Volatility Spillovers between Commodity and Financial Markets

The paper analyzes price volatility spillovers between commodity and financial markets order to investigate the interconnectedness and market integration and their potential in port-risk diversification. The paper analyzes gold and silver prices, oil prices, and the exchange of the Euro and British pound using the Diebold-Yilmaz spillover index methodology for-frequency weekly data from 1988 to 2020. The total spillovers between commodities and exchange rates were found to be 25.7% and the volatility spillover index during the analyzed period mostly ranged between 25% and 50% with extremes during the global financial crisis and during COVID-19 pandemic. This indicates a strong integration of commodity and financial markets, especially in crisis periods. Also, the results of the work suggest that silver price movements are affected by spillovers from other markets and therefore silver can be used to diversify risks. contribution of the paper to the existing literature is as follows: Firstly, the analysis of transmis-processes showed significant volatility spillovers between commodity markets and exchange indicating the existence of integration between different markets. Furthermore, a long period time is analyzed and the dynamic analysis shows intensified volatility spillovers in global crises periods. Secondly, the results of the analysis can help professional forecasters in forecasting and financial analysts to provide a comprehensive investment analysis. Managers and investors can thus design optimal protection instruments against unwanted movements in the financial and commod-markets. Investors benefit from portfolio diversification, and the information content obtained volatility spillover analysis can be used to assess potential determinants of future risk-adjusted returns, which would help them make investment decisions.

Trade-based money laundering and informal remittance services: implications on the sustainability of the balance of payments of a small open economy

PurposeThis study aims to discuss the consequences of trade-based money laundering (TBML) and informal remittance services on the sustainability of the position of balance of payments and net foreign assets of a small open economy.Design/methodology/approachThis paper uses a case study design using facts related to TBML and informal remittance services on the balance of payment and net foreign assets of Sri Lanka.FindingsThe contextual analysis reveals that the growth of the informal economy promotes informal remittance services in Sri Lanka. The policy decision to peg local currency to US dollars as a result of a shortage of foreign exchange had forced people to use informal channels for different purposes. The unclear and vague customer due diligence process of the anti-money laundering and countering the financing of terrorism (AML/CFT) regime also has forced people to use informal remittance services. Criminals especially drug traffickers have grabbed the promoted informal remittance services to transfer proceeds from Sri Lanka to overseas drug suppliers. On the other hand, systematic deficiencies in monitoring and regulation of movement of fund transfers and merchandise across borders provide opportunities for criminals to use different TBML techniques to transfer funds. These limitations force policymakers and regulators to think of developing a comprehensive payment ecosystem to prevent money laundering and terrorist financing. Therefore, the global initiative is required to move towards a payment ecosystem from a recommendation-based AML/CFT regime to reduce global crimes.Research limitations/implicationsThis study was designed to discuss the implications of TBML and informal remittance services on the balance of payments and net foreign assets in a small open economy. The structure and size of the economy, the strength of the overall economy and the AML/CFT regime will play an important role in controlling criminal activities and combating money laundering of an economy;hence, the impact of TBML and informal remittance services will vary accordingly across the countriesOriginality/valueThis paper is an original work done by the authors, which discusses the implications of TBML and informal remittance services on the balance of payments and net foreign assets of an emerging market context.

BIST Tourism and BIST Electricity Index Relationship

In this study, which is based on daily data, the relationship between BIST electricity index and BIST tourism index was measured between 2012:M9 – 2022:M9 periods. The aim of the study is to measure the relationship between BIST electricity index and BIST tourism index. VAR Granger causality test was applied to determine whether there is any causal relationship between the variables. It has been determined as a result of the analysis that the BIST electricity index has no effect on the BIST tourism index. Two-way ineffectiveness was determined among the variables. In addition, it was obtained as a result of the analysis that the applied correlation relationship was weak between these variables. The results obtained from the study are important in terms of measuring the effects among BIST indices.

