Normal Market Conditions Research Articles

Quantile Connectedness Amongst Green Assets Amid COVID-19 and Russia–Ukraine Tussle

With the advent of greening the global economy and the introduction of green financial assets, this study examines the connectedness and spillover effect of green assets using a QVAR approach focusing on the average connectedness and connectedness under extreme market conditions. The time of the study captures the crucial global incidents of COVID-19 and Russia–Ukraine war to investigate the effect of major incidents on the connectedness of green assets. The results of the QVAR analysis reveal that green assets are moderately connected under normal market conditions; however, their connection is strengthened under extreme market conditions. IOTA and SP Green Bonds are the net receivers of shocks from other assets, and SP Green Bonds are connected to green energy indices and green cryptocurrencies during turbulent markets. Since green cryptocurrencies are closely connected, a lower portion of them should be added to portfolios, whereas SP Green Bonds qualify as a good diversifying agent in a portfolio. The study has significant implications for market participants, investors, and policymakers.

The Definition of ‘Covered Entities’ under the GPA: The General Scope and Escape Conditions

Abstract The WTO Government Procurement Agreement (GPA) does not legally define what entities should be covered by the Agreement. However, its member Parties list their ‘covered entities’ in a series of schedules. The list approach has complicated accession negotiations and discourages Parties from providing a ‘wider’ range of entity coverage. Moreover, the list approach raises some tensions and a lack of legal certainty, especially concerning those that are not strictly ‘government entities’, such as State-owned enterprises (SOEs). This problem is exacerbated in the case of modern SOEs in developing countries, many of which can bear both public and private features. Given these conditions, the author proposes a definition of ‘covered entities’ to facilitate accession negotiations and the future expansion of the GPA. The proposal is based on a comparative study of the GPA and the EU public procurement regulations. It develops a framework by which all publicly controlled entities are presumably covered by the GPA. Nevertheless, Parties can rebut GPA obligations by proving that an entity competes with other commercial entities under normal market conditions.

Dependence of green energy markets on big data and other fourth industrial revolution technologies

This paper analyzes the dependence and connectedness among fourth-industrial revolution technology markets (including big data and artificial intelligence, blockchain, and financial technology) and global and regional (US, Europe, and Asia) green energy markets. In particular, we consider the dynamic dependence among these markets in terms of both returns and volatility across different market conditions and investment horizons using the cross-spectral coherence and Quantile-VAR connectedness approach. Three main results emerge from our analysis. First, the return dependence is relatively stronger than volatility dependence and is stronger across most time scales among the technology markets and the European and Asian regional green energy indexes. Second, the return and volatility connectedness is stronger during extreme than normal market conditions. Unless under bullish market times, volatility connectedness appears smaller than return connectedness, implying that market volatility risks spread less forcefully among these markets than return risks under normal and bearish market periods. Third, geopolitical risks, business environment, economic policy, fixed-income, and oil and gold markets’ uncertainties are significant predictors of the degree of return and volatility connectedness. Overall, our findings offer crucial insights for short- and long-term investors interested in portfolios with modern technology and green assets. They also emphasize the roles of market and macroeconomic factors in shock propagation and their implications for low-carbon transition.

Quantile connectedness among fintech, carbon future, and energy markets: Implications for hedging and investment strategies

This study proposes a novel framework to explore the quantile connectedness among fintech, carbon future, and energy markets with implications for hedging and investment strategies. We aim to construct portfolio strategies for extreme market conditions based on the results of quantile connectedness to address the complex price fluctuations in the energy markets. Using the daily data from January 4, 2017, to March 6, 2023, we find that (i) carbon future and crude oil are the net receivers, while fintech and clean energy are the net contributors. However, the net spillover direction of fintech, carbon futures, and energy markets change frequently under extreme market conditions; (ii) the COVID-19 pandemic and Russia-Ukraine conflict significantly enhance connectedness across all quantile levels; (iii) the sample period is dominated by the lower quantile connectedness, indicating that investors react more strongly to bad news. Furthermore, the result of portfolios shows that, compared to normal market conditions, the dynamic investment weights of minimum connectedness (MCOP) exhibit greater volatility under extreme market conditions. This highlights the differences in portfolio adjustments across different market conditions. In addition, the result shows that the minimum connectedness and minimum correlation portfolio have a greater cumulative return than the minimum variance portfolio. The analysis based on the Sharpe ratio shows that in extreme market conditions, carbon futures and fintech can serve as effective hedging tools for the energy markets. Robustness tests further confirmed our conclusions. Our results have positive implications for decision-makers and investors about risk management and diversified portfolio selection.

