Volatility Spillovers Research Articles

A Comprehensive Study of Stock Market Volatility: Types, Determinants, and Measurement Methods

The Volatility, a critical aspect of financial markets, quantifies the uncertainty associated with asset price movements. This paper provides a comprehensive review of volatility, encompassing its definition, types, determinants, and measurement methods. Various factors, including macroeconomic indicators, market sentiment, liquidity, global events, and corporate actions, influence volatility. A range of methods, such as standard deviation, historical volatility, implied volatility, GARCH models, EWMA, and range-based volatility, are employed to measure volatility. Understanding volatility is essential for investors, traders, and risk managers to make informed decisions and manage risk effectively. Future research can delve into advanced volatility models, volatility spillovers, investor behavior, and the relationship between volatility and asset pricing.

Spillovers of good and bad volatility in Asian emerging markets: insights from global and regional perspectives

Spillovers of good and bad volatility in Asian emerging markets: insights from global and regional perspectives

Long-Run Volatility Memory Dynamics and Inter-Market Linkages in GCC Equity Markets: Application of DCC-FIGARCH Models

Abstract The study investigates volatility persistence, long-term memory and time-varying conditional correlations among the stock markets of the Gulf Cooperation Council (GCC) countries. Daily equity index data between 2012 and 2024 were analyzed using univariate fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) models to examine long-memory behavior and multivariate dynamic conditional correlation (DCC) models to assess conditional correlations between these markets. For each of the GCC equity markets, the analysis highlighted large degrees of long-memory and volatility persistence. Finally, the DCC model shows that strong and dynamic Intermarket links among the GCC, especially between KSA and UAE, exist and reflect significant volatility spillover from good economic ties. This study fills the gap in the literature by providing a comprehensive understanding of long-run volatility memory and inter-market associations in the GCC stock markets.

Dynamic Linkages Between U.S and Indian Equity Markets: An Empirical Study

In this study, we investigate the dynamic linkages between US and Indian equity markets by examining the daily Total Return Index values of NIFTY 50 and NASDAQ Composite over a decade ending October 2024. While the indices are not found to be cointegrated, suggesting potential long-term diversification benefits, they exhibit significant short-term correlations. Through various statistical analyses including regression, correlation, and variance tests, we find strong positive associations between the markets, with NASDAQ Composite movements explaining approximately 89% of variations in NIFTY 50 Total Return Index (TRI). The study reveals distinct volatility patterns, with the Indian market showing higher price fluctuations but stronger absolute growth compared to its US counterpart. Despite the strong correlation, both markets maintain unique characteristics influenced by their respective economic fundamentals. The findings demonstrate the evolving nature of market integration between developed and emerging economies, offering valuable insights for international portfolio diversification strategies and risk management in an increasingly interconnected global financial system. Keywords: Market Integration, Volatility Spillover, Portfolio Diversification, Stock Market Returns, Emerging Markets, Cross-market Correlation

Asymmetric Higher-Moment spillovers between sustainable and traditional investments

This study proposes a novel framework that decomposes volatility and higher-moment kurtosis into good and bad volatility/kurtosis—related to positive and negative shocks, respectively. Accordingly, we analyze the spillover effects of good and bad volatility/kurtosis between sustainable and traditional investments separately. During most periods, bad volatility spillovers dominate good volatility spillovers, whereas good kurtosis spillovers dominate bad kurtosis spillovers. However, during specific extreme events, such as Brexit and COVID-19, bad kurtosis spillovers dominate. This study’s findings can help investors in developing extreme risk management strategies and policymakers in preventing harmful shock transmissions across markets and fostering financial stability.

Impact of Volatility Spillovers upon Electric Utilities during the Russia-Ukraine Conflict

Impact of Volatility Spillovers upon Electric Utilities during the Russia-Ukraine Conflict

Analyzing risk contagion and volatility spillover across multi-market capital flow using EVT theory and C-vine Copula

There exists a potential interdependence among the United States markets, alongside an exceptional dependence on the East Asian stock markets. This transmission of risks is similarly evident in funds that are traded within markets. The current study seeks to uncover the pathways of risk contagion among various financial markets. This study examines how risks of funds spread across borders among China, the United States, and East Asia using Extreme Value Theory (EVT) and C vine copula quantile regression. It uses models such as AR (1), EGARCH (1, 1), Peak over Threshold (POT), and Copula methods to predict volatility events and correlation patterns during times of high volatility versus regular periods. Notably, the United States exhibits a risk transmission effect on the East Asian market compared to China. Furthermore, the findings indicate that, in times of high volatility, the risk spillover effect is comparatively weak, which is contrary to the situation of the US market. The study suggests that China's financial impact could progressively rise due to initiatives aimed at sector integration and enhancing financial independence. These findings have enlightening consequences for macro-prudential regulatory agencies, emphasizing the necessity of effective regulation to address cross-border risk spillovers. International investors can benefit from these results by incorporating risk-hedging strategies, accurately evaluating derivatives, and making wise investment decisions.