Economic Policy Uncertainty and Emerging Stock Market Volatility

This research examines the effect of economic policy uncertainty (EPU) indices on Pakistan's stock market volatility. Particularly, we examine the impact of the economic policy uncertainty index for Pakistan and bilateral global trading partner countries, the US, China, and the UK. We employ the GARCH-MIDAS model and combination forecast approach to evaluate the performance of economic uncertainty indices. The empirical findings show that the US economic policy uncertainty index is a more powerful predictor of Pakistan stock market volatility. In addition, the EPU index for the UK also provides valuable information for equity market volatility prediction. Surprisingly, Pakistan and China EPU indices have no significant predictive information for volatility forecasting during the sample period. Lastly, we find evidence of all uncertainty indices during economic upheaval from the COVID-19 pandemic. We obtained identical results even during the Covid-19. Our findings are robust in various evaluation methods, like MCS tests and other forecasting windows.

Do cryptocurrencies integrate with the indices of equity, sustainability, clean energy, and crude oil? A wavelet coherency approach

The paper examines market co-movement between pairs of financial assets in the time-frequency domain. Recent finance literature confirms the integration of cryptocurrencies and financial assets, which may bring more investments with the possibility of surplus liquidity in the cryptocurrency segment, leading to financial instability. The novelty of this paper is examining the integration of cryptocurrencies and the indices of equity, sustainability, renewable energy, and crude oil for the daily observations from 2015 to 2021 by using the wavelet coherency method. The empirical results signify no integration in the short-term scales and grow stronger in the medium-term scales, especially during the COVID-19 period, and further exhibit weaker heterogeneous associations in the long-term scales. However, the sustainability, clean energy indices follow similar dynamics of the equity market and crypto pairs. In contrast, the global crude oil index showcases the minor integration with cryptocurrencies compared with other traditional asset classes. Hence, the cryptocurrency market fails to confirm the safe haven features, especially during the COVID-19 periods (Medium-term), which facilitate the domestic and international investors expecting to hedge their price risk in equity markets using cryptocurrencies may have to look for short-term. The lead-lag heterogeneous effects of the asset-pairs may pave arbitrage opportunities for investors.

Hedging potentials of green investments against climate and oil market risks

PurposeThis study aims to examine the ability of clean energy stocks to provide cover for investors against market risks related to climate change and disturbances in the oil market.Design/methodology/approachThe study adopts the feasible quasi generalized least squares technique to estimate a predictive model based on Westerlund and Narayan's (2015) approach to evaluating the hedging effectiveness of clean energy stocks. The out-of-sample forecast evaluations of the oil risk-based and climate risk-based clean energy predictive models are explored using Clark and West's model (2007) and a modified Diebold & Mariano forecast evaluation test for nested and non-nested models, respectively.FindingsThe study finds ample evidence that clean energy stocks may hedge against oil market risks. This result is robust to alternative measures of oil risk and holds when applied to data from the COVID-19 pandemic. In contrast, the hedging effectiveness of clean energy against climate risks is limited to 4 of the 6 clean energy indices and restricted to climate risk measured with climate policy uncertainty.Originality/valueThe study contributes to the literature by providing extensive analysis of hedging effectiveness of several clean energy indices (global, the United States (US), Europe and Asia) and sectoral clean energy indices (solar and wind) against oil market and climate risks using various measures of oil risk (WTI (West Texas intermediate) and Brent volatility) and climate risk (climate policy uncertainty and energy and environmental regulation) as predictors. It also conducts forecast evaluations of the clean energy predictive models for nested and non-nested models.

Integration and risk transmission in the market for crude oil: New evidence from a time-varying parameter frequency connectedness approach

In this study, we introduce a novel time-varying parameter vector autoregressive frequency connectedness approach to obtain refined measures of the frequency transmission mechanism and dynamic integration among six well-established crude oil benchmarks. The period of investigation ranges from May 14th, 1996 to December 3rd, 2020 and focuses on the differences between short-term (1–5 days) and long-term (6–100 days) crude oil volatility connectedness. Findings are suggestive of relatively strong co-movements among crude oil volatility over time. For most part of the sample period, connectedness occurs in the short-run;nonetheless, starting approximately in 2010, long-run connectedness gains much prominence until at least the end of 2015. Long-run connectedness is also prevalent at the beginning of 2020 caused by the COVID-19 pandemic. We opine that periods of increased long-run connectedness relate to deeper changes in the market for crude oil that bring about new dynamics and associations within the specific network.

