Extreme Market Research Articles

Analysis of market entry, restructuring and economic risks at energy companies – literature review

The entry of companies dealing with renewable energy sources into the market will be analyzed firstly, in addition to the companies dealing with fossil-based energy production, which are still strongly present. Here the game-theoretic stylization of the competition between two types of companies is in scope, while the incumbents determine the energy prices, and those entering the market can respond with quantitative adjustments. After that, the cyber security issues of the market-leading energy companies are under revision. Nowadays, due to the widespread use of computer control systems, a possible cyber-attack is a big risk for a power plant, as it can result in a complete system shutdown or, in the worst case, even irreparable damage. In light of these, risk networks are analyzed in three layers, reflecting normal and extreme market conditions. Ultimately, we will analyze the strategic steps of companies' restructuring and withdrawal. In the current economic situation, many dominant companies have a diversified portfolio, which on one hand promises beneficial profit opportunities and financial stability due to the parallel presence of diverse investment and production directions, but also makes it necessary to abandon the given route and to reallocate resources to another sector if necessary. Based on these points, by the end of the analysis, we will have a clearer picture of the struggle for market dominance of large energy companies and the steps necessary for success.

Predicting extreme events in the stock market using generative adversarial networks

Accurately predicting extreme stock market fluctuations at the right time will allow traders and investors to make better-informed investment decisions and practice more efficient financial risk management. However, extreme stock market events are particularly hard to model because of their scarce and erratic nature. Moreover, strong trading strategies, market stress tests, and portfolio optimization largely rely on sound data. While the application of generative adversarial networks (GANs) for stock forecasting has been an active area of research, there is still a gap in the literature on using GANs for extreme market movement prediction and simulation. In this study, we proposed a framework based on GANs to efficiently model stock prices’ extreme movements. By creating synthetic real-looking data, the framework simulated multiple possible market-evolution scenarios, which can be used to improve the forecasting quality of future market variations. The fidelity and predictive power of the generated data were tested by quantitative and qualitative metrics. Our experimental results on S&P 500 and five emerging market stock data show that the proposed framework is capable of producing a realistic time series by recovering important properties from real data. The results presented in this work suggest that the underlying dynamics of extreme stock market variations can be captured efficiently by some state-of-the-art GAN architectures. This conclusion has great practical implications for investors, traders, and corporations willing to anticipate the future trends of their financial assets. The proposed framework can be used as a simulation tool to mimic stock market behaviors.

Causal Relationship Between Spot Price & Future Derivative Prices – Case of Crude Oil

This research aims to examine the theoretical and empirical connections between current and future crude oil prices using cutting-edge econometric methods. It surveys existing literature and theoretical frameworks, exploring theories like arbitrage, market efficiency, and risk management techniques. The study then conducts a comprehensive empirical investigation using a large dataset, including data from extreme market volatility and other significant events in the financial world. Time series econometric methods like Granger causality tests, Vector Autoregression (VAR), and impulse response functions are used to capture the causal connections between spot and future prices. The analysis uncovers insights on lead-lag dynamics, volatility spillovers, and both short- and long-term causal impacts between current and future crude oil prices. Market fundamentals, geopolitical developments, and speculative behavior are also highlighted as influencing the causal relationships studied. The findings shed light on the link between future and current crude oil prices and provide valuable insights into the factors influencing these prices. This paper examines the relationship between future and spot crude oil prices, offering valuable insights for energy policy, risk management, hedging techniques, investment allocation, and governmental interventions.

Do green bond and green stock markets boom and bust together? Evidence from China

This paper investigates the green bond-stock correlation in China, as well as the influence of economic policy uncertainty (EPU) on that correlation, through cross-quantilogram and partial cross-quantilogram approaches. Directional spillovers are detected at different quantile levels across various investment horizons, and the findings reveal that the green bond and green stock markets are more connected in extreme market conditions than in normal conditions. The results indicate that the dependence structure between these markets exhibits distinct sector variation; only the green stocks (GS) in the water environment treatment (WT) and atmospheric protection (AP) sectors and green bonds (GB) boom together. Besides providing considerable diversification benefits, GB can act as a safe-haven asset for GS. Moreover, we find that spillover effects tend to be medium- and long-term. The uncertainty variable (EPU) is not a significant determinant of the cross-asset relation. The empirical results have significant implications for the formulation of ESG (Environment, Social and Governance) portfolio strategies and carbon neutral-oriented policymaking.