Connectedness between artificial intelligence, clean energy, and conventional energy markets: Fresh findings from CQ and WLMC techniques

In line with achieving the objectives of COP27 and SDG7, this paper examines the interdependence of the Artificial Intelligence market, clean energy, and conventional energy markets from 19th December 2017 to 5th May 2023 by using Cross-Quantilogram (CQ) and Wavelet Locale Multiple correlations (WLMC) techniques. Heatmaps of CQ show a bidirectional relationship between the AI market and clean energy at lag one with negative cross-quantile dependence evident throughout most quantiles, especially in normal market conditions. It also indicates a positive relationship between AI return rates and the clean energy market, but only when both datasets are in the same extreme quantiles (10th and 90th). Additionally, WMLC results reveal that time, scale, and investment horizons influence the interaction between AI and clean and non-clean energy industries. A considerable positive association exists between the AI market and traditional energy markets, ranging from 0.6 to 0.8. However, during the pandemic, this dependency turned negative, and it has since been minor, with an uptick in co-movement during Russia – Ukraine conflict. Several policy implications are suggested for the clean energy and conventional energy markets in line with AI.

US Dollar Exchange Rate Elasticity of Gold Returns at Different Federal Fund Rate Zones

We examine the relationship between gold prices and the U.S. dollar exchange rate, arguing that their interactions are state-dependent and asymmetric under different market conditions. State dependency hinges on different short-term interest rate zones. To prove this point, we determine three distinct levels or zones of the effective federal funds rate using SETAR(2,p) tests. Subsequently, we perform conditional least square estimations of log changes in gold prices as a function of log changes in the nominal broad U.S. dollar exchange rate index for each of the obtained zones. Their relationship is consistently inverse, suggesting that gold and the U.S. dollar are risk-hedging substitutes for normal market periods. This also implies that gold is a safe-haven asset against the U.S. dollar exchange rate risk against a broad range of currencies. The substitution is weaker in the low-interest rate zone, more robust in the intermediate zone, and very pronounced in the high zone. We also perform a Markov switching test on the double-log function of gold prices and the exchange rate. The tests show a pronounced inverse relationship, i.e., substitution between assets, at normal market conditions. The relationship becomes significantly positive during episodes of financial distress, indicating complementarity between gold and U.S. dollar assets.

Metaverse and financial markets: A quantile-time-frequency connectedness analysis

Amidst increasing interest from investors and scholars in the emerging Metaverse market, this paper marks a pioneering attempt to investigate the volatility connections between the Metaverse stock index and traditional financial markets such as Gold, Crude Oil, the Volatility Index, Bitcoin, and the Nasdaq. Utilizing a novel Quantile Vector Autoregressive (QVAR) model, the study assesses the transmission of shocks between the Metaverse market and its counterparts during bearish, normal, and bullish market conditions. The results highlight a significant increase in connectivity during extreme conditions compared to median levels. Notably, the Nasdaq emerges as a principal volatility transmitter to the Metaverse index, while Bitcoin shows minimal influence, suggesting that technological innovations, rather than cryptocurrencies, predominantly drive the Metaverse market. This investigation provides valuable insights for investors and policymakers, considering the nascent stage of Metaverse-related empirical research.

Frequency domain cross-quantile coherency and connectedness network of exchange rates: Evidence from ASEAN+3 countries

This study examines the frequency domain connectedness and synchronization between the exchange rates of Association of Southeast Asian Nations (ASEAN) member countries and those of China, Japan, and South Korea across quantile levels. We propose a quantile cross-spectrum of exchange rates to establish the coherency of connectedness and synchronization measurements. Our empirical results are as follows: First, the return connectedness between the exchange rates is heterogeneous, being stronger in the long run than in the short run and more pronounced under normal market conditions than under extreme market conditions. Second, the dynamic return connectedness among the exchange rates follows a similar trend in the monthly and yearly cycles. Third, exchange rate returns and volatility exhibit long-term synchronization. However, short-term heterogeneity persists across market conditions and investment horizons. Overall, these findings offer valuable insights for monetary authorities in their efforts to maintain exchange rate stability and for investors in making informed portfolio decisions.