Measuring the effect of exchange rate variations on diverse economic sectors in the United States

ABSTRACT This study examined how exchange rates affect the US stock market and 27 industries. Using the TVP-VAR frequency-connectedness approach, it explores volatility spillover transmission and employs wavelet coherence analysis to uncover the co-movement between the USD and various sectors. The results show that the USD has been mainly volatile due to the 2008 stock market crisis, except for gold mining and select industries. During the crisis, the USD’s relationship with sectors exhibited out-of-phase patterns with long-term oscillations. This suggests that policymakers can use exchange rates to boost economic activity.

Spatial Price Transmission and Dynamic Volatility Spillovers in the Global Grain Markets: A TVP-VAR-Connectedness Approach.

The global food market's escalating volatility has led to a complex network of uncertainty and risk transmission across different grain markets. This study utilizes the Time-Varying Parameter Vector Autoregression (TVP-VAR)-Connectedness approach to analyze the price transmission and volatility dynamics of key grains, including wheat, maize, rice, barley, peanut, soybean, and soybean meal, and their dynamic spillover directions, intensity, and network. By integrating the TVP-VAR-Connectedness model, this research captures the time-varying variability and interconnected nature of global grain price movements. The main findings reveal significant spillover effects, particularly in corn prices, with prices of soybean dominating other grains while prices of peanut and corn experience higher external spillover effects from other grains. The conclusions drawn underscore the imperative for policymakers to consider a holistic perspective of all types of grains when addressing global food security, with this study providing valuable insights for risk management in the grain sector at both global level and country level.

Volatility spillovers and time-varying correlations across stock and commodity markets: An assessment during the epidemic crisis era

The financial and economic stability of the business world is greatly damaged by a number of crises over the years. These crises also change and affect stock markets around the globe. This study examines the nonlinear behavior of well-known stock markets in the US, Europe, Canada, and Asia (S&P 500, Nikkei 225, CAC40, DAX30, Bombay Stock Index, Hang Seng Index, and Canadian Stock Index). We also investigate the commodity indexes (Gold, Bitcoin, Brent Crude Oil, and West Texas Crude Oil). The transmission of returns and the volatility propagation pattern across stock-commodity markets during the COVID-19 era is analyzed using the VAR DCC MEGARCH model. The results demonstrate that correlations intensify and become more complicated during the COVID-19 period, especially between financial market indices and oil commodity indices. However, the volatility spillover statistics during the pandemic period show a definite rise in the uncertainty of financial market indices due to index instability. The analysis also shows that financial markets experience a substantial leverage effect during the pandemic crisis. According to the research, Bitcoin is crucial for reducing portfolio risk during the health crisis, and gold proves to be the best option for investors to diversify their financial risk.

Interlinkage between lending and borrowing tokens and US equity sector: Implications for social finance

Decentralized peer-to-peer social network-based platforms and financial products (i.e., tokens) are emerging phenomena in the area of social finance. This paper explores the return and volatility spillovers between Lending and Borrowing tokens and US equity sectors using the TVP-VAR framework. The results indicate a considerable static return and volatility spillovers between tokens and sectors. Most equity sectors are net transmitters of return and volatility spillovers, whereas tokens are more net receivers. Moreover, our findings show that dynamic return and volatility spillovers vary over time and experience a significant rise during the initial phase of the Russia-Ukraine conflict in the second quarter of 2022. Finally, we explore the portfolio implication of DeFi and find that the hedge effectiveness scores are significantly greater than zero at a 1 % significant level for all optimal weights and hedge ratios of sector/token pairs, suggesting that investors and portfolio managers should consider adding tokens to their equity sector portfolios and adjust the portfolio allocations over time to achieve diversification benefits. This paper explores the association between social finance-based emerging tokens and traditional equity markets, providing insightful information to policymakers and portfolio managers regarding diversification, hedging, and risk management.

Impact of central bank digital currency uncertainty on international financial markets

Is the impact of central bank digital currency (CBDC) uncertainty on investment and financial stability significant? To address this pivotal question in the era of CBDC development, we employ a combination of models: the time-varying parameter vector autoregression with stochastic volatility (TVP-SV-VAR); the Baba, Engle, Kraft, and Kroner multivariate generalized autoregressive conditional heteroskedasticity (BEKK-MGARCH) model, and wavelet quantile correlation (WQC) analysis. Based on the findings, macrofinancial variables significantly respond to CBDC uncertainty shocks, exhibiting time-varying, heterogeneous, and stage-specific characteristics. These effects are most pronounced in the short term, but diminish over the long term. Notably, the impact of CBDC uncertainty shocks on other financial variables exceeds the reverse impact. In addition, asymmetric volatility spillovers are found, suggesting complex interdependencies. The implication of the findings is that interactions between CBDC uncertainty and financial variables evolve across short-, medium-, and long-term horizons, emphasizing the need for central banks to adopt a flexible approach in CBDC design to alleviate market concerns and enhance overall financial stability.

