Do oil shocks affect the green bond market?

This study examines the predictive power of oil shocks for the green bond markets. In line with this aim, we investigated the extent to which oil shocks could be used to accurately make in- and out-of-sample forecasts for green bond returns. Three striking findings emanated from our results: First, the three types of oil shock are reliable predictors for green bond indices. Second, the performances of the predictive models were consistent across the different forecasting horizons (i.e. H = 1 to H = 24). Third, our findings were sensitive to classifying the dataset into pre-COVID and COVID eras. For instance, the results confirmed that the predictive power of oil shocks declined during the crisis period. We also discuss some policy implications of this study's findings. © 2022 The Author(s)

The role of stock markets in the US, Europe, and China on oil prices before and after the COVID-19 announcement

This research investigates the asymmetric effects of the three major stock prices of the US, Europe, and China on WTI and Brent oil futures prices before and after the COVID-19 announcement by covering weekly data from January 2015 to April 2021. The results of the nonlinear autoregressive distributed lag (NARDL) model show that the US stock price has a significant positive effect in both models prior to the COVID-19 announcement but loses its effect on the WTI oil futures price after the COVID-19 announcement. Its impact on the Brent oil futures price remains after the COVID-19 announcement. The Europe stock price has a significant positive effect in all states. China stock price is not significant in the pre-COVID-19 period, but it has a significant effect after the COVID-19 announcement in both models. However, the only positive asymmetric changes in China stock price show a significant effect on the Brent oil futures price. Before COVID-19, the US stock price is the strongest, while the Europe stock price is the strongest after the COVID-19 announcement.

Dissecting hedge funds' strategies

This paper dissects the dynamics of the hedge fund industry with four financial markets, including the equity market, commodities, currencies, and debt market by employing a large number of assets from these markets. We employ four main representative hedge fund strategy indices, and a cap-weighted global index to estimate an asymmetric dynamic conditional correlation (ADCC) GJR-GARCH model using daily data from April 2003 to May 2021. We break down the performance, riskiness, investing style, volatility, dynamic correlations, and shock transmissions of each hedge fund strategy thoroughly. Further, the impact of commodity futures basis on hedge funds' return is analyzed. Comparing the dynamic correlations during the 2008 global financial crisis (GFC) with COVID-19 pandemic reveals changing patterns in hedge funds' investing styles. There are strong and pervasive shock spillovers from hedge fund industry to other financial markets, especially to futures commodities. An increase in the futures basis of several commodities drives up hedge funds' performance. While hedge fund industry underperforms compared to equity market and commodities, the risk-reward measures show that hedge funds are superior to other markets, and safer than the bond market. © 2022 Elsevier Inc.

Stochastic ordering of systemic risk in commodity markets

The contribution of commodity risks to the systemic risk is assessed in this paper through a novel approach that relies on the stochastic property of concordance ordering of CoVaR. Considering the period that spans from 2005 to 2022 and the VIX as the proxy for the stability of the financial system, we build the stochastic ordering of systemic risk for 35 commodities belonging to four sectors: Agriculture, Energy, Industrial Metals, and Precious Metals. The estimates of the ΔCoVaR signal that contagion effects from commodity markets to the financial system have been stronger during the years 2017–2019. Backtests validate CoVaR as a more resilient risk measure than the VaR, especially during periods of market turmoils. The stochastic ordering of CoVaR shows that severe losses (downside risk) in commodity markets tend to exacerbate systemic financial distress more than gains (upside risk). Commodity risks arising from WTI and EUA are threatening triggers for systemic risk. In contrast, the financial system is less vulnerable to a broader range of scenarios arising from fluctuations in Gold prices. As top contributors to the systemic risk, among the sectors we find Energy and Precious Metals with respect to upside risk and downside risk. The Covid-19 crisis has deeply amplified the systemic influence arising from the downside risk of WTI, Gasoline, and Natural Gas UK and has confirmed the safe-haven role of Gold. © 2022 Elsevier B.V.