Do climate change risks affect the systemic risk between the stocks of clean energy, electric vehicles, and critical minerals? Analysis under changing market conditions

This paper analyzes the impact of climate change risks—specifically from natural disasters, global warming, international summits, and U.S. climate policy—on the return connectedness (systemic risk) of a network consisting of the stocks of clean energy, electric vehicles, and critical minerals in bear, bull, and normal market conditions. Employing a quantile vector autoregression (QVAR) approach, we find significant temporal variations in the total connectedness index, with notable spikes during the COVID-19 pandemic and the Russia-Ukraine war. Total connectedness is higher but less variable under bear and bull market conditions. Concerns about global warming has a positive and significant impact on systemic risk during bear and normal market conditions while international summits have a negative impact during normal market conditions. However, the effects of these climate change risks are small in magnitude. Economic policy uncertainty and stock market volatility have the largest positive impacts on systemic risk under most market conditions. Our results reveal a nonlinear (inverted U-shaped) relationship between variable importance and systemic risk quantile, showing that the impact on connectedness is largest in magnitude under normal market conditions.

Analysing COVID-19′s impact: Gold, oil, and stock markets in African oil-exporting economies

This article employs quantile regressions to investigate the link between oil prices, gold prices, and stock market returns in five African oil-exporting nations: Nigeria, Ghana, Egypt, Algeria, and Tunisia. The analysis utilises daily data spanning from January 1, 2017, through September 30, 2021. Before the onset of COVID-19, oil played a pivotal role as a hedge in Tunisia, Egypt, Algeria, and Ghana, providing stability in both normal and bullish market conditions. Furthermore, it functioned as a diversifier in Egypt and Ghana during bearish market phases. However, as the pandemic unfolded, oil took on the characteristics of a safe haven in Egypt, Ghana, Nigeria, Algeria, and Tunisia, particularly notable during bearish and normal market conditions. Gold, prior to the pandemic, served as a hedge in Egypt, Ghana, Nigeria, and Tunisia, while also acting as a diversifier across both bearish and bullish market conditions in these economies. Conversely, during the pandemic, gold predominantly functioned as a diversifier in most oil-exporting African economies, with significant hedge properties observed specifically in Algeria and Ghana. The practical implications of these findings are significant for various stakeholders, including oil exploration and production firms, businesses in the transport and hospitality sectors, policymakers, and investors with stocks sensitive to oil and gold prices.

Do clean energy stocks diversify the risk of FinTech stocks? Connectedness and portfolio implications

The FinTech sector is growing rapidly, prompting a need to explore effective investment diversification strategies for stocks in this sector. The existing literature has identified the benefits of using clean energy stocks to diversify stock portfolios and the purpose of this research is to estimate how useful clean energy stocks are for diversifying an investment in FinTech stocks. This study uses a QVAR model to estimate the dynamic return connectedness between FinTech stocks and clean energy stocks for the period September 2016 to April 2024. Total connectedness is time varying and is higher in the tails than at the median. The onset of the COVID-19 pandemic had a large but short-term impact on connectedness. Under normal market conditions, systemic risk increases by 3.5% per year. FinTech is a net transmitter of shocks to nuclear energy but is mostly unaffected by shocks from wind, solar, and nuclear energy stocks illustrating the diversification benefits of these sub-sectors. Portfolio analysis shows that adding solar, wind, and nuclear energy to a portfolio with FinTech can produce higher risk adjusted returns and lower drawdown than an investment solely in FinTech stocks. These results are robust across various portfolio rebalancing frequencies (daily, weekly, monthly). For example, a minimum connectedness portfolio rebalanced daily has an average annual return of 11% and a Sharpe ratio of 0.37. These values are higher than their respective values for an investment solely in FinTech stocks (5.4%, 0.11). Thus, clean energy stocks do provide diversification benefits for investments in FinTech stocks.