Exploring Asymmetric Volatility Spillovers among Major Bombay Stock Exchange Indices

Exploring Asymmetric Volatility Spillovers among Major Bombay Stock Exchange Indices

Cotton market contagion: analyzing volatility spillovers across borders

PurposeThe paper analyzes the volatility transmission within the cotton markets by utilizing commodity futures prices from the USA, China and India, encompassing important global events that have significantly influenced the global cotton markets, like the China-USA trade dispute, the COVID-19 outbreak and the Russia–Ukraine conflict.Design/methodology/approachThe authors employ a volatility spillover measure developed by Diebold and Yilmaz (2009, 2012, 2014). Additionally, the methodology proposed by Baruník and Křehlík (2018), which divides the overall volatility spillover into short, medium and long-term segments has been used. To investigate the volatility connectedness, weekly (close-to-close) returns of the cotton futures contracts that are traded on the Chicago Board of Trade Dalian Commodity Exchange National Commodity Exchange of India (NCDEX), and Multi Commodity Exchange (MCX) are considered.FindingsThe paper identifies the presence of long-term volatility transmission among the three cotton futures markets. It demonstrates that a global shock like the Russia–Ukraine conflict has a greater impact on volatility in other markets than USA–China trade disputes. It also highlights the weakening role of the US cotton futures markets as a price leader for Indian and Chinese markets.Research limitations/implicationsSince only three major markets have been studied, the future studies can explore the interconnectedness by including other important markets including Brazil, Turkey, Bangladesh, etc. Further, the moderating role of relationship between other important variables such as cotton production, harvest, inventory, exchange rate, oil price, trade policies, etc. can be examined. Furthermore, the interconnectedness with the regional spot markets in India can also be examined to study how the volatility from the futures market can affect the volatility in the spot markets and vice-versa.Practical implicationsThe understanding of domestic food price volatility and its transmission from international to domestic markets is crucial for designing effective policies to address excessive volatility and protect vulnerable groups like producers, consumers, etc.Social implicationsThe findings emphasize on the substantial market dependence with the US and the Chinese markets which have a significant impact on the Indian markets with considerable implications for hedgers, producers and exporters, particularly during periods of higher volatility.Originality/valueThis study assesses the interdependencies among three major cotton-producing countries and the influence of factors like the USA–China trade tensions in 2018, the COVID-19 crisis and the Russia–Ukraine conflict in order to gauge the degree of volatility interconnection among these key players in the cotton market.

What happens when the disruptive asset meets the conventional asset? An analysis of cryptocurrency and REIT

ABSTRACT This study investigates the symmetric and asymmetric connectedness of recently developed disruptive assets – cryptocurrency and important conventional assets – Real Estate Investment Trusts (REITs) across various time scales and frequencies. We measure the strength and magnitude of returns and volatility spillover between REITs and cryptocurrency using both time series and machine learning approaches. Our findings support the dominance of conventional assets in the correlation with disruptive assets, demonstrated by REITs price movements’ destabilization of the correlation network. Interestingly, this destabilization is concentrated in bear markets, suggesting that while investors trade both cryptocurrencies and REITs in bull markets, investors are likely to hedge their cryptocurrency investments with REITs or avoid cryptocurrencies in bear markets. Our empirical findings provide implications for both asset allocation and policy making.

Volatility spillover between carbon market and related markets in time-frequency domain based on BEKK-GARCH and complex network analysis

With rising public attention to climate issues and sustainable development, the connection between carbon market and its related markets has become closer. Based on BEKK-GARCH method and complex network theory, this study tries to explore the volatility spillover effects and network topology among China's carbon market, non-renewable energy market, renewable energy market, high-tech market and climate policy uncertainty index in time-frequency domain. It is found that, firstly, there are significant asymmetric volatility spillover effects among the markets. Secondly, the spillover effects between markets are time-varying, especially during crisis periods in which they are significantly higher than that during the stable period. Thirdly, with time increasing, the closer the connection between markets. In the long run, the closeness of the connection is directly proportional to the spillover effect. Finally, climate policy uncertainty is the main risk source. For full sample, from perspective of frequency domain, non-renewable energy and carbon markets are the main risk recipients. Based on the findings, it should step up efforts on market monitoring and intervention during crisis periods, strengthen cross-market risk management, pay attention to climate uncertainty and time-frequency varying characteristics of volatility spillovers, so as to provide useful information for policy makers and investors.