Market efficiency and information flow between the crude palm oil and crude oil futures markets

This study analyzes the efficiency of the crude palm oil (CPO) futures market by conducting a variance ratio test and comparing it to the West Texas Intermediate (WTI) futures market. We discover that the weak-form efficient market hypothesis holds for both the CPO and WTI futures markets despite the significant difference in their liquidity. Using a scaling exponent, we investigate speculative trading activities and find that trading CPO futures in expectation of significant returns does not strongly involve a high level of risk unlike WTI futures. Our findings regarding market efficiency of the two futures markets are supported by the significant integration of the two with similar level of information flow from each market to the other. To explore the role of speculation in their market integration, we introduce a natural experimental setting using the coronavirus disease 2019 (COVID-19) pandemic, which caused a sudden decrease in the demand for fuel. The bidirectional information flow between the two markets is intensified after the COVID-19 pandemic due to lower level of speculation. The findings suggest that (i) stakeholders in the CPO market need to pay attention to the crude oil markets to anticipate its price changes, (ii) investors can use WTI futures as a hedging tool against CPO futures as long as there is mutual information flow, and (iii) regulators should carefully implement new CPO futures market policy, as either asymmetric changes in speculation or unbalanced regulation with the WTI futures market can create market distortion and regulatory arbitrage. © 2022 The Authors

The impact of the Russian-Ukrainian war on global financial markets

On February 24, 2022, Russia invaded the Ukraine. In this paper, we analyze the response of European and global stock markets alongside a representative sample of commodities. We compare the war response against the recent Covid-19 pandemic and the not-too-distant 2008 global financial crisis. Applying a Markov-switching HAR model on volatility proxies, estimates are made of synchronization, duration and intensity measures for each event. In broad terms, stock markets and commodities respond most rapidly to the Russian invasion;and post-invasion crisis intensity is noticeably smaller compared to both the Covid-19 and the GFC. Wheat and nickel are the most affected commodities due to the prominent exporter status of the two countries.

Impacts of COVID-19 on dynamic return and volatility spillovers between rare earth metals and renewable energy stock markets

We examine the time-frequency co-movements and return and volatility spillovers between the rare earths and six major renewable energy stocks. We employ the wavelet analysis and the spillover index methodology from January 1, 2018 to May 15, 2020. We report that the COVID-19-triggered significant increase in co-movements and spillovers in returns and volatility between the rare earths and renewable energy returns and volatility. The rare earths act as net recipient of both return and volatility spillovers, while the clean energy stocks are net transmitters of return and volatility spillovers before and during the COVID-19 crisis. The solar and wind stocks are net transmitters/receivers of spillovers before/during the pandemic. The remaining markets shift from net spillover receivers to transmitters or vice versa;evidencing the effects of the pandemic. Our results show that cross-market hedge strategies may have their efficiency impaired during the periods of crises implying a necessity of portfolio rebalancing. © 2022 The Authors

Can Investors Profit from Utilizing Technical Trading Rules during the COVID-19 Pandemic?

In the past, it was believed that investors may generate abnormal returns (AR) for trading stocks by employing technical trading rules. However, since the COVID-19 pandemic broke out, stock markets around the world seem to suffer a serious impact. Therefore, whether investors can beat the markets by applying technical trading rules during the period of COVID-19 pandemic becomes an important issue for market participants. The purpose of this study is to examine the profitability of trading stocks with the use of technical trading rules under the COVID-19 pandemic. By trading the constituent stocks of DJ 30 and NASDAQ 100, we find that almost all of the trading rules employed in this study fail to beat the market during the COVID-19 pandemic period, which is different from the results in 2019. The revealed findings of this study may shed light on that investors should adopt technical trading with care when stock markets are seriously affected by black swan events like COVID-19. © 2023 World Scientific Publishing Company.

The nexus between oil and airline stock returns: Does time frequency matter?

This paper investigates the relationship between oil and airline stock returns under different time frequencies. First, we propose an Autoregressive moving average model with mixed frequency exogenous variable to analyse the different impacts of oil on airline stock returns on daily, weekly, and monthly basis. We consistently find a negative oil-airline stock return nexus on a daily basis, but a positive relationship on a weekly basis. While the former supports the economic-based channel, the latter is in line with the market inertia channel. Our findings help explain mixed results reported in the literature. Further, our time frequency connectedness analysis shows that the economic-based channel dominates the market inertia channel since the connectedness is more pronounced in the short-run compared to the medium- and long-run. Our block connectedness results highlight that business models of airline firms can play a significant role in affecting the connectedness, in which the low-cost airlines are more sensitive to the oil price changes. It is worth noting that there are distinguished drivers of the oil-airline stock return nexus in different time frequencies. The drivers also vary between the Global Financial Crisis and the COVID-19 pandemic. Our results are consistent under a battery of robustness checks and deliver important implications to investors, portfolio managers, and executives of airline firms. © 2022 Elsevier B.V.