Price risk transmissions in the water-energy-food nexus: Impacts of climate risks and portfolio implications

The water-energy-food (WEF) nexus has quickly materialized as a critical factor that could threaten sustainable development goals. To contribute to the understanding of the WEF nexus from an economic perspective, this study investigates the price risk transmissions among the water, energy, and agricultural markets. We further analyze the critical impacts of climate risks, including physical and transition risks, on the transmission behaviors. Employing a quantile connectedness approach in the time-frequency domain, we provide a comprehensive analysis that allows for the consideration of different market conditions (extreme vs. normal) and time horizons (long vs. short). We find that the WEF nexus is relatively small in a normal market condition but increases substantially in an extreme market condition. The nexus is more pronounced in the short-term horizon compared to the long-term horizon. Among investigated factors, the U.S. climate policy and economic policy uncertainty have generally strengthened the WEF nexus while geopolitical risk and U.S. term spread tend to weaken the nexus. Analyzing the impacts of climate risks in detail, we find that in the short term, investors worry more about transition risks rather than physical risks in a normal market condition. In the long term, investors integrate information on both transition and physical risks but only in an extreme market condition. Our analysis of the economic implications further supports the view that investors care more about transition risks than physical risks when forming investment portfolios.

Diversification, hedge, and safe-haven properties of gold and bitcoin with portfolio implications during the Russia–Ukraine war

The aim of this study is to examine the diversification, hedge, and safe-haven properties of gold and bitcoin against the main commodities exported by Russia (crude oil, natural gas, wheat) and the G7, Russia, China, and Europe market indices during the Russia–Ukraine war. The results show gold is only a diversifier during the war. Bitcoin is a strong hedge against wheat but a weak hedge against natural gas. While gold has been a strong safe-haven against Italy, Russia, the MSCI Europe index and natural gas in the extreme market declines during the war, bitcoin has not exhibited strong safe-haven properties against any asset. As a result of portfolio practices, gold generally provides a better hedge than bitcoin. However, the hedging effectiveness of gold takes relatively low values, ranging from 0.8% to 19%. The conditional diversification benefit (CDB) analysis results show that gold provides a higher and more stable CDB against assets than bitcoin. The results are significant for portfolio strategies to be implemented by investors and portfolio managers.

Connectedness and hedging effects among China's nonferrous metal, crude oil and green bond markets: An extreme perspective

The interactions between nonferrous metal and crude oil markets have been wildly discussed in recent years. However, these interaction effects under extreme bearish and bullish market conditions are rarely investigated. This paper aims to quantify not only the extreme connectedness effects between China's nonferrous metal and crude oil futures markets, but also the hedging benefits of green bond on the nonferrous metal and crude oil futures portfolios. The empirical results show that China's nonferrous metal, crude oil and green bond markets are very closely connected under extreme bearish and bullish market conditions with no clear asymmetry. Moreover, the China's green bond can provide moderate hedging benefits to the nonferrous metal and crude oil futures portfolios even in extreme market environments, and these benefits are stable across different allocation methods.

Constructing time-series momentum portfolios with deep multi-task learning

A diversified risk-adjusted time-series momentum (TSMOM) portfolio can deliver substantial abnormal returns and offer some degree of tail risk protection during extreme market events. The performance of existing TSMOM strategies, however, relies not only on the quality of the momentum signal but also on the efficacy of the volatility estimator. Yet many of the existing studies have always considered these two factors to be independent. Inspired by recent progress in Multi-Task Learning (MTL), we present a new approach using MTL in a deep neural network architecture that jointly learns portfolio construction and various auxiliary tasks related to volatility, such as forecasting realized volatility as measured by different volatility estimators. Through backtesting from January 2000 to December 2020 on a diversified portfolio of continuous futures contracts, we demonstrate that even after accounting for transaction costs of up to 3 basis points, our approach outperforms existing TSMOM strategies. Moreover, experiments confirm that adding auxiliary tasks indeed boosts the portfolio’s performance. These findings demonstrate that MTL can be a powerful tool in finance.

A good hedge or safe haven? The hedging ability of China's commodity futures market under extreme market conditions

AbstractUsing a proposed quantile‐based hedging analysis framework, this paper analyzes the hedging properties and portfolio performance of 15 commodities futures in China, especially under extreme market conditions. Quantile‐on‐quantile regression results show that the dependency structures of most spot and futures prices exhibit an almost similar shape with positive coefficients, which indicates that most futures products are good hedges and safe havens for the corresponding spot prices. Quantile hedge ratios vary with the quantiles, displaying a U‐shape, so the hedge ratios of most commodities should increase moderately in both bearish and bullish markets. As for portfolio performance, metal commodities perform best; agricultural commodities second best, and energy commodities relatively poorly. Out‐of‐sample quantile hedging results are consistent with in‐sample results. Therefore, participating in the futures market can effectively manage risk. The premise is that investors pay attention to market dynamics and adjust their portfolios promptly.