Is style drift informative? Evidence from mutual funds in China

In this study, we investigate whether the style drift of portfolio managers is informative. We establish that style drift is based on public information rather than the imitation of peers. Fund managers are found to adjust their portfolios to align with market expectations, particularly in normal market conditions. However, we observe a pronounced shift towards irrational herd behavior during market turbulence and style rotation periods. While style drift can offer benefits in contemporary periods, such as positive risk-adjusted returns, the funds that exhibit excessive drift can hardly beat their peers in subsequent periods. More notably, sophisticated investors tend to abandon funds with more considerable style drift in favor of those adhering to a consistent investment strategy. Our findings indicate that while style drift may be a strategic response to market conditions, its advantages are transient and often unaligned with the preferences of sophisticated investors.

Quantile connectedness and network among Green bonds, Renewable Energy, and G7 sustainability markets

This paper examines the quantile spillovers and interconnectedness between green bonds, renewable energy, and the G7 sustainability markets under extreme market conditions. It reaches beyond the widely employed studies based in the mean values of variables by resorting to the advanced quantile vector auto-regression methodology. The results show that the overall connectedness is stronger during extreme, either bearish or bullish market situations, than during normal market conditions. Furthermore, we show that all sustainability markets, except Japan, are the net transmitters of shocks, while all the green bonds are the net receivers. Moreover, we document that spillovers vary significantly over time and that extreme market conditions reduce the net spillover influences. The relative tail dependence analysis shows that the connectedness at the lower quantile relatively dominates the connectedness at the upper quantile. Finally, the total spillovers are higher at the extremes than at the median quantile, whereas the periods of major crises are associated with unusually high connectedness, as consistent with the literature on contagion effects.

Are clean energy markets hedges for stock markets? A tail quantile connectedness regression

Acknowledging the long-term potential of alternative energy sources, this paper examines the quantile frequency connectedness between clean energy markets and international stock markets, with implications related to hedging effectiveness. The main results point out that spillovers run from the US, the EU, the UK, and the Renewable Energy and Clean Technology Index to Japan and the Global Clean Energy Index. Furthermore, while the transmissions are concentrated in the short run during normal (0.5) and bull market (0.95) conditions, they extend to intermediate and long-term amid busting market (0,05 quantile) states, signifying a long-lasting impact that cannot be absorbed in the short run. Notably, clean energy index roles in information transmissions range from a net sender (Renewable Energy and Clean Technology Index), isolated (Green Bond Index), and a net receiver (Global Clean Energy Index). From a multivariant portfolio design perspective, we notice that a substantial weight should be allocated to clean energy assets, WTI, and CSI300 when compared with the rest of the financial markets. Moreover, the low (high) volatility regime yields lower (higher) weights than the ones reported in the mean state, but the results remain largely similar. Bivariant portfolio weights show that GB should have substantial weight when paired with all assets.

Dynamic quantile connectedness between oil and stock markets: The impact of the interest rate

This paper explores the heterogeneous and dynamic connectedness between the oil and stock markets of emerging economies under various market conditions by introducing a novel quantile regression TVP-VAR network method. Moreover, a semiparametric model is used to analyze the impact of interest rates on the connectedness. The results show that (1) the total connectedness between the oil and stock markets of emerging economies in bull and bear markets is significantly larger than under normal market conditions. Moreover, the total connectedness is time-varying and crisis-sensitive in all market scenarios. (2) The total connectedness has asymmetric characteristics in bull and bear markets. The net information spillover from oil markets to stock markets of emerging economies shows heterogeneity under different market backgrounds. (3) The impact of interest rates on the total connectedness exhibits a “U-shaped” curve pattern for all market statuses. This study can serve as a reference for regulators aiming to formulate monetary policies for different market environments, especially extreme markets, and for investors aiming to adjust their investment strategies and optimize their investment portfolios according to market conditions.

Global macroeconomic factors and the connectedness among NFTs and (un)conventional assets

This paper examines return and volatility connectedness among Non-Fungible Tokens (NFTs) and (un)conventional financial assets across various market conditions using a Quantile-VAR connectedness technique. It also explores the predictive powers of major global macroeconomic and geopolitical indicators on both connectedness across these market conditions. First, we find that return and volatility connectedness vary across market conditions, with higher levels during extreme events. Except during bullish periods, return connectedness dominates volatility connectedness. Second, NFTs are decoupled from both (un)conventional assets during normal market condition but it is a net return shocks receiver except under bullish market period, where it is a net transmitter. However, it is a net volatility shocks receiver irrespective of the market situation. Lastly, geopolitical risks, business condition and economic policy uncertainty are important predictors of return and volatility connectedness, although the strength and direction are heterogeneous. We discuss the policy implications of these findings.