Efficiency of the financial markets during the COVID-19 crisis: Time-varying parameters of fractional stable dynamics

This paper investigates the impact of COVID-19 on financial markets. It focuses on the evolution of the market efficiency, using two efficiency indicators: the Hurst exponent and the memory parameter of a fractional Lévy-stable motion. The second approach combines, in the same model of dynamic, an alpha-stable distribution and a dependence structure between price returns. We provide a dynamic estimation method for the two efficiency indicators. This method introduces a free parameter, the discount factor, which we select so as to get the best alpha-stable density forecasts for observed price returns. The application to stock indices during the COVID-19 crisis shows a strong loss of efficiency for US indices. On the opposite, Asian and Australian indices seem less affected and the inefficiency of these markets during the COVID-19 crisis is even questionable. © 2022 Elsevier B.V.

Futures hedging in crude oil markets: A trade-off between risk and return

Risk and return are two fundamentals that have an impact on an investor's or hedger's investing choices. Based on the proposed synchronous movement intensity index, this paper aims to improve the hedging performance by adjusting the model-driven hedge ratio and realize the trade-off between return and risk in futures hedging. First, without loss of generality, we forecast crude oil spot and futures volatility using 10 GARCH-type models, including three linear models and seven nonlinear models, to obtain the ex-ante hedging ratio under the minimum variance framework. Then, we develop a novel and tractable method to identify the market state based on the index of consistency intensity, in which the index portrays the synchronous degree of stock price movements in the energy sector. Last but not least, we propose the hedge ratio adjustment criteria based on the identified state, and adjust the ratio driven by GARCH-type models of futures in accordance with the market state. Empirical results of crude oil futures markets indicate that the proposed state-dependent hedging model is superior to the commonly used models in terms of three criteria including mean of returns, variance, and ratio of mean to variance of returns for measuring hedging effect. We apply the DM test to make a statistical inference and discover that while the mean and the ratio of mean to variance of returns are increasing, the variance and hedging effectiveness of the hedged portfolio based on the modified methods are not significantly affected. Furthermore, the superiority of the proposed method is robust to different market conditions, including significant rising or falling trends, large basis, and COVID-19 pandemic. We also test the robustness of the proposed method with respect to the baseline model, quantile, and evaluation window. Overall, this paper provides a more realistic approach for crude oil risk managers to hedge crude oil price risk, some corresponding implications are also concluded. © 2022 Elsevier Ltd

Connectedness among El Niño-Southern Oscillation, carbon emission allowance, crude oil and renewable energy stock markets: Time- and frequency-domain evidence based on TVP-VAR model

Understanding the interactions among climate change, carbon emission allowance trading, crude oil and renewable energy stock markets, especially the role of climate change in this system is of great significance for policy makers, energy producers/consumers and relevant investors. The present paper aims to quantify the time-varying connectedness effects among the four factors by using the TVP-VAR based extensions of both time- and frequency-domain connectedness index measurements proposed by Antonakakis et al. (2020) and Ellington and Barunik (2021) [8,48]. The empirical results suggest that, firstly, the average total connectedness among climate change, carbon emission allowance trading, crude oil and renewable energy stock markets is not so strong for the heterogenous fundamentals underlying them. Nevertheless, the time-varying total connectedness fluctuates fiercely through May 2005 to September 2021, varying from about 8% to 30% and rocket to very high levels during the global subprime mortgage crisis and the COVID-19 pandemic. Furthermore, the total connectedness mainly centers on the short-term frequency, i.e., 1–3 months. Secondly, climate change is generally the leading information contributor among the four factors, although not particularly strong, and its leading role also performs mainly on the short-term frequency (1–3 months). Thirdly, renewable energy stock market and crude oil market show tight interactions between them and they are the two major bridges of information exchanges across various time frequencies (horizons) in this system. Finally, we confirm the evidence that the primary net connectedness contributor and receiver switch frequently across different time frequencies, implying that it is extremely essential for policy makers, energy producers/consumers and investors to make time-horizon-specific regulatory, production/purchasing or investment decisions when facing the uncertain effects of climate change on the interactions among carbon emission allowance, crude oil and renewable energy stock markets. © 2022 Elsevier Ltd