Asymmetric downside risk across different sectors of the US equity market

This study integrates a threshold-mean equation with an asymmetric power autoregressive conditionally heteroscedastic (APARCH) model to examine the behavior of sector-specific exchange-traded funds (ETFs) during extreme market downturns between December 23, 1998, and November 2, 2022. Thus, predetermined and optimal boundary points are applied to the extreme left tail of the return distribution to assess the extent of downside risk differentials inside and outside the extreme drawdown zone without splitting the sample period. According to the findings, the betas of the ETFs XLI, XLP, XLV, and XLY are comparable under both extreme and nonextreme market conditions. In contrast, XLF, XLE, and XLU have higher downside betas during extreme market conditions compared with their nonextreme betas, while XLB and XLK exhibit the opposite pattern. The results remain robust and consistent regardless of how the boundary points are established. The estimated models in this study were successfully subjected to a series of diagnostic tests, suggesting that the commonly held view about asymmetric responses in different market conditions does not apply to all market segments.

Modelling Systemic Risk in Morocco’s Banking System

The Moroccan banking system suffered a significant impact due to the extreme market conditions caused by the COVID-19 outbreak, which led to an increase in non-performance loans. This, in turn, reduced the value of banks’ assets and their ability to meet their obligations, implicitly raising systemic risk. In such circumstances, the collapse of one financial institution could cause a series of bankruptcies and endanger the overall state of the economy. Given the limited attention devoted to the analysis of systemic risk in the Moroccan banking system, this paper aimed to fill this gap by analyzing the Moroccan banks’ systemic risk exposure and assessing their stability during the COVID-19 crisis, using Quantile Regression Neural Network (QRNN) optimized by Adam algorithm to calibrate the Conditional Value at Risk (CoVaR). This study revealed a significant increase in systemic risk during the pandemic crisis and highlights the suitability of more complex QRNN in assessing systemic risk. The findings emphasize the need for regulators to pay close attention to banks’ risk exposures when implementing measures to mitigate systemic risk, such as increasing banks’ capital requirements or increasing the amount of high-quality liquid assets.

Extreme risk transmission mechanism between oil, green bonds and new energy vehicles

This paper combines a time-varying spillover index based on a time-varying vector auto-regressive (TVP-VAR) model with quantile regression to investigate the mechanism of extreme risk contagion among oil, green bonds and new energy vehicles under different market conditions. The empirical analysis in this paper show that total spillover index between oil, green bonds and new energy vehicles is about 37% in the mean and median situations, and about 80% at extreme quantile conditions. The quantile-based connectedness model outperforms the mean-based connectedness model. We are also able to conclude that in the extreme downside market scenario, except for WTI, rest of the market was a net transmitter of systemic shocks; in the extreme upside market scenario, except for WTI and green bonds, rest of the market was a net transmitter of systemic shocks. The spillovers among green bonds, new energy vehicles and oil are asymmetrical. Market regulators should make timely regulation and adjustments in extreme market conditions to prevent systemic risk.

Bubble behaviors in nickel price: What roles do geopolitical risk and speculation play?

The stable operation of nickel market provides essential safeguard for the realization of global sustainable energy transition. In the context of prominent geopolitical risks and financial market instability, the explosive dynamics of nickel market price have not been thoroughly investigated. From a boom-bust cycle perspective, this study examines the existence of periodically bubble behaviors in nickel price utilizing the Generalized Supremum Augmented Dickey-Fuller method. The method delivers an innovative consistent date-stamping assessment for the exuberance and collapse of bubble episodes. This paper further probes into the effects of geopolitical risk, speculations, demand, supply and dollar on bubbles through logistic technique. The results find that four boom-bust cycles in nickel price are detected during 1990–2022, which are basically accompanied by extreme market volatility. This finding is in line with the asset pricing model which implies both expected and invisible components exist in asset price. In terms of macroeconomic determinants, nickel demand positively affects the boom-bust behaviors, while nickel supply and dollar dynamics have a negative impact on bubble occurrence. Particularly, geopolitical risk and speculations are proven to be important drivers in boosting the explosiveness of bubbles. To counter the hazardous economic consequences of bubble collapse, policymakers should formulate precautionary policies in the face of complicated geopolitical landscape and volatile capital market environment.