Predicting Crude Oil Future Price Using Traditional and Artificial Intelligence-Based Model: Comparative Analysis

Crude oil is an imperative energy source for the global economy. The future value of crude oil is challenging to anticipate due to its nonstationarity in nature. The focus of this research is to appraise the explosive behavior of crude oil during 2007-2022, including the most recent influential crisis COVID-19 pandemic, to forecast its prices. The crude oil price forecasts by the traditional econometric ARIMA model were compared with modern Artificial Intelligence (AI)-based Long Short-Term Memory Networks (ALSTM). Root mean square error (RMSE) and mean average percent error (MAPE) values have been used to evaluate the accuracy of such approaches. The results showed that the ALSTM model performs better than the traditional econometric ARIMA forecast model while predicting crude oil opening price on the next working day. Crude oil investors can effectively use this as an intraday trading model and more accurately predict the next working day opening price.

The relationship between crude oil futures and exchange rates in the context of the Covid-19 shock: a tale of two markets

We examine the high-frequency intraday return and volatility transmission between crude oil futures prices and exchange rates during the 2020 Covid-19 pandemic in the context of two markets: the newly established renminbi-denominated Shanghai International Energy Exchange in China and the US-dollar-denominated Brent market in the United Kingdom. By controlling for the influence of the stock markets, our findings reveal significant disparities in return linkages, yet fairly comparable volatility transmission patterns. The International Energy Exchange shows no return linkages with exchange rates except before the shock, while Brent consistently shows return spillovers from crude oil futures prices to exchange rates. In both markets, the volatility spillovers from exchange rates to crude oil futures prices are unidirectional prior to the shock but become bidirectional as a result of the shock. Nevertheless, both the return and volatility spillover patterns in China resemble those in the United Kingdom when utilizing offshore instead of onshore exchange rates. Such similarities in return and volatility spillovers can also be observed during the 2022 Covid-19 shock that emerged in Shanghai. These findings have significant practical implications. © Infopro Digital Limited 2023.

Price leadership in China's oil futures market: take two

This study is the first to conduct a comprehensive analysis of the price discovery and market liquidity aspects of China's crude oil futures market compared to WTI and Brent. With intraday-day data consolidated into 1-second intervals and three measures of price discovery, we find that China's crude oil futures market reports encouraging signs in terms of price discovery and efficiency, also showing great resilience during the COVID-19 pandemic. The market has obtained a dominant role in price discovery relative to WTI and Brent during its day trading hours, and has almost caught up with Brent in terms of market liquidity. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

The impact of COVID-19 on the portfolio optimization

PurposeThis article unveils first the lead–lag structure between the confirmed cases of COVID-19 and financial markets, including the stock (DJI), cryptocurrency (Bitcoin) and commodities (crude oil, gold, copper and brent oil) compared to the financial stress index. Second, this paper assesses the role of Bitcoin as a hedge or diversifier by determining the efficient frontier with and without including Bitcoin before and during the COVID-19 pandemic.Design/methodology/approachThe authors examine the lead–lag relationship between COVID-19 and financial market returns compared to the financial stress index and between all markets returns using the thermal optimal path model. Moreover, the authors estimate the efficient frontier of the portfolio with and without Bitcoin using the Bayesian approach.FindingsEmploying thermal optimal path model, the authors find that COVID-19 confirmed cases are leading returns prices of DJI, Bitcoin and crude oil, gold, copper and brent oil. Moreover, the authors find a strong lead–lag relationship between all financial market returns. By relying on the Bayesian approach, findings show when Bitcoin was included in the portfolio optimization before or during COVID-19 period;the Bayesian efficient frontier shifts to the left giving the investor a better risk return trade-off. Consequently, Bitcoin serves as a safe haven asset for the two sub-periods: pre-COVID-19 period and COVID-19 period.Practical implicationsBased on the above research conclusions, investors can use the number of COVID-19 confirmed cases to predict financial market dynamics. Similarly, the work is helpful for decision-makers who search for portfolio diversification opportunities, especially during health crisis. In addition, the results support the fact that Bitcoin is a safe haven asset that should be combined with commodities and stocks for better performance in portfolio optimization and hedging before and during COVID-19 periods.Originality/valueThis research thus adds value to the existing literature along four directions. First, the novelty of this study lies in the analysis of several financial markets (stock, cryptocurrencies and commodities)' response to different pandemics and epidemics events, financial crises and natural disasters (Correia et al., 2020;Ma et al., 2020). Second, to the best of the authors' knowledge, this is the first study that examine the lead–lag relationship between COVID-19 and financial markets compared to financial stress index by employing the Thermal Optimal Path method. Third, it is a first endeavor to analyze the lead–lag interplay between the financial markets within a thermal optimal path method that can provide useful insights for the spillover effect studies in all countries and regions around the world. To check the robustness of our findings, the authors have employed financial stress index compared to COVID-19 confirmed cases. Fourth, this study tests whether Bitcoin is a hedge or diversifier given this current pandemic situation using the Bayesian approach.

