Volatility Shocks Research Articles

Nexus among Economic Uncertainty, Geopolitical Risk, Oil Price Volatility and Economic Complexity in Saudi Arabia: Fresh Insights from Wavelet Local Multiple Correlations

Enhancing future economic growth through higher economic complexity is vital for sustainable growth strategies. Even though most studies are concerned with the key factors governing the economic complexity process, there needs to be more studies about the impact of risk factors on economic sophistication evolvement, particularly for oil-rentier economies. The present paper intends to delve into the dynamic connectedness between some risk factors, such as economic, political, and geopolitical risks and oil price volatility and the progress of economic complexity. We utilized quarterly data (1995Q1-2021Q4) for Saudi Arabia as a heavy oil-rich economy. As the first study to delve into the short-long term connections at various time scales and frequencies within a multivariable setting, we contribute to the literature by offering a spotless picture of economic complexity risk factors. In doing so, we resort to a novel wavelet local multiple correlation method, which can explore the varying patterns of time periods in the interconnections between the variables. Our findings disclose that oil volatility, geopolitical risk, and global uncertainties are positively connected to economic complexity over the long run. In contrast, their short-term effect is weak and mostly insignificant. These results indicate the resiliency of the Saudi economy to external to oil volatility shocks and intensification of global and local uncertainties. These outcomes offer policymakers new insights and prominent policy recommendations when designing economic strategies to achieve higher economic sophistication and sustainable growth.

Risk parity portfolio optimization under heavy‐tailed returns and dynamic correlations

Risk parity portfolio optimization, using expected shortfall as the risk measure, is investigated when asset returns are fat‐tailed and heteroscedastic with regime switching dynamic correlations. The conditional return distribution is modeled by an elliptical multi‐variate generalized hyperbolic distribution, allowing for fast parameter estimation via an expectation‐maximization algorithm, and a semi‐closed form of the risk contributions. A new method for efficient computation of non‐Gaussian risk parity weights sidesteps the need for numerical simulations or Cornish–Fisher‐type approximations. Accounting for fat‐tailed returns, the risk parity allocation is less sensitive to volatility shocks, thereby generating lower portfolio turnover, in particular during market turmoils such as the global financial crisis or the COVID shock. While risk parity portfolios are rather robust to the misuse of the Gaussian distribution, a sophisticated time series model can improve risk‐adjusted returns, strongly reduces drawdowns during periods of market stress and enables to use a holistic risk model for portfolio and risk management.

Dynamic spillovers of various uncertainties to Russian financial stress: Evidence from quantile dependency and frequency connectedness approaches

Being geopolitically exposed, the Russian financial sector is vulnerable to various ­uncertainties. The aim of the article is to examine the quantile movements and dynamic connectedness of uncertainty indices with the financial stress index of Russia employing the cross-quantilogram (CQ), recursive cross-quantilogram (R-CQ) and TVP‑VAR dynamic connectedness using monthly data from July 2011 to August 2023. It is found that for Tweeter-based Economic Uncertainty (TEU) and Global Economic Policy Uncertainty (GEPU), there is strong positive dependence on the Russian Financial Stress Index (RFSI) in the bearish states of market in the initial memory, and the strength of this positive spillover effect gradually wilts towards longer memory structures. Unlike the GEPU, Russian Economic Policy Uncertainty (REPU) has long-lasting heterogeneous spillover effects on RFSI. Though there are significant positive, as well as negative, spillover ­effects of Global Geopolitical Risk (GGPR) on RFSI in the initial memory, across the longer­ memory structures these entire heterogeneous effects wash out. However, Russian Geopolitical Risk (RGPR) have long-lasting heterogeneous spillover effects on RFSI, unlike GGPR. GEPU, GGPR and RGPR were the net transmitters while RFSI, TEU and REPU were net receivers of volatility shocks. Since RFSI shows resilience over the long-term horizon to global geopolitics and economic uncertainty, investors are advised to keep patience and hold their capital/investment up to a minimum of 1 year in Russian financial system in order to be rewarded with positive returns.