Diversification effects of China's carbon neutral bond on renewable energy stock markets: A minimum connectedness portfolio approach

Socially responsible investment (SRI) is becoming increasingly popular in China, with the announcement of carbon dioxide peaking by 2030 and carbon neutrality by 2060 (30–60 targets). As a conventional underlying asset of SRI, renewable energy stocks are attractive, but with higher price volatility than many other traditional industrial stocks. This paper aims to investigate the interaction and diversification effects of China's Carbon Neutral Bond (CNB), a new SRI underlying asset just launched in 2021, on renewable energy stocks using a novel minimum connectedness portfolio approach recently proposed by Broadstock et al. (2020). The empirical results show that, first, China's CNB is weakly related to renewable energy stocks, whether in the time or frequency domain, and is the main recipient of the net linkage effect. Second, different renewable energy stocks benefit differently from the diversification effect of CNB. However, in extreme market conditions, the diversification effect of the CNB is reduced. Third, the newly developed minimum connectedness portfolio approach can outperform traditional minimum variance and correlation methods by achieving higher cumulative portfolio returns. Finally, the Sharpe ratios of renewable energy stock portfolios with CNB are significantly higher than those without it across different allocation methods. These findings have important implications for policy makers and SRI investors.

Global hidden factors predicting financial distress in Gulf Arab states: a quantile–time–frequency analysis

PurposeThis study aims to uncover the main predictors of financial distress in the Gulf Cooperation Council (GCC) countries using a wide range of global factors and asset classes.Design/methodology/approachThis study uses novel approaches that take into account extreme events as well as the nonlinear behavior of time series over various time intervals (i.e. short, medium and long term) and during boom and bust episodes. This study primarily uses the conditional value at risk (CoVaR), the quantile multivariate causality test and the partial wavelet coherence method. The data collection period ranges from March 2014 to September 2022.FindingsUS T-bills and gold are the primary factors that can increase financial stability in the GCC region, according to VaRs and CoVaRs. More proof of the predictive value of the oil, gold and wheat markets, as well as geopolitical tensions, uncertainty over US policy and volatility in the oil and US equities markets, is provided by the multivariate causality test. When low extreme quantiles or cross extreme quantiles are taken into account, these results are substantial and sturdy. Lastly, after adjusting for the effect of crude oil prices, this study’s wavelet coherence results indicate diminished long-run connections between the GCC stock market and the chosen global determinants.Research limitations/implicationsDespite the implications of the author’s research for decision makers, there are some limitations mainly related to the selection of Morgan Stanley Capital International (MSCI) GCC ex-Saudi Arabia. Considering the economic importance of the Kingdom of Saudi Arabia (KSA) in the region, the author believes that it would be better to include this country in the data to obtain more robust results. In addition, there is evidence in the literature of the existence of heterogeneous responses to global shocks; some markets are more vulnerable than others. This is another limitation of this study, as this study considers the GCC as a bloc rather than each country individually. These limitations could open up further research opportunities.Originality/valueThese findings are important for investors seeking to manage their portfolios under extreme market conditions. They are also important for government policies aimed at mitigating the impact of external shocks.

Analyzing the static and dynamic dependence among green investments, carbon markets, financial markets and commodity markets

PurposeThis paper investigates the static and dynamic directional return spillovers and dependence among green investments, carbon markets, financial markets and commodity markets from January 2013 to September 2020.Design/methodology/approachThis study employed both the quantile vector autoregression (QVAR) and time-varying parameter VAR (TVP-VAR) technique to examine the magnitude of static and dynamic directional spillovers and dependence of markets.FindingsResults show that the magnitude of connectedness is extremely higher at quantile levels (q = 0.05 and q = 0.95) compared to those in the mean of the conditional distribution. This connotes that connectedness between green bonds and other assets increases with shock size for both negative and positive shocks. This further indicates that return shocks spread at a higher magnitude during extreme market conditions relative to normal periods. Additional analyses show the behavior of return transmission between green bond and other assets is asymmetric.Practical implicationsThe findings of this study offer significant implications for portfolio investors, policymakers, regulatory authorities and investment community in terms of carefully assessing the unique characteristics offered by each markets in terms of return spillovers and dependence and diversifying the portfolios.Originality/valueThe study, first, uses a relatively new statistical technique, the QVAR advanced by Ando et al. (2018), to capture upper and lower tails’ quantile price connectedness and directional spillover. Therefore, the results possess adequate power against departure from mean-based conditional connectedness. Second, using a portfolio of green investments, carbon markets, financial markets and commodity markets, the uniqueness of this study lies in the examination of the static and dynamic dependence of the markets examined.