Cross-Market Volatility Dynamics Among Crude Oil and South Asian Emerging Markets During the COVID-19 Pandemic.

The COVID-19 pandemic had profound impacts on the global economy, initially triggering a severe economic downturn due to widespread lockdowns, this outbreak generated various economic shocks, a major one being the oil price crash. A brief understanding of how this pandemic is affecting the financial markets, empirical analysis is done on three financial markets. Three stock exchange markets are selected from emerging markets in South Asia (Pakistan, China, and India). The world crude oil market is selected as a reference, to how it transmits volatility to South Asian markets during the COVID-19 period. We employ a bivariate BEKK-GARCH model for analyzing the volatility spillover from November 19, 2019, to March 31, 2021. All markets show negative coefficients with the oil market, indicating that the previous day’s shocks in oil prices reduce today's stock market volatility, with significant values for India and Pakistan. China has a negative coefficient while the rest of the two markets show a positive and significant coefficient means past volatilities in the stock market decrease today’s oil market volatility, likely indicating a reactive or adaptive market behavior to stock volatility. Generally, oil price changes have a significant impact on these markets, with both immediate effects (via ARCH terms) and persistent effects (via GARCH terms). The study's findings offer valuable insights for policymakers, as they help identify the direction of spillovers among interconnected economies. Additionally, portfolio investors can use this information to effectively allocate weights to different asset classes when making investment decisions.

COVID-19 and time-frequency spillovers between oil and sectoral stocks and portfolio implications: Evidence from China and US economies

This paper examines the volatility spillovers and the time-frequency dependence between crude oil and stock sectors in the U.S. and China using both wavelet coherence and asymmetric BEKK GARCH models. This study also investigates the impact of the COVID-19 pandemic on spillover effects and portfolio management. The results reveal strong positive co-movements between WTI oil and US sector stock returns at medium and low frequencies particularly in 2020Q1. We find significant long-term co-movements between oil and Chinese sector stock returns (64–128 days). Furthermore, the findings reveal a significant and asymmetric volatility transmission between oil and sector stocks in both the US and Chinese economies. The evidence of spillovers is more pronounced during the COVID-19 period. It is recommended that equity investors should hold more stocks than oil assets to minimize risk without reducing the expected return. Finally, hedging with oil is expensive for U.S. sectors during the pandemic but inexpensive for Chinese sectors.

“Volatility in a Mug Cup”: Spillovers among cocoa, coffee, sugar futures and the role of climate policy risk

Hot beverages have surged in popularity due to the increasing emphasis on healthy living among consumers. This trend extends upstream to prosper futures markets for essential raw materials within the supply chain. However, there is a significant gap in the literature concerning volatility spillovers within these futures markets, specifically cocoa, coffee, and sugar futures. Given the susceptibility of these raw material agricultural productions to weather conditions, an intriguing question emerges regarding the potential impact of climate policy uncertainty on the volatility spillover dynamics of these commodity prices. This study employs the TVP-VAR-DY methodology to quantify volatility spillovers among global mainstream cocoa, coffee, and sugar futures, then the Time-Varying Granger Causality test is deployed to uncover the influence of climate policy uncertainty. The findings underscore a pronounced and statistically significant spillover effect among the volatilities of coffee-related commodity futures, with the U.S. sugar futures market emerging as a pivotal conduit for risk transmission. In exploring the impact of climate policy uncertainty, it is discerned that global climatic patterns exert some unique and substantive influence on systemic risk spillovers. These insights provide a basis for stakeholders in the hot drink industrial chain to think about effective risk management mechanisms.

Beyond volatility: Systemic resilience and risk mitigation in interconnected commodity markets

In today's interconnected commodity markets, understanding volatility spillover dynamics is crucial. This research builds on Diebold & Yilmaz (2012, 2014) to estimate System Resilience, a vital measure for investors and policymakers. By using connectedness-based estimates, a unique stability score for each system component is constructed and aggregated to estimate overall System Resilience. This concept is applied to three key commodity segments: Energy, Agriculture, and Metals. Results show that most commodities, especially bullion, maintain moderate stability, offering investment alternatives even during periods of heightened systemic risk. The study also examines rolling estimates of System Resilience to identify early warning signals through increased standard deviation in successive resilience series. Tipping points indicate critical slowdowns where recovery to a stable state is hindered. Findings suggest that apart from the Global Financial Crisis (GFC), commodity portfolios remain relatively stable. The implications are significant for energy policymakers, traders, and financial investors. Early detection of warning signals supports strategic risk management. This research contributes to academic discourse and provides actionable insights, highlighting the importance of early warning indicators in enhancing financial resilience in an ever-evolving market landscape. It underscores the value of foresight in navigating global financial stability.