Can treasury inflation-protected securities safeguard investors from outward risk spillovers? A portfolio hedging strategy through the prism of COVID-19

Documenting the interlinkages among assets that are widely used to hedge against inflation is crucial for investors, as the necessity to protect the investment portfolio is stronger under inflationary conditions. For this purpose, we investigate the volatility spillovers between treasury inflation-protected securities (TIPS) and a battery of other assets perceived as inflation hedges, including bonds, gold, real estate, oil and equities. The applied methodology comprehends the time-varying parameter vector autoregressive (TVP-VAR) extension of the Diebold and Yilmaz (Int J Forecast 28:57–66, 2012, 10.1016/j.ijforecast.2011.02.006) approach for the period 1/1/2010–3/31/2022. Our results indicate that the assets under consideration are moderately interconnected and subjected to several exogenous shocks, such as the US–China trade war, the COVID-19 pandemic and the Russia–Ukraine war. Furthermore, we assess the hedging effectiveness of TIPS against each asset by estimating hedge ratios and optimal portfolios weights, before and after the spread of COVID-19 pandemic, by using conditional variance estimations (DCC-GARCH). The empirical findings show that the short position in the volatility of TIPS is proved to be an excellent hedge for all the sampled assets, with the exception of short-term Treasury bonds, and their hedging ability was improved during COVID-19.

Financial Contagion Between Crude Oil, Gold, and Equity Sectors in India During COVID

Purpose: This study examined the financial contagion between crude oil and gold prices with the equity prices of different sectors in the Indian equity market during the recent COVID crisis. Design/Methodology/Approach: Dynamic conditional correlation (DCC) GARCH model was employed to analyze the behavior of time-varying conditional correlation during the time of COVID-19. For examining the financial contagion, regression analysis was performed on the dynamic conditional correlation and the conditional volatilities of the different markets. Findings: The DCC model showed a sharp increase in correlations between markets during the COVID-19 wave. It also suggested the presence of financial contagion between the crude oil and gold markets and the different equity sectors. It also indicated that the COVID-19 effect on the conditional correlation between gold and equity sectors was temporary. In contrast, it increased the correlation between crude oil and the equity sectors. Practical Implications: The findings of this study have implications for portfolio diversification methods because higher correlations lower the benefits of diversification. Originality: This study examined the financial contagion during COVID-19 from crude oil and gold to equity sectors. Not all sectors react in the same way to changes in the prices of these commodities, and some may witness less impact compared to others during the crisis period, which makes it interesting for the study. © 2023, Associated Management Consultants Pvt. Ltd.. All rights reserved.

The Pandemic Shut-In: Reactivating Wells Shut-In in Spring/Summer 2020 – The Impact on 10-Year Forecast and EUR Outlook: A Duvernay Oil Case Study