Tail spillover effects between cryptocurrencies and uncertainty in the gold, oil, and stock markets

This study investigates tail dependence among five major cryptocurrencies, namely Bitcoin, Ethereum, Litecoin, Ripple, and Bitcoin Cash, and uncertainties in the gold, oil, and equity markets. Using the cross-quantilogram method and quantile connectedness approach, we identify cross-quantile interdependence between the analyzed variables. Our results show that the spillover between cryptocurrencies and volatility indices for the major traditional markets varies substantially across quantiles, implying that diversification benefits for these assets may differ widely across normal and extreme market conditions. Under normal market conditions, the total connectedness index is moderate and falls below the elevated values observed under bearish and bullish market conditions. Moreover, we show that under all market conditions, cryptocurrencies have a leadership influence over the volatility indices. Our results have important policy implications for enhancing financial stability and deliver valuable insights for deploying volatility-based financial instruments that can potentially provide cryptocurrency investors with suitable hedges, as we show that cryptocurrency and volatility markets are insignificantly (weakly) connected under normal (extreme) market conditions.

Examining the superiority of the Sharpe single-index model of portfolio selection: A study of the Indian mid-cap sector

The purpose of the article was to examine the superiority and efficacy of Sharpe’s single-index model of portfolio optimisation. The study has attempted to build an optimal portfolio of Indian mid-cap companies using William Sharpe’s single-index model. The methodology is also known as the Market model. A portfolio was selected from the Nifty mid-cap 100 index of the NSE. MS-Excel 365 has been used for the analysis. The optimal portfolio returns during the fixed period of analysis were compared with the returns of the benchmark market portfolio. The return of the optimal portfolio using Sharpe’s model was found to be considerably higher than the benchmark market portfolio and the risk of the same was found to be much lower. Hence it could be established that in the five years of the study period, the optimal portfolio outperformed the benchmark market portfolio—the Nifty mid-cap 100 index. The selected optimal portfolio was also found to be well diversified comprising 11 securities and eight sectors. The limitation of this model is that it was based on historical data and hence, in case of extreme market conditions, the optimal portfolio could fail to give superior returns. Sharpe’s model resolves most of the technical difficulties of the earlier portfolio models and can very well be used by individual investors as well as portfolio managers worldwide to build optimal portfolios. The study has focussed on the mid-cap sector which is riskier than the large-cap sector. This is one of the first studies which has shown the efficacy of the single-index model for mid-cap companies in India.

Measuring the frequency and quantile connectedness between policy categories and global oil price

This study explores the connectedness between various categories of economic policy uncertainty (EPU) and global crude oil prices in different frequencies and quantiles using the generalized forecast error variance decomposition and data in the US, China, and Japan from January 2000 to May 2022. The empirical results may be summarized as follows. First, total short and long term connectedness exhibits different patterns and is more sensitive to extreme positive and negative shocks than regular shocks. Second, fiscal policy uncertainty (FPU) and monetary policy uncertainty (MPU) tend to act as net transmitters of shocks, while the roles of trade policy uncertainty (TPU) are mixed in the short term, irrespective of the country. However, under extreme market conditions, no specific-category EPU features a clear net transmitter/recipient. Finally, the results are qualitatively and quantitatively unaffected by the chosen proxy of crude oil prices and are not altered by global real economic activity.

The Eligibility of Green Bonds as Safe Haven Assets: A Systematic Review

This study follows Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to examine the existing literature on the connectedness of green bonds with other markets as an attempt to highlight the effectiveness of green bonds in risk management and the benefits associated with incorporating green bonds in investment portfolios. An extensive search of relevant research papers to the scope of the review led to the identification of 31 articles published by February 2022. Our analysis traces the evolution of studies on green bonds’ interactions with other markets, the methodologies and data frequencies used for cross-market relations analysis, and the role of green bonds in portfolio risk management (diversifier, hedge, and safe-haven) in normal and extreme market conditions. The study reports several interesting findings. First, green bonds can be a strategic safe-haven avenue for investors in stocks, dirty energy stocks, and the foreign exchange market in the US and China in extreme market downturns. Second, green bonds demonstrated hedging properties against spillovers from Bitcoin, forex, soft commodities, and CO2 emission allowance. Third, the role of green bonds in the markets of natural gas, industrial metals, and crude oil is limited to a portfolio diversifier in different investment horizons. Fourth, green bonds had no diversification or hedge benefits for investors in conventional bonds. Fifth, the interrelationships between green bonds and most markets’ understudy were influenced by macroeconomic and global factors such as the COVID-19 pandemic, economic policy uncertainty, OVX, and VIX. Our review of the literature also facilitated identification of future research topics. The outcome of the review offers insightful information to investors in green bonds in risk management and assets allocation. Policy makers can benefit from this review in effective policy legislation for the advancement of the green bonds market and acceleration of a smooth transition to a net zero emission economy.