Evaluating economic recovery by measuring the COVID-19 spillover impact on business practices: evidence from Asian markets intermediaries

The COVID-19 outbreak significantly affected the global economy and energy markets. To mitigate the shock, maintain financial market stability, and encourage economic recovery, this study investigates the influence of post-COVID-19 on monetary policy transmission to business practices and financial market indicators for green economic recovery. We utilised 37 Asian markets' panel data from 1 January 2020, through 30 December 2020. The empirical findings demonstrate that the pandemic's emergence impeded monetary policy transmission, business practices, and financial markets. Our empirical contribution is to examine the size, sectoral allocation, and implementation options of three leading countries' (China, Japan, and Thailand) green recovery spending plans, which range significantly. However, this effect mainly affects the medium-and-long-term effects, and short-term spillover effects are primarily unaffected by Asian monetary policy uncertainty. Our findings have significant implications for green economic recovery among market players and regulators in the Asian market.

Jump forecasting in foreign exchange markets: A high-frequency analysis

Using tick data for 14 emerging and developed market currencies covering the period from January 2018 until April 2021, we first detect jumps by Lee and Mykland methodology then apply various machine learning algorithms to forecast out of sample jump occurrences and their direction. Our results show that the arrival and the direction of intraday jumps in the foreign exchange market can be predicted with these algorithms combined with liquidity metrics and technical indicators, even for the Covid pandemic period where volatility in the foreign exchange market is very high. Among all the methods considered, multilayer perceptron has the highest average accuracy for jump prediction overall, followed by support vector machine and random forest methodologies with slightly less average accuracy results. Results are robust to alternative sampling schemes. Accordingly, central bankers can adjust liquidity injection timing with these jump prediction models in the foreign exchange markets where they can try to minimize jump strength if not completely eliminate its occurrence. For investors, having information regarding jump occurrence timings gives an opportunity to hedge against foreign exchange risks more efficiently. © 2023 John Wiley & Sons Ltd.

Cryptocurrencies Are Becoming Part of the World Global Financial Market.

In this study the cross-correlations between the cryptocurrency market represented by the two most liquid and highest-capitalized cryptocurrencies: bitcoin and ethereum, on the one side, and the instruments representing the traditional financial markets: stock indices, Forex, commodities, on the other side, are measured in the period: January 2020-October 2022. Our purpose is to address the question whether the cryptocurrency market still preserves its autonomy with respect to the traditional financial markets or it has already aligned with them in expense of its independence. We are motivated by the fact that some previous related studies gave mixed results. By calculating the q-dependent detrended cross-correlation coefficient based on the high frequency 10 s data in the rolling window, the dependence on various time scales, different fluctuation magnitudes, and different market periods are examined. There is a strong indication that the dynamics of the bitcoin and ethereum price changes since the March 2020 COVID-19 panic is no longer independent. Instead, it is related to the dynamics of the traditional financial markets, which is especially evident now in 2022, when the bitcoin and ethereum coupling to the US tech stocks is observed during the market bear phase. It is also worth emphasizing that the cryptocurrencies have begun to react to the economic data such as the Consumer Price Index readings in a similar way as traditional instruments. Such a spontaneous coupling of the so far independent degrees of freedom can be interpreted as a kind of phase transition that resembles the collective phenomena typical for the complex systems. Our results indicate that the cryptocurrencies cannot be considered as a safe haven for the financial investments.

INDONESIA'S FINANCIAL STRESS EVENTS AND MACROECONOMIC DYNAMICS

In this study, we use a Markov-Switching Bayesian Vector AutoRegression model to investigate the episodic relationship between financial stress and the key macroeconomic variables in the case of Indonesia. We find different nature of relationships among Indonesia's real sector variables (household consumption expenditure and consumer price index), financial sector variables (interbank money market rate) and the policy variable (broad money supply during the times of high and low financial stress). Regime changes occurred on several occasions, including during the 2008 global financial crisis period and at the beginning of the COVID-19 pandemic. © 2022 Authors. All rights reserved.

