Extreme Shocks Research Articles

Dynamic spillovers among global oil shocks, economic policy uncertainty, and inflation expectation uncertainty under extreme shocks

This study explores the quantile connectedness between United States (U.S.) economic policy uncertainty (EPU), global structural oil shocks, and U.S. inflation expectations uncertainty (IEU) under extreme shocks using a connectedness method based on the quantile VAR model. We find that the total connectedness index (TCI) exhibits a U-shaped pattern that varies with the conditional quantiles of variables, demonstrating that the spillover effect under extreme market conditions is much greater than under regular market conditions. Further proven that the spillover effect in the extreme upward state is stronger than in the extreme downward state. Moreover, the dynamic TCI is heterogeneous over time and economic-event dependent, specifically affected by COVID-19 epidemic. IEU is the largest net receiver of spillover effects among variables and hence is more susceptible to EPUs and oil shocks. Finally, although there is significant heterogeneity in the spillover effects of different EPUs and structural oil price shocks, overall, EPUs influence the IEU more than global oil shocks.

Modeling of Machine Learning-Based Extreme Value Theory in Stock Investment Risk Prediction: A Systematic Literature Review.

The stock market is heavily influenced by global sentiment, which is full of uncertainty and is characterized by extreme values and linear and nonlinear variables. High-frequency data generally refer to data that are collected at a very fast rate based on days, hours, minutes, and even seconds. Stock prices fluctuate rapidly and even at extremes along with changes in the variables that affect stock fluctuations. Research on investment risk estimation in the stock market that can identify extreme values is nonlinear, reliable in multivariate cases, and uses high-frequency data that are very important. The extreme value theory (EVT) approach can detect extreme values. This method is reliable in univariate cases and very complicated in multivariate cases. The purpose of this research was to collect, characterize, and analyze the investment risk estimation literature to identify research gaps. The literature used was selected by applying the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and sourced from Sciencedirect.com and Scopus databases. A total of 1107 articles were produced from the search at the identification stage, reduced to 236 in the eligibility stage, and 90 articles in the included studies set. The bibliometric networks were visualized using the VOSviewer software, and the main keyword used as the search criteria is "VaR." The visualization showed that EVT, the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models, and historical simulation are models often used to estimate the investment risk; the application of the machine learning (ML)-based investment risk estimation model is low. There has been no research using a combination of EVT and ML to estimate the investment risk. The results showed that the hybrid model produced better Value-at-Risk (VaR) accuracy under uncertainty and nonlinear conditions. Generally, models only use daily return data as model input. Based on research gaps, a hybrid model framework for estimating risk measures is proposed using a combination of EVT and ML, using multivariable and high-frequency data to identify extreme values in the distribution of data. The goal is to produce an accurate and flexible estimated risk value against extreme changes and shocks in the stock market. Mathematics Subject Classification: 60G25; 62M20; 6245; 62P05; 91G70.

Impact evaluation of a maternal and child cash transfer intervention, integrated with nutrition, early childhood development, and agriculture messaging (MAZIKO-IE): a study protocol for a cluster-randomised controlled trial

BackgroundChildren in Malawi face high rates of malnutrition and are at risk of not reaching their developmental potential. Community-based childcare centres (CBCCs) can be cost-effective platforms for scaling-up early childhood development (ECD) and nutrition social behaviour change (SBC) interventions. However, evidence also suggests potential synergies from coupling nutrition SBC with cash transfers (CT), given that rural households in Malawi face high levels of poverty and recurring extreme lean season food-security shocks. The Maziko trial is aimed at evaluating the effectiveness and cost-effectiveness of using CBCCs and parenting care groups as platforms to improve maternal diets and child nutrition and development by providing nutrition-sensitive SBC and CT intervention packages in communities already receiving a standard of care Government SBC program.MethodsWe designed a 3-year cluster-randomised controlled trial in two districts of Malawi, including 156 communities randomised to one of four treatment arms: (1) standard of care (SoC) arm: receiving the standard Government SBC program; (2) SBC arm: receiving the SoC intervention with additional nutrition-sensitive SBC activities to improve nutritious food production, diets, and care practices for young children; (3) low CT arm: SoC plus SBC plus a maternal and child cash transfer ~ 17 USD per month; and (4) high CT arm: SoC plus SBC plus a maternal and child CT ~ 43 USD per month. The trial will enrol pregnant women and children < 2 years of age. The primary outcomes are maternal diet assessed using the mean probability of adequacy and child development assessed using the Malawi Developmental Assessment Tool. Intermediate outcomes along the programme impact pathways will also be measured, including maternal mental health, maternal empowerment, child feeding practices, and child nutritional status.DiscussionThis is the first study to examine the impact and synergies of combining ECD SBC with nutrition-sensitive SBC and CTs on maternal and child outcomes during the first 1000 days. The findings from this evaluation will inform national ECD and nutrition programmes.Trial registrationISRCTN ISRCTN53055824. Registered on 7 March 2022.