An Empirical Analysis of Price Volatility of Turmeric in India

The objective of the present study aimed at examining the price volatility of turmeric in major markets of the state. The secondary data on monthly modal prices for the period January 2011 to December 2023 were collected from Duggirala and Kadapa (Andhra Pradesh), Nizamabad and Warangal (Telangana), Sangli (Maharashtra) and Erode (Tamil Nadu) by using purposive sampling method. The findings of ARCH-GARCH analysis revealed that the price series of Duggirala, Nizamabad and Erode markets showed the presence of price fluctuations as indicated by the sum of Alpha and Beta co-efficient which were nearer to one whereas in the remaining markets, the volatility shocks were not quite persistent.

Stylized facts of metaverse non-fungible tokens

Non-Fungible Tokens (NFTs) within the metaverse represent a rapidly emerging sector in the digital asset space. This paper provides a comprehensive review of the metaverse’s history and an analysis of the stylized facts of five metaverse NFTs: Axie Infinity, Decentraland, Enjin Coin, Theta Network, and The Sandbox. We examine market efficiency, volatility clustering, leverage effects, and the return-volume relationship. Our key findings show that all NFT returns exhibit kurtosis values significantly exceeding the standard value of three, indicating more peaked and heavier-tailed distributions than a normal distribution. Autocorrelation analysis reveals statistically insignificant results, suggesting minimal influence of past returns on current returns. The Hurst exponent fluctuates between 0.3 and 0.8, indicating relative inefficiency in log returns with varying degrees of trend reinforcement and anti-persistence. The GARCH(1,1) model confirms the presence of volatility clustering, with high persistence of volatility shocks over time, and most NFT returns exhibit a negative leverage effect, where negative returns decrease volatility. These findings provide critical insights for investors, content creators, and policymakers, emphasizing the need for innovative strategies and regulatory considerations in this evolving ecosystem. A comparative analysis using alternative metaverse-related assets from Bloomberg and Yield Guild Games enhances the robustness of our findings, enriching the academic discourse on digital assets and laying the groundwork for future research in metaverse NFTs.

Are Indian markets insulated from the impact of cryptocurrencies? Unveiling the volatility linkages through multi‐index dynamic multivariate GARCH analysis

AbstractThis paper investigates the dynamic relationships between the volatility of Bitcoin and major Indian stock market indices. Employing a dynamic conditional correlation–generalized autoregressive conditional heteroskedasticity (DCC‐GARCH) model, we explore how volatility shocks and information flow influence the correlations between these asset classes. Our findings reveal a key characteristic: volatility spillovers tend to be short‐lived, indicated by a relatively low DCC‐GARCH parameter (dcca1). This suggests that while a surge in volatility in one market might lead to a temporary increase in correlation with the other, this heightened correlation is unlikely to persist for extended periods. However, the model also highlights a high DCC‐GARCH parameter (dccb1), signifying that the correlations themselves are responsive to new information. This implies that volatility linkages can adjust rapidly in response to market events or economic data releases. To enhance accessibility for a broad audience, we translate these findings into economic intuitions. We illustrate how the model can be interpreted through real‐world examples, such as the impact of sudden policy changes in India or global market flash crashes. By understanding the short‐lived nature of volatility spillovers and the responsiveness of correlations, investors in the Indian markets can make more informed decisions when considering the potential influence of Bitcoin's volatility while contributing to a deeper understanding of the dynamic interactions between cryptocurrency and traditional financial markets in the Indian context.

The impact of COVID-19 stringency measures on emerging stock market stability: Does economic resilience matter?