The Influence of The COVID-19 Pandemics in Indonesia On Predicting Economic Sectors

Several studies have tried to prove the link between the economic sectors in Indonesia with the COVID-19 pandemic. However, research has yet to observe the influence of the COVID-19 pandemic on the predicted performance of regression models. This study proposes the development of previous research following the impact of the COVID-19 pandemic on machine learning performances in predicting economic sectors in Indonesia. The economic sectors mentioned include the exchange rate, CPI, and stock price. The proposed methods for comparison are decision tree (DST) and random forest (RF). Comparison of prediction performance with legacy uses root mean squared error (RMSE), mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Test results show that the RF regression model has superior performance compared to DST with the best MSE, RMSE, MAPE, MAE, and r2 value of 0.010, 0.102, 0.64%, 0.100, and 0.89, respectively. Using the T-Test, we prove that the COVID-19 pandemic does not significantly affect machine learning predictions on the exchange rate but significantly affects machine learning predictions on CPI and stock prices. © 2022 IEEE.

Research on the Influence of Securities Markets on Cross-Border Capital Flows Based on the DCC-MVGARCH Model

In the study of the impact of cross-border capital flows, most scholars at home and abroad focus on the method of linear time series mainly based on the vector autoregressive model (VAR), ignoring the volatility of variables in time series. In order to make up for the deficiency, the dynamic conditional correlation-generalized autoregressive conditional heteroskedasticity (DCC-GARCH) model can be used to study the nonlinear time-varying correlation between variables. With the help of Eviews12 software and the DCC-MVGARCH model, this paper studies the impact of securities markets on cross-border capital flows in China from domestic and foreign perspectives in the context of two financial crises and COVID-19. The results indicate that financial crises affect the correlation between the securities markets and cross-border capital flows. China's stock market is positively correlated with short-term capital flows and negatively correlated with long-term capital flows. Its booming bond market promotes short-term capital flows but fails to affect the long-term capital flows, and China's short-term capital flows are increasingly linked to the volatility of foreign stock markets. Therefore, it is necessary to improve the mechanism for better monitoring and analyzing cross-border capital flows, promote further development of financial supervision, and guide market players to face the securities market rationally. © 2023 SPIE.

Commodity supply risk assessment of China's copper industrial chain: The perspective of trade network

With the rapid development of China's new energy industry, the consumption demand for copper resources is increasing. As a key raw material, copper resources are becoming increasingly important. Taking the demand for copper commodities in China's new energy development as the research background and the international trade environment and pattern of copper supply as the research perspective, this paper makes an overall assessment of the commodity supply risk of China's copper industrial chain from 2010 to 2021 using the complex network and the newly established three-dimensional risk assessment model and finally reaches the following conclusions. The supply risk of commodities in China's copper industrial chain has been rising continuously since 2019 after experiencing fluctuating development in the early stage and a continuous decline in recent years, and there may be a trend of continuing to rise. The supply risk of China's copper industrial chain was gradually reduced from upstream to midstream and downstream, and the supply risk of copper smelting was more severe. The disruption potential risk of China's copper industrial chain was relatively low, and the international import market structure of copper commodities was relatively reasonable. The supply risk characteristics of each link in China's copper industrial chain were different. Due to the influence of import dependence, the copper mining industry had a high risk of trade exposure. However, the smelting and copper processing industries had certain limitations in production management, operation management and technology research and development, and their ability to withstand risks was weak. In addition, the impact of the domestic COVID-19 epidemic ha caused a high industrial chain vulnerability risk. © 2023 Elsevier Ltd

COVID-19 Pandemic & Financial Market Volatility, Evidence from GARCH Models

Across the globe, COVID-19 has disrupted the financial markets, making them more volatile. Thus, this paper examines the market volatility and asymmetric behavior of Bitcoin, EUR, S&P 500 index, Gold, Crude Oil, and Sugar during the COVID-19 pandemic. We applied the GARCH (1, 1), GJR-GARCH (1, 1), and EGARCH (1, 1) econometric models on the daily time series returns data ranging from 27 November 2018 to 15 June 2021. The empirical findings show a high level of volatility persistence in all the financial markets during the COVID-19 pandemic. Moreover, the Crude Oil and S&P 500 index shows significant positive asymmetric behavior during the pandemic. Apart from this, the results also reveal that EGARCH is the most appropriate model to capture the volatilities of the financial markets before the COVID-19 pandemic, whereas during the COVID-19 period and for the whole period, each GARCH family evenly models the volatile behavior of the six financial markets. This study provides financial investors and policymakers with useful insight into adopting effective strategies for constructing portfolios during crises in the future.