Modeling Development of a Diamagnetically Stabilized Magnetically Levitated Gravimeter.

The aim of this work is to create a new type of gravimeter that can function effectively in the challenging conditions of space, specifically on the surfaces of planets and moons. The proposed device, called a diamagnetically stabilized magnetically levitated gravimeter (DSMLG), uses magnetic forces to balance a test mass against the force of gravity, allowing for accurate measurements. A diamagnetically stabilized levitation structure comprises a floating magnet, diamagnetic material, and a lifting magnet. The floating magnet levitates between two diamagnetic plates without the need for external energy input due to the interaction between the magnetic forces of the floating magnet and the stabilizing force of the diamagnetic material. This structure allows for stable levitation of the floating magnet without requiring additional energy. The goal is to design a gravimeter that is lightweight, requires minimal power, can withstand extreme temperatures and shocks, and has a low data rate. The authors envision this gravimeter being used on various robotic spacecraft, such as landers and rovers, to study the interiors of rocky and icy celestial bodies. This paper reports on the results of a finite element model analysis of the DSMLG and the strength of the resulting diamagnetic spring. The findings contribute to the understanding of the levitation characteristics of diamagnetically stabilized structures and provide valuable insights for their practical applications, including in the development of the proposed DSMLG.

City-level resilience to extreme weather shocks revealed by satellite nighttime lights in China

Given the unprecedented climate change, extreme weathers have become more intense and frequent, causing severe socio-economic impacts. Urban resilience is vital for mitigating extreme events, but little is known about its city-level response to such shocks in China. Here, we aim to investigate the persistent effects of extreme heat and heavy rainfall on Chinese cities, ultimately revealing urban resilience. We use monthly nighttime lights from 2013–2019 as a proxy for urban functioning. Our results suggest that cities are less resilient to extreme heat than to heavy rainfall, yet the adverse effects of heavy rainfall can persist for up to seven months. Importantly, we reveal for the first time that areas near the Hu Line are vulnerable to heavy rainfall (e.g., Beijing, Tianjin and Chongqing), and cities in the Yangtze River basin are most affected by extreme heat (up to 24.8 % loss of nighttime light intensity). There is an urgent need to address severe weather impacts in regions dominated by secondary sector, while developed economies are vulnerable to climate hazards, despite having high defense. Our findings identify urban climate risk hotspots and underlying impact mechanisms, providing valuable insights into climate mitigation policies and urban development strategies.

Tail connectedness between artificial intelligence tokens, artificial intelligence ETFs, and traditional asset classes

We examine extreme connectedness between the artificial intelligence (AI) tokens, artificial intelligence ETFs, and other asset classes, using the quantile VAR approach of Ando et al., (2022). We find moderate connectedness levels at mean and median quantiles, with AI ETFs (AI tokens) acting as strong (weak) net emitters (receivers) of return spillovers. Findings, confirmed by alternative testing, suggest that, during normal market conditions, AI tokens may offer utility as diversifiers for portfolios of traditional assets. However, at both lower and upper quantiles, connectedness levels increase, consistent with AI tokens and ETFs being sensitive to extreme shocks. Results suggest that AI tokens and ETFs do not diversify the risk of other assets during extreme market conditions. Finally, AI tokens, especially, may offer effective hedging at low cost for traditional assets (gold, equity, real estate, bonds, and currency), except for the oil and cryptocurrency market. Investors including AI assets in portfolios need to diligently monitor for changing market conditions.