PurposeThe current research aims to investigate the impact of coronavirus 2019 (COVID-19) evolution, government stringency measures and economic resilience on stock market volatility in the Middle East and North African (MENA) emerging markets. Other macroeconomic factors were also taken into account.Design/methodology/approachBased on financial data from 10 selected MENA countries, we tested an integrated framework that has not yet been explored in prior research. The exponential generalized autoregressive conditional heteroskedasticity (E-GARCH) was adopted to analyze data from March 2020 to February 2022.FindingsOur research illustrates the direct and indirect effects of the virus outbreak on stock market stability and reports that economic resilience could alleviate the volatility shock. This finding is robust across the various proxies of economic resilience used in this study. We also argue that the negative impact of the pandemic on equity market variation gets more pronounced in countries with higher level of stringency scores.Practical implicationsPolicymakers ought to strengthen their economic structures and reinforce the economic governance at the national level to gain existing and potential investors’ trust and ensure lower stock market volatilities in times of crisis. Our study also recommends some key economic factors to consider while establishing efficient policies to tackle unexpected shocks and prevent financial meltdowns.Originality/valueOur findings add to the evolving literature on the reaction of economic and financial markets to the sanitary crisis, particularly in developing countries where research is still scarce. This study is the first of its kind to investigate the stock market reaction to stringency measures in the understudied MENA region.

The technological origins of the decline in labor market dynamism

In the last decades, there has been a marked decline in the job flows to and from unemployment and between employment. We ask whether and how technological change can account for his secular decline in labor market dynamism. We propose a theory that focuses on the determinants of technology broadly defined: 1. the complementarity between worker skill and firm productivity; and 2. the volatility in productivity shocks; and 3. search frictions. We derive job flows in a sorting model with search frictions and endogenous search effort both on and off the job, as well as shocks that lead to mismatch. We quantify our model using the US data and find an increase in the complementarity between labor and technology, a decline in the frequency and volatility of productivity shocks, and a decline in the match efficiency as well as an increase in the search costs. The changing nature of these features of the technology contributes to the secular decline in labor market dynamism.

Trends and cycles during the COVID-19 pandemic period

We perform several trend-cycle decompositions through the lens of two unobserved components models, herein estimated for Portugal and the euro area. Our procedure copes with the COVID-19’s consequences by explicitly considering potentially larger second moments during that period. This is achieved through a set of pandemic-specific shocks affecting only the 2020–21 period and embedded into estimation through a piecewise linear Kalman filter. Our methodology generates negligible historical revisions in key smoothed variables when the sample period is expanded until 2021:4, since pandemic shocks absorb a great deal of data volatility with minimal impacts on filtered data revisions or estimated parameters. Furthermore, non-pandemic shock volatility remains largely unaffected by the pandemic period. Innovations affecting the cycle in our preferred model are the key propellers of GDP developments during the COVID-19 pandemic period.

Navigating APEC Countries: TVP-VAR Insights into Developed and Emerging Stock Markets

The interdependence of stock markets provides important discernment into the behavior of the larger international financial markets. This study investigates magnitude and directional volatility spillover patterns among developed and emerging countries within the APEC bloc, utilizing the TVP-VAR model. The findings indicate that Russia (15.06%), Vietnam11.64%), and Thailand (11.57%) are identified as major transmitters, and Malaysia (-28.95%), Philippines (-9.28%), China (-9.53%) are major receptor of the volatility spillovers in APEC emerging countries. In APEC-developed countries, the United States (56.85%) and Canada (42.6%) are major transmitters, and Japan (-34.02%) and Australia (-53.54%) are identified as a major receptor of the spillover. Moreover, COVID-19 was the most significant crisis, with the highest volatility spillover identified in the APEC bloc's developed and emerging economies. The discoveries have substantial ramifications, offering valuable insights into optimal investment strategies by identifying patterns, magnitudes, and directions of economic volatility shocks.

The nexus between mineral, renewable commodities, and regional stock sectors during health and military crises

We examine the return and volatility spillover dynamics and their portfolio implications among regional stock super-sectors, mineral (Energy, Industrial, and Precious Metals), and renewable (Agricultural and Livestock) commodities. To this end, we apply the time-varying parameter-vector autoregressive (TVP-VAR) model to analyze the intersectoral-commodity linkages separately in four major regions (Europe, Eastern Europe, Asia Pacific, and North America). The empirical results show that stock market sectors dominate the spillover transmission mechanism, while commodities emerge as net recipients of shocks. Moreover, we find heterogeneous connectedness patterns between stock sectors and commodities, predominantly running from the Energy and Basic Resources stocks to the Energy and Industrial Metals commodities. Thus, energy commodities play a more vital role as a spreader of volatility shocks amid the COVID-19 pandemic and the wake of the Russia-Ukraine war. The optimal portfolio weights reflect two episodes of “flight to safety” towards all commodities except Energy, triggered by the pandemic and the war, as investors decrease their fund allocation to stocks in favor of commodities.

