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Is green investment different from grey? Return and volatility spillovers between green and grey energy ETFs
Investment in Green energy is becoming a popular alternative asset class for investors, primarily due to its environment-friendly attributes. However, there is a dire need for subjective evaluation of this emerging asset class based on the risk-return dynamics to which investors are exposed. To respond to this call, in this study, we conduct this evaluation utilizing a unique and rich data set consisting of daily prices of exchange-traded funds (ETFs) established on different asset classes. We use Vector autoregression and Baba-Engle-Kraft-Kroner parameterization of multivariate GARCH models and assess the relative strength of return and volatility spillovers from the Green and Grey energy markets. Our results reveal the return shocks originated in the Green energy market and transmitted to other markets are more pronounced. It is also observed that the potential to earn high returns and the weak correlation of Green energy ETFs with the traditional asset classes are the crucial factors helpful in inviting attention and investment of investors after 2015. Although our results further suggest that the role of Grey energy is diminishing, as shown by the Impulse response functions and the coefficients of multivariate ARCH and GARCH. Nonetheless, for some asset classes, e.g., Bonds, the volatility spillovers that originated in the Grey energy market are still prominent and robust.
Journal Article
Effects of Liquidity on TE and Performance of Japanese ETFs
This study identifies a nonlinear relationship among liquidity, tracking error, and risk-adjusted performance in JETFs. Collecting daily data for 1077 JETFs from January 2008 to April 2022, we find a concave association, whereby both highly liquid and highly illiquid JETFs exhibit lower risk-adjusted returns and higher tracking errors. Employing quantile regression, we further show that smaller, less liquid JETFs tend to deliver superior risk-adjusted performance. When comparing across listing venues—Japan, the U.S., Ireland, and Luxembourg—we find that the impact of liquidity on performance is most pronounced in the Japanese market, which also shows the highest average tracking error. In contrast, U.S.-listed JETFs offer the lowest tracking error. These results suggest that investors may benefit from choosing smaller JETFs listed in Japan.
Journal Article
Exchange-traded funds for dummies
Shows you in plain English how to weigh your options and confidently pick the ETFs that are right for you to build a lean, mean portfolio and optimize your profits.
Book
Evaluating the Performance of Real Estate Exchange-Traded Funds
This study examines the net monthly returns of real estate exchange-traded funds (ETFs) through various performance evaluation models and market situations. The results reveal that these ETFs generated positive alphas and outperformed benchmark indices in absolute returns. However, their performance varied across market conditions, demonstrating both outperformance and underperformance compared to U.S. and global stocks. During the COVID-19 pandemic, real estate ETFs displayed a decline, trailing behind U.S. and global equities in both absolute returns and risk-adjusted performance. This emphasized their vulnerability during economic crises. Utilizing the Carhart four-factor model, significant exposure of real estate ETFs to the stock market was observed. Moreover, an assessment of ETF portfolio managers’ skills indicated proficiency in security selection but limited capabilities in market timing.
Journal Article
Investing in Residential Real Estate: Understanding Homebuilder Exchange-Traded Fund Performance
Homebuilder ETFs provide investors with a diversified portfolio of residential construction and sales companies which reduces risks associated with individual stock selection in the sector. This study examines the net monthly returns of homebuilder exchange-traded funds (ETFs) through various performance evaluation models and market situations. The results reveal that these ETFs outperformed benchmark indices in absolute returns. Despite homebuilding being part of the real estate sector, the correlation between monthly returns of homebuilder ETFs and the Dow Jones US Real Estate Index, though positive, is not very high. The performance of ETFs varied across market conditions, demonstrating both outperformance and underperformance compared to U.S. stocks. During the COVID-19 pandemic, homebuilder ETFs displayed a decline, trailing behind U.S. equities in both absolute returns and risk-adjusted performance. This result emphasizes their vulnerability during economic crises. Utilizing a modified version of the Carhart factor model, significant exposure of real estate ETFs to the stock market was observed. Moreover, an assessment of ETF portfolio managers’ skills indicated proficiency in security selection but limited capabilities in market timing. Homebuilder ETFs pose higher downside risks than other indices, evident in their elevated Value at Risk (VaR) and Conditional Value at Risk (CVaR) values.
Journal Article
Towards New Strategies for Investing: Insights on Sustainable Exchange-Traded Funds (ETFs)
As investors increasingly incorporate environmental, social, and governance (ESG) factors into their decision-making, sustainable Exchange-Traded Funds (ETFs) have gained prominence in both investment portfolios and financial research. This study aims to provide a comprehensive analysis of the Sustainable ETF research landscape by utilizing scientometric and bibliometric methods with tools such as VOSviewer, Bibliometrix, and CiteSpace. Drawing from the Web of Science and Scopus databases, the study identifies key thematic areas, influential authors, and emerging trends. The findings highlight the conceptual evolution of Green ETFs, from early definitions focused on ESG-aligned investments to more complex instruments incorporating diversified screening criteria and advanced technologies like machine learning and artificial intelligence. Practical challenges such as regulatory inconsistencies, high implementation costs, and limited investor education are underscored as critical barriers to broader adoption. Future trends reveal the growing role of blockchain technology for ESG verification, crisis-specific ETF models, and the development of more inclusive screening strategies. Strategically, Green ETFs demonstrate resilience during market volatility and support sustainability-driven investment frameworks. The study provides valuable insights for investors, policymakers, and researchers, emphasizing Green ETFs’ role in driving sustainable finance and offering actionable guidance for optimizing ESG investment strategies.
Journal Article
Consistent and Efficient Dynamic Portfolio Replication with Many Factors
Factor investing involves choosing securities to construct portfolios with particular risk–return profiles. With the proliferation of benchmark-tracking exchange-traded funds (ETFs) virtually any risk–return profile can be reconstructed; the challenge is to find the right ETFs because the number of relevant ETFs is very large. This article proposes an innovative modification to the resampling procedure used in many popular machine learning methods for reducing the dimensionality of this problem. The proposed method allows selection of the specific ETFs used to replicate returns, taking the total costs of using the optimal portfolio to dynamically track returns into consideration. Existing variable selection algorithms are not designed to incorporate rebalancing costs, which are accumulated over time. The methodology is illustrated by replicating hedge fund returns with ETFs. The results show that, by selecting the right replication instruments in a way that is consistent with an investor’s economic utility instead of using purely statistical losses, the investor can save around 60 bps per year. TOPICS: Exchange-traded funds and applications, statistical methods, simulations, big data/machine learning Key Findings • A modified LASSO approach is developed for replication when variables are selected from many potential factors and transaction costs are accounted for in a dynamically consistent way. • By accounting for investor’s economic utility instead of purely statistical losses, the improved portfolio optimization procedure saves investors around 60 bps per year out of sample. • The new cross validation procedure is applicable for a wide range of problems in a time series context, when overfitting and transaction costs are major concerns of the model user.
Journal Article