Attention to climate change and eco-friendly financial-asset prices: A quantile ARDL approach

In recent years, heightened global attention to climate change threats has spurred a rising tide of investors towards eco-conscious investment alternatives, reflecting a persistent urgency to align financial activities with sustainability objectives. This paper examines the potential role of people's attention to climate change concerns in explaining the price dynamics of various environmentally friendly financial assets, including solar energy stocks, clean energy stocks, sustainable stocks, low-carbon footprint stocks, and green bonds. The sample period is from January 03, 2014 to March 08, 2024. The principal component analysis is used to construct a proxy indicator for climate change-related attention, while the quantile ARDL approach is adopted to assess the relevance of this attention not only across short- and long-term horizons but also amidst normal, bearish, and bullish market conditions. The results reveal that eco-friendly financial assets are sensitive to public focus on climate change in the short run. The observed sensitivity, nonetheless, tends to fade away over the long term. For most assets examined, the magnitude of this sensitivity is not symmetric across quantiles; rather, it appears to vary depending on the prevailing market states. Our findings bear important implications for both investors and policymakers.

Shelter in Uncertainty: Evaluating Gold and Bitcoin as Safe Havens Against G7 Stock Market Indices During Global Crises

This paper investigates the hedging and safe haven capacity of gold and Bitcoin against the G7 stock market indices during the COVID-19 pandemic, the Russia-Ukraine military conflict, and the Silicon Valley Bank collapse. Using a novel Quantile-VAR connectedness approach, the results show that, at the median quantile, both gold and Bitcoin act as effective hedges during normal market conditions and strong safe-haven assets during the three crises. Gold emerges as the most prominent safe haven asset, outperforming Bitcoin, especially during the war and the SVB collapse. Among the G7 stock market indices, the Japanese and the American stocks may be used as risk diversifiers during crises. As for the rest of the G7 stocks, they are regarded as “risk-on” investments. Next, we assessed the robustness of our results at various quantiles. We found them to be generally consistent with the outcomes obtained at the median quantile, with one exception related to the S&P500.The results show that the repercussions of the COVID-19 pandemic and the war are much stronger than the American banking crisis.

Comparative Analysis of Spillover Effects in the Global Stock Market under Normal and Extreme Market Conditions

Using the volatility spillover index method based on the quantile vector autoregression (QVAR) model, this paper systematically examines structural changes and corresponding spillover effects within 20 major stock markets under both extreme and normal market conditions, using data spanning from January 2005 to January 2023. The results show that, compared to the traditional volatility spillover index method, which focuses mainly on average spillover effects, the QVAR model-based spillover index better captures spillover effects under extreme and various market conditions among global stock markets. The connections between stock markets are closer in extreme market conditions. The total spillover index of major global stock markets significantly increases in extreme conditions compared to normal conditions. In extreme market conditions, inflow indices show varying degrees of increase, with emerging economy stock markets displaying more significant increases. The outflow indices exhibit heterogeneity; emerging economies show consistent increases, while developed economies show mixed changes.

Identifying systemic risk drivers of FinTech and traditional financial institutions: machine learning-based prediction and interpretation

ABSTRACT We study systemic risk drivers of FinTech and traditional financial institutions under normal and extreme market conditions. We use machine learning (ML) techniques (i.e. random forest and gradient boosted regression trees) to evaluate the role of macroeconomic variables, firm characteristics, and network topologies as systemic risk drivers and perform the ML-based interpretation by Shapley individual and interaction values. We find that (i) the feature importance in driving systemic risk depends on market conditions; namely, market volatility (MVOL), individual stock volatility (IVOL), and market capitalization (MC) are positive drivers of systemic risk under extreme (downside and upside) market conditions, while under normal market conditions, institutions with high price-earnings ratio, large MC, and low IVOL play an essential role in stabilizing markets; (ii) macroeconomic variables are the most important extreme systemic risk drivers, while firm characteristics are more important under normal market conditions; and (iii) the interaction between IVOL and MC or MVOL is the significant source of extreme systemic risk, and MC is the most crucial interaction attribute under normal market conditions. The interactions between macroeconomic variables are the most prominent in systemic risk under different market conditions.