Quantile-based extended joint connectedness between trade policy uncertainty and Gulf Cooperation Council Islamic sectoral volatility

This study quantifies the shock transmission mechanism between the trade policy uncertainty (TPU) index and Sharia-compliant stock sectoral conditional volatility in the Gulf Cooperation Council (GCC) countries. We employ a comprehensive analysis that includes the time-domain extended joint and frequency-domain quantum vector autoregressive (QVAR) frameworks. The time-domain QVAR demonstrates that TPU causes the most substantial shocks to utility sector volatility. Investors in the GCC must strategically allocate their investment portfolios to the consumer services and energy industries, which are less vulnerable to the TPU shock. The results show that TPU shocks provide more error variances to the long-term and bullish conditional volatility (or higher quantiles) of the utility, real estate investment trust funds (REIT), healthcare, and industry sectors. Conversely, during bearish volatility conditions (lower quantiles), TPU shocks result in higher shocks in the conditional volatility of the utility, REIT, finance, and industrial sectors. Long-term investors should diversify their portfolios to mitigate risk in the utility, REIT, and industry sectors through strategic investments in consumer service sector. However, in the short term and during both bearish and bullish sectoral volatility, the consumer service and industry sectors are more vulnerable to TPU shocks. Regarding diversity, the REIT, energy, and financial sectors are the least affected by TPU shocks, offering a protective buffer in the short term and during periods of higher and lower GCC Islamic sectoral volatility.

Does Bullion Appeal As A Hedge Or A Safe Haven For Oil Wild Ride During The Russia-Ukraine War? A Wavelet Approach

In this research, we empirically evaluate whether Indian bullion market instru- ments glitter as a hedge or a safe haven against oil volatility shock before and during the Russia-Ukraine war. The wavelet coherence mechanism is used to encapsulate the hedge or a safe haven potential over the time-frequency horizon. We find evidence that gold and silver act as a medium horizon investment hedge for crude oil during pre - and invasion periods. However, the results unveil that gold and silver act as a weak safe haven for crude oil during the turmoil period, partly consistent with the prior research findings. The results further show that there is a positive relationship between gold and oil at the beginning of the war and at intervals, 29/06/2022 to 15/07/2022, illustrating that gold acts as a diversifier for crude oil

Foreign Exchange Futures Trading and Spot Market Volatility in Thailand

This paper investigates how the introduction of foreign exchange futures has an impact on spot volatility and considers the contemporaneous and dynamic relationship between spot volatility and foreign exchange futures trading activity, including trading volume and open interest in the Thailand Futures Exchange context, with the examples of the EUR/USD futures and USD/JPY futures. The results of the EGARCH (1,1) model show that the introduction of foreign exchange futures decreases spot volatility. It also increases the rate at which new information is impounded into spot prices but decreases the persistency of volatility shocks. A positive effect of unexpected trading volume and a negative effect of unexpected open interest on contemporaneous spot volatility are in line with the VAR(1) model results of the dynamic relationship between spot volatility and foreign exchange futures trading activity. With the impact on spot volatility caused by unexpected open interest rate being stronger than by unexpected trading volume, foreign exchange futures trading stabilizes spot volatility.

Volatility and Return Connectedness Between the Oil Market and Eurozone Sectors During the Financial Crisis: A TVP-VAR Frequency Connectedness Approach

This paper analyzes the returns and volatility connectedness between oil prices and Eurozone sector returns during the global financial crisis. We employ the TVP-VAR frequency connectedness approach with daily data of Brent prices and 18 Eurozone supersector indices from 15 November 2014 to 24 November 2023. Our results show a high average connectedness of the returns and volatilities. Industrial Goods are the largest transmitter contrariwise Media supersector is the largest receiver of shocks on returns. The same finding is for volatility, the result shows that Industrial Goods and Services transmit the highest risk in contrast, the Media has the highest receiver volatility indices. The time-varying connectedness (TCI) of returns and volatilities in both show a drastic increase in March 2020. This increase is a result of COVID-19. Whereas, there has been no rise in connectivity following Russia’s invasion of Ukraine. Our result highlighted that Brent was a net receiver of volatility shocks during the Russian invasion of Ukraine.