Commodity futures markets under stress and stress-free periods: Further insights from a quantile connectedness approach

Most of the academic literature on connectedness focuses on stock markets and commodity spot markets. However, there is still much to say about the connectedness among commodity futures markets at different expiration dates, as this part of the literature is as yet small and inconclusive. This paper builds on the existing literature by focusing on connectedness among a set of ten commodity futures markets (including energy, agriculture, and metal sectors) at different maturities, the global equity market and three different sources of uncertainty (financial, economic, and geopolitical) over the period 2000–2022. In doing so, we estimate a combination of complementary connectedness metrics based on the work of Diebold and Yilmaz (2012), which enables measuring average, and the works of Chatziantoniou et al. (2021) and Ando et al. (2022) which enable measuring connectedness under different market circumstances (i.e., low, median and high quantiles). The analysis provides evidence of the variable aspect of connectedness across commodities and uncertainty measures assessed across different quantiles. The average directional connectedness network suggests that commodity futures markets within the same category are significantly sensitive to each-other. However, interdependencies between commodities belonging to different categories are relatively lower. The average total connectedness across quantiles provides evidence of (i) a clear symmetric pattern at the extreme lower and upper quantiles, and (ii) an increase in connectedness with the magnitude of extreme negative and positive shocks. The time-varying analysis indicates that connectedness increases at all quantiles during periods of high market stress, but with relatively higher intensity at the lower quantiles. Additionally, the structure and magnitude of connectedness at the extremes – upper and lower quantiles – differs from the pattern of connectedness at the median quantiles.

Job displacement and reallocation failure. Evidence from climate shocks in Morocco

Abstract We investigate the impact of severe drought shocks in Morocco’s agricultural sector. Using a staggered difference-in-differences design, we estimate that climatic shocks resulted in a job displacement of approximately 6.5 percentage points for workers exposed to severe drought events. Additionally, we observe that, overall, approximately 39 per cent of these workers remained unemployed, leading to a partial reallocation failure. These effects are significant only for severe and extreme shocks, persist for at least 5 years, and are more pronounced among informal and female workers.

The impact of COVID-19 on the volatility connectedness of the Chinese tourism sector

This paper investigates the dynamic behaviour of the volatility connectedness of COVID-19-related stock indices on Chinese tourism sub-sectors using a novel TVP-VAR frequency approach of Chatziantoniou et al. (2021). Our empirical results show that COVID-19 affects all six examined tourism sub-sectors, but the volatility transmissions between COVID-19 and each tourism sector are not uniform. Different waves of COVID-19 have contributed to time-varying changes of volatility connectedness to all tourism sectors. Our results will be of great interest and importance to policymakers and investors, who will better understand developments in China’s tourism sectors under extreme financial shocks and learn how to better manage risk considering potential future epidemics and pandemics.

Intraday and overnight tail risks and return predictability in the crude oil market: Evidence from oil-related regular news and extreme shocks

This study examines the possible impacts of various market shocks, covering oil-related regular news releases and extreme shocks on the intraday and overnight variations in tail risk and intraday return predictability in the crude oil market using high-frequency United States Oil Fund (USO) data from April 10, 2008, to May 10, 2022. First, we find that both regular news and extreme shocks significantly impact the distribution of tail risk during daytime trading and overnight non-trading sessions, with the latter showing stronger effects. Second, extreme shocks have a greater impact on intraday return predictability, with most market shocks obliterating the predictive power of first half-hour returns. Third, the predictability of the first half-hour returns comes primarily from the market opening session from 9:30 am to 10:00 am, and overnight returns lose their predictability. These results have important implications for both market participants and policymakers.

The asymmetric volatility spillover across Shanghai, Hong Kong and the U.S. stock markets: A regime weighted measure and its forecast inference

Based upon generalized spillover index method under Markov switching vector auto-regression (MS-VAR) framework, this paper aims at a regime dependent measure of the volatility spillover asymmetry across Shanghai, Hong Kong and the U.S. stock markets. For that, we apply a regime weighted measure of spillover and its asymmetry. The empirical results show that there existed evident negative asymmetric effect of spillover across the three markets, and it was regime dependent and time-varying: it got intensified under high volatility regime and turned out stronger after the Program of Shanghai-Hong Kong stock connect. The regime weighted measure of spillover asymmetry is found to be reliable; it could be explained appropriately with the economic policy uncertainty and investors' fear in the U.S., extreme shocks like crash of Shanghai stock market and COVID-19 as well as the capital flow through the Program. From in-sample evidence and out-of-sample forecast of Shanghai stock market, we infer that traditional forecast models could be improved if regime dependent spillover and its asymmetry are considered as additional factors.