The COVID-19 pandemic forced Canadian oil and gas operators to cut crude oil production by almost 1 MMb/d in the first half of 2020 due to low oil prices driven by reduced demand. This study explores the forecast and EUR performance of unconventional horizontal oil wells producing from the Duvernay Formation in central Alberta that were shut-in versus those that continued to produce uninterrupted throughout the reduced production period. How were forecasted production and EURs impacted? Did the manner in which the wells were completed play a role? This paper investigates these questions and more in a regional case study of 95 unconventional Duvernay oil wells using public data and a fully automated, physio-statistical, predictive analytical production forecasting tool. The bases of the performance comparison were the results of a 10-year forecast and EUR outlook for the wells evaluated in January, 2020 before the production slow down, and then re-evaluated in January, 2021, 12 months later, after the wells that were shut-in were back on production. In general, wells that continued producing uninterrupted throughout the study period exhibited significantly improved forecast and EUR performance over wells that were shut-in. Analyzing the performance of the largest field (Cygnet with 32 wells), with respect to lateral length, the results pointed to shorter wells that were shut-in exhibiting the poorest performance, where the wells' EUR performance degraded by 7% on average. The proppant intensity study for the same wells told a similar story, with shut-in wells with smaller fracs exhibiting negligible EUR improvement (0.4%) compared to the other categories of wells, with respect to frac size and shut-in status. A proximity study investigated two pads, one with only shut-in wells and the other with only non-shut-in wells, with the results pointing to competitive drainage between individual wells despite the overall performance of a given pad being neutral. Copyright 2022, Unconventional Resources Technology Conference (URTeC)

Dependence structure among rare earth and financial markets: A multiscale-vine copula approach

This paper examines the dynamic upper and lower tail dependence across rare earth metals, clean energy, gold, world equity, base metals, and crude oil markets at various time scales. Firstly, raw return series are decomposed into various time scales using the maximum overlapping discrete wavelet transform method, then the time-varying pairwise dependencies, accounting for the impact of the covariate (in our case, the rare earth stock index), are analysed using vine-copula. This so called multiscale-vine copula approach is applied to daily data from June 25, 2009 to October 7, 2022, covering the Covid-19 outbreak. The results show that, for raw returns, the rare earth market moderates the positive dependence between world equity and clean energy markets. At the short-term time scale, unlike other pairwise dependencies, rare earth eases the dependency between clean energies. During the Covid-19 pandemic period, the rare earth stock index significantly affects the correlation of the gold and oil markets and makes them more resilient to global health shocks. At the mid-term time scale, the impact of the rare earth index is more pronounced, for both the entire sample and during the Covid-19 outbreak, as the dynamic dependencies of most indices, such as clean energy-world equity, base metals-world equity, and crude oil-clean energy, significantly decline after accounting for the influence of rare earth metals. The main result at the long-term time scale is that the Covid-19 pandemic moderates the dependency of clean energy-gold even further when considering the impact of the rare earth stock index. In general, the rare earth stock index plays a significant role in easing the extent of dependency in the medium term during the entire sample and the pandemic. These findings provide some useful implications for heterogeneous investors and market participants operating at various time scales.

Oil price shocks, economic policy uncertainty, and green finance: a case of China

This study investigates the long- and short-run effects of crude oil price (COP) and economic policy uncertainty (EPU) on China's green bond index (GBI) using the quantile autoregressive distributed lag model. The empirical results show that COP and EPU produce a significant positive and negative influence on GBI in the long-run across most quantiles, respectively, but their short-run counterparts are opposite direction and only significant in higher quantiles. Thus, major contributions are made accordingly and shown in the following aspects. The findings emphasise the importance of understanding how COP and EPU affect China's green bond market for the first time. In addition, both the long- and short-run effects are captured, but long-run shocks primarily drive the green bond market. Finally, time- and quantile-varying analyses are adopted to explain the nexus between COP and EPU to GBI, which considers not only different states of the bond market but also events that occur in different time periods. Some detailed policies, such as a unified and effective green bond market, an early warning mechanism of oil price fluctuation, and prudent economic policy adjustments, are beneficial for stabilising the green finance market.

The Differential Effects of Oil Prices on the Development of Renewable Energy in Oil-Importing and Oil-Exporting Countries in Africa

The shift to renewable sources of energy has become a critical economic priority in African countries due to energy challenges. However, investors in the development of renewable energy face problems with decision making due to the existence of multiple criteria, such as oil prices and the associated macroeconomic performance. This study aims to analyze the differential effects of international oil prices and other macroeconomic factors on the development of renewable energy in both oil-importing and oil-exporting countries in Africa. The study uses a panel vector error correction model (P-VECM) to analyze data from five net oil exporters (Algeria, Angola, Egypt, Libya and Nigeria) and five net oil importers (Kenya, Ethiopia, Congo, Mozambique and South Africa). The study finds that higher oil prices positively affect the development of renewable energy in oil-importing countries by making renewable energy more economically competitive. Economic growth is also identified as a major driver of the development of renewable energy. While high-interest rates negatively affect the development of renewable energy in oil-importing countries, it has positive effects in oil-exporting countries. Exchange rates play a crucial role in the development of renewable energy in both types of countries with a negative effect in oil-exporting countries and a positive effect in oil-importing countries. The findings of this study suggest that policymakers should take a holistic approach to the development of renewable energy that considers the complex interplay of factors, such as oil prices, economic growth, interest rates, and exchange rates.