Modeling and forecasting the volatility of some industry development indicators in Ethiopia using multivariate GARCH models

Industry development indicators refer to a set of measures used to assess the performance and growth of industries. The main aim of this study was to assess the relationship between industry development indicators in Ethiopia using a multivariate GARCH model based on World Bank data from 1982 to 2021. A time series technique using annual data for the period 1982–2021 is utilized, and multivariate generalized autoregressive conditional heteroscedasticity was performed for volatility modeling. The results of the diagonal BEKK (1, 1)-GARCH model showed that there is strong evidence for a GARCH effect and the presence of a weaker ARCH effect, Equations show a statistically significant co-variation in shocks, which depends more on their lags than on past errors. Consequently, development indicator shocks are influenced by past information. The cross-volatility effects are higher than the own-volatility effects in Industry GDP, manufacturing GDP, and manufacturing exports. However, past volatility shocks in industry growth have less effect on cross-volatility than its own volatility shock. The implication of the study is that both domestic policymakers and development partners should support and motivate the growth of manufacturing sectors and manufacturing exports, since this is a necessary condition for promoting industry growth.

Dynamic impact of the US yield curve on green bonds: Navigating through recent crises

We examine the effect of the US yield curve on the global green bond markets at the forefront of the climate change fight. For this purpose, we compute three components (level, slope, and curvature) of the US yield curve based on daily data of the treasury yields with several maturities from January 2009 to June 2022 and employ country-level S&P green bond indices. Our dynamic network analysis shows that the level component of the yield curve is more influential in transmitting return and volatility shocks to green bonds, while curvature is primarily absorptive. The European, the US, and Hong Kong green bonds are the leading players in shock propagation. Both return- and volatility spillovers are time-varying and remain high during periods of systemically important events, especially COVID-19 and the Russia-Ukraine war, supporting the Global Financial Cycle Hypothesis. The war also changes the net behaviors (transmitter versus receiver) of the components and the indices. Investors and issuers of green bonds are advised to keenly observe the shape of the US yield curve and systemic events for better decision-making regarding investment horizons and contagion risk management.

Volatility Shocks, Leverage Effects, and Time-Varying Conditional Skewness

Abstract I use GARCH-X specifications for stock index returns to investigate the connections between conditional skewness and variance shocks computed from realized variances. The evidence indicates that the conditional skewness of index returns is strongly influenced by variance shocks and displays a remarkable degree of persistence. Indeed, variance shocks not only drive changes in conditional skewness, but also act as a common factor that generates substantial negative correlation between contemporaneous changes in the conditional volatility and conditional skewness of index returns. The resultant linkages between these conditional moments could have important implications in asset pricing, portfolio selection, and risk management applications.

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

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

Volatility Dynamics of Base Metal Futures: Empirical Evidence from an Emerging Economy

The paper examines the leverage effects and the spillover effects in the base metal cash and futures market. The study also attempts to find the trend and the pattern of volatility clustering in the base metal markets of India. Further, the significance of the risk premium and the possible downside risk of the market are also examined. The study confirms that unlike aluminium futures market, leverage effect is found for copper futures traded at MCX, India. Similar to aluminium, it is evident that the market advances generate larger volatility than the market turbulence in the cash and futures market of nickel. The study finds that the variance term (ξ) is not statistically significant for both cash and futures markets, which indicates that the risk premium of the asset is not significant to hedge. Further, unlike copper and aluminium, short-run volatility spillover is absent from the futures to the cash market of nickel. The paper concludes that the long-run volatility shock of futures has a persistent effect on the cash market of aluminium, copper and nickel and vice-versa. Future research might address the cross-volatility spillover between the base metal futures market. Further, the spillover between the Indian and London base metal futures markets is left for future research.