Extreme events and the resilience of decentralised governance

ABSTRACT Extreme events, such as economic crises, natural disasters or military conflicts, can affect the balance between centralisation and decentralisation forces across countries and transform, temporarily or more permanently, the design of multilevel governance. Using a panel of 91 developing and developed countries from 1960 to 2018, and another for Organisation for Economic Co-operation and Development (OECD) countries between 1995 and 2018, we examine the effects of extreme external shocks on the decentralisation level. We find that armed conflicts boost decentralisation, while natural disasters reduce it only in non-OECD countries, with long-lasting effects in both cases. Economic recessions do not have significant effects on the level of decentralisation, except for the lasting effects on expenditure recentralisation in OECD countries.

Predicting cryptocurrency market volatility: Novel evidence from climate policy uncertainty

This study employs two mixed-frequency volatility models to examine the impact of climate policy uncertainty on cryptocurrency price volatility. The empirical findings reveal that climate policy uncertainty has a significant positive impact on cryptocurrency price volatility and that this effect is mainly driven by extreme climate policy shocks. That is, abrupt and substantial shifts in climate policy lead to increased volatility in cryptocurrency prices. Moreover, different cryptocurrencies exhibit different responses to climate policy uncertainty. In addition, the predictive performance of the model that accounts for extreme shocks to climate policy uncertainty outperforms the basic GARCH-MIDAS-CPU model for all three cryptocurrencies.

How do sectoral Islamic equity markets react to geopolitical risk, economic policy uncertainty, and oil price shocks?

This study explores the impact of three major risk and uncertainty indices (Geopolitical Risk (GPR), Economic Policy Uncertainty (EPU), and Oil Market Volatility (OVX)) on the Dow Jones Islamic Market (DJIM) World and the ten major sectoral Islamic equity indices. The investigation further evaluates whether the impact of these uncertainties varies between Islamic sectoral indices and the global benchmark. Our time-varying analysis shows that most Islamic equity indices are more resilient in hedging against GPR and EPU shocks compared to OVX shocks. Furthermore, the Quantile-on-Quantile results establish a positive correlation between DJIM World and most Islamic equity indices with GPR, demonstrating their robust capabilities to hedge against GPR shocks. These hedging abilities are more pronounced in the consumer goods, oil & gas, and financial sectors. The dependence structures between extreme EPU shocks and the DJIM world and consumer goods, financials, healthcare, and industrial sectors are positive only in bearish conditions, suggesting hedging benefits are predominantly limited to lower quantiles. Yet, the basic materials and oil & gas sectors can hedge EPU shocks better than the DJIM world and other sectors, depending on market states. Notably, no Islamic equity indices show resilience against OVX shocks. Our findings have prominent portfolio risk management and policy implications for investors and policymakers. These findings hold significant implications for portfolio risk management and policy strategies for investors and policymakers.

Dynamic information spillover between Chinese carbon and stock markets under extreme weather shocks

The present study aims to investigate the dynamic information spillover relationship between Chinese carbon and stock markets, as well as the impact of extreme weather shocks exerted on this relationship. The method adopted is the least absolute shrinkage and selection operator–vector autoregressive–Diebold-Yilmaz spillover approach so that the degree and direction of risk spillovers among markets can be assessed simultaneously. Empirical results reveal that there is a high level of extreme risk spillover among markets in comparison to return spillover. The carbon market receives return spillover from high-polluting sectors, but it will turn into a risk transmitter under extreme risk conditions. Weather shocks significantly affect extreme risk spillover among markets and may lead to spillovers from the carbon market to low-polluting sectors. The portfolio strategy constructed based on the identified information spillover relationship is shown to achieve higher average returns than strategies focusing on a single carbon or stock market sector. This paper is among the first to integrate carbon markets and 38 stock sector indices for different pollution intensities, comprehensively exploring their dynamic interrelationships under extreme weather threats. The corresponding practical and policy implications for investors and regulators are also provided along with these findings.

Exchange rate volatility predictability: A new insight from climate policy uncertainty

Against the background of growing concern about climate change, this study attempts to examine the impact of US climate policy uncertainty on exchange rate volatility. Specifically, this paper explores whether climate policy changes and their extreme observations, as well as the short-term asymmetry of exchange rate volatility, can help predict future exchange rate volatilities. The in-sample estimation results show that the uncertainty of US climate policy significantly affects the volatility of the US exchange rate, and extreme positive shocks of climate policy uncertainty have a greater impact on the exchange rate than extreme negative shocks of the same magnitude. In addition, we find significant asymmetries in the short-term components of exchange rate volatility, especially in the USD-CNY exchange rate and the USD-EUR exchange rate. Multiple out-of-sample forecasting tests show that models incorporating extreme values of climate policy uncertainty exhibit superior forecasting performance. Notably, the predictive performance of these models tends to be stronger during periods of low volatility and relatively weak during periods of high volatility. This paper provides valuable insights for stakeholders to make informed decisions and optimize strategies in the face of uncertain climate policies.

