-making of whether to provide financing to the issuer.
We study price connectedness between the green bond and financial markets using a structural vector autoregressive (VAR) model that captures direct and indirect transmission of financial shocks across markets. Using heteroskedasticity to identify the structural VAR model parameters, our empirical findings reveal that the green bond market is closely linked to the fixed-income and currency markets, receiving sizeable price spillovers from those markets and transmitting negligible reverse effects. We also show that, in contrast, the green bond market is weakly tied to the stock, energy and high-yield corporate bond markets. These findings have implications in terms of portfolio and risk management decisions for environmentally aware investors holding positions in green bonds.
The issue of how to finance the transition to a low-carbon economy in order to achieve the Paris Agreement’s goal is crucial, especially considering the enormous amount of financing necessary to shift from rhetoric into action. Green bonds have recently emerged as one of the best candidates to help mobilizing financial resources towards clean and sustainable investments. Despite the growing relevance of green bonds, there is limited evidences on whether such bonds are actually convenient in comparison to other bonds with similar characteristics except for the “greeness”. By adopting a propensity score matching approach, we study 121 European green bonds issued between 2013 and 2017. We find that green bonds are more financially convenient than non-green ones. The advantage is larger for corporate issuers, and it persists in the secondary market. Our findings support the view that these bonds can potentially play a major role in greening the economy without penalizing financially the issuers.
We use green bonds as an instrument to identify the effect of non-pecuniary motives, specifically pro-environmental preferences, on bond market prices. We perform a matching method, followed by a two-step regression procedure, to estimate the yield differential between a green bond and a counterfactual conventional bond from July 2013 to December 2017. The results suggest a small negative premium: the yield of a green bond is lower than that of a conventional bond. On average, the premium is -2 basis points for the entire sample and for euro and USD bonds separately. We show that this negative premium is more pronounced for financial and low-rated bonds. The results emphasize the low impact of investors’ pro-environmental preferences on bond prices, which does not represent, at this stage, a disincentive for investors to support the expansion of the green bond market.
We demonstrate that analysis of long series of daily returns should take into account potential long-term variation not only in volatility, but also in parameters that describe asymmetry or tail behaviour. However, it is necessary to use a conditional distribution that is flexible enough, allowing for separate modelling of tail asymmetry and skewness, which requires going beyond the skew- t form. Empirical analysis of 60 years of S&P500 daily returns suggests evidence for tail asymmetry (but not for skewness). Moreover, tail thickness and tail asymmetry is not time-invariant. Tail asymmetry became much stronger at the beginning of the Great Moderation period and weakened after 2005, indicating important differences between the 1987 and the 2008 crashes. This is confirmed by our analysis of out-of-sample density forecasting performance (using LPS and CRPS measures) within two recursive expanding-window experiments covering the events. We also demonstrate consequences of accounting for long-term changes in shape features for risk assessment.
We introduce a “bad environment–good environment” (BEGE) technology for consumption growth in a consumption-based asset pricing model with external habit formation. The model generates realistic non-Gaussian features of consumption growth and fits standard salient features of asset prices including the means and volatilities of equity returns and a low risk-free rate. BEGE dynamics additionally allow the model to generate realistic properties of equity index options prices and their comovements with the macroeconomic outlook. In particular, when option-implied volatility is high—as measured, for instance, by the VIX index—the distribution of consumption growth is more negatively skewed.
We investigate the impact of 19 announcements of environmental regulation on the equities listed on the Australian Stock Exchange over the period 2005–2011. Using a well-established event study methodology, we assess whether these announcements are value constructive or destructive for equity investors. Additionally, we estimate the change in systematic risk following the announcements. Our results show that the Australian market was particularly sensitive to the carbon pollution reduction scheme (CPRS) announcement. A cumulative abnormal return of −31% was recorded in the alternative energy sector after Australia submitted its target range to the Copenhagen Accord. We observe that a move towards a greener nation has a mixed effect on abnormal returns with apparent sector-by-sector differences. Green policies appear to affect the long-term systematic risk of industries, leading to the diamond risk phenomenon.
The authors find support for a negative relation between conditional expected monthly return and conditional variance of monthly return using a GARCH-M model modified by allowing (1) seasonal patterns in volatility, (2) positive and negative innovations to returns having different impacts on conditional volatility, and (3) nominal interest rates to predict conditional variance. Using the modified GARCH-M model, they also show that monthly conditional volatility may not be as persistent as was thought. Positive unanticipated returns appear to result in a downward revision of the conditional volatility, whereas negative unanticipated returns result in an upward revision of conditional volatility. Copyright 1993 by American Finance Association.