Performance of Equity Investments in Sustainable Environmental Markets

Despite a significant increase in global clean energy investments, as part of the decarbonization process, it remains insufficient to meet the demand for energy services in a sustainable manner. This study investigates the performance of sustainable energy equity investments, with focus on environmental markets, using monthly equity index data from 31 August 2009 to 30 December 2022. The main contributions of our study are (i) assessment of the performance of trading strategies based on the trend, momentum, and volatility of Environmental Opportunities (EO) and Environmental Technologies (ET) equity indices;and (ii) comparison of the performance of sustainable equity index investments to fossil fuel-based and major global equity indices. Market performance evaluation based on technical analysis tools such as the Relative Strength Index (RSI), Moving Averages, and Average True Range (ATR) is captured through the Sharpe and the Sharpe per trade. The analysis is divided according to regional, sector, and global EO indices, fossil fuel-based indices, and the key global stock market indices. Our findings reveal that a momentum-based strategy performed best for the MSCI Global Alternative Energy index with the highest excess return per unit of risk, followed by the fossil fuel-based indices. A trend-based strategy worked best for the MSCI Global Alternative Energy and EO 100 indices. The use of volatility-based information yielded the highest Sharpe ratio for EO Europe, followed by the Oil and Gas Exploration and Production industry, and MSCI Global Alternative Energy. We further find that a trader relying on a system which simultaneously provides momentum, trend, or volatility information would yield positive returns only for the MSCI Global Alternative Energy, the S&P Oil and Exploration and Production industry, NYSE Arca Oil, and FTSE 100 indices. Overall, despite the superior performance of the MSCI Global Alternative Energy index when using momentum and trend strategies, most region and sector EOs performed poorly compared to fossil fuel-based indices. The results suggest that the existing crude oil prices continue to allow fossil fuel-based equity investments to outperform most environmentally sustainable equity investments. These findings support that sustainable investments, on average, have yet to demonstrate consistent superior performance over non-renewable energy investments which demonstrates the need for continued, rigorous, and accommodating regulatory policy actions from government bodies in order to reorient significant capital flows towards sustainable equity investments.

Clean Energy Action Index Efficiency: An Analysis in Global Uncertainty Contexts

Climate change, the scarcity of fossil fuels, advances in clean energy, and volatility of crude oil prices have led to the recognition of clean energy as a viable alternative to dirty energy. This paper investigates the multifractal scaling behavior and efficiency of green finance markets, as well as traditional markets such as gold, crude oil, and natural gas between 1 January 2018, and 9 March 2023. To test the serial dependency (autocorrelation) and the efficient market hypothesis, in its weak form, we employed the Lo and Mackinlay test and the DFA method. The empirical findings showed that returns data series exhibit signs of (in)efficiency. Additionally, there is a negative autocorrelation among the crude oil market, the Clean Energy Fuels Index, the Global Clean Energy Index, the gold market, and the natural gas market. Arbitration strategies can be used to obtain abnormal returns, but caution should be exercised as prices may increase above their actual market value and reduce the profitability of trading. This work contributes to the body of knowledge on sustainable finance by teaching investors how to use predictive strategies on the future values of their investments.

Dynamic co-movement in major commodity markets during crisis periods: A wavelet local multiple correlation analysis

We study time-scale co-movement of returns and implied volatilities of oil, gold, wheat, and copper in a multivariate setting using the wavelet local multiple correlation (WLMC) approach. Daily data cover January 03, 2007 – August 08, 2022, including the global financial crisis, COVID-19 pandemic, and Russia-Ukraine war. The results show that the correlations across the commodities are heterogeneous, less stable in the short-term, and more pronounced in the long-term, and vary in sign and magnitude. Despite market instability, contagion is not clearly seen in either return or volatility, reflecting noise trading and the importance of the individual characteristics of commodities.