Do world stock markets “jump” together? A measure of high-frequency volatility risk spillover networks

We provide a systematic framework for studying high-frequency risk connectedness among world stock markets. Our framework, based on high-frequency realized volatility decompositions, TVP-VAR networks, and innovative extensions, shows risk connectedness among 16 world stock markets with respect to seven levels of high-frequency volatility. The findings are as follows. First, all seven levels of risk connectedness are significantly enhanced in response to extreme event shocks. The COVID-19 event led to a peak in total connectedness, and the event changed the structure of the spillover network the most, with markets in Asia-Pacific continent being the most affected by this event. The transmission of information is unique to different levels, with the “bad” volatility level being the strongest and the jump volatility level being the most sensitive. Second, there is a geographical effect on stock market performance, with the U.S. stock market being the most stable and dominant. The mainland Chinese stock market is the most vulnerable to volatility, being a net receiver of volatility 91.04% of the time. Third, we provide evidence of asymmetric spillovers at the level of “good-bad” volatility and “good-bad” jumps. The similarity between these two asymmetric effects is that the negative asymmetry is substantial and more pronounced in response to risky event shocks. The difference is that the asymmetry of “good-bad” jumps reflects more strongly the impact of crisis event shocks. Seven countries are dominated by pessimism most of the time, with the UK, Canada and Mexico being the main transmitters of pessimism, and their ability to transmit pessimism has increased during the crisis.

Tree diversity and social–ecological resilience of agroforestry after volcanic ash deposition in Indonesia

Smallholder farmers and their agroecosystems in active volcanic landscapes need to deal with and recover from eruptions. Resilience to extreme shocks may increase with system diversity, enhancing food and income security and ecosystem services provision; however, the longer term effects of volcanic ash are rarely assessed. To test the hypothesis that tree diversity contributes to the social–ecological resilience of coffee-based agroforestry, we quantified (1) the immediate effects of deposition of a 15-cm ash layer on tree survival, (2) the effect of volcanic ash on aboveground C stocks, tree diversity and wood density frequencies, (3) litter layer dynamics, and (4) farming system and income recovery 3 years after. Observations in four land-use systems before and after ash deposition (remnant forest, coffee-based complex and simple agroforestry, annual crops) were complemented by 46 farmer interviews on tree species’ survival, system, and financial recovery. Based on farmer interviews, low-wood-density trees were most affected by volcanic ash deposition. Ash deposition did not, after 3 years and across land-use systems, significantly change tree density, basal area, or C stocks. In contrast, species richness in coffee-based agroforestry increased significantly. Standing litter stocks in agroforestry decreased, but slower decomposition partially compensated for reduced litter input. Farmers stated that diversity and flexibility in coffee-based agroforestry support a system recovery that is faster than that for annual crops, suppressing income fluctuation. Farmer’s adaptive responses to enhance species diversity contributed to the resilience of farms, by retaining basic system structure and functions of agroforestry, and increasing product diversity and income.Graphical abstract

Lessons from a time of upheaval: COVID‐19 in South Asia

AbstractNumerous structural vulnerabilities put developing regions at a disadvantage as they confront the prospect of increasingly frequent extreme shocks. Typical of these regions, South Asia had several characteristics that suggested it would be badly hit by COVID‐19: a sizeable informal sector, growing inequalities in access to health services and social protection, and high levels of hunger and malnutrition. This Special Issue focuses on the South Asian experience through COVID‐19 and distills forward‐looking lessons for the developing world. Included papers point to the importance of strengthening individual resilience, building basic infrastructure and institutional capacity, and implementing inclusive social protection measures.

Can we breathe a sigh of relief now? The impact of First Republic Bank takeover by JP Morgan on the US equity markets

This paper investigates the effects of the takeover of First Republic Bank (FRB) by JP Morgan Chase on different US equity indices. Results from an event-study approach reveal that this acquisition exerts no significant impact on US equity markets, indicating that these markets in the US are relatively immune to extreme shocks and therefore, could act as a safe haven for investors during times of high uncertainty.