Upside Risk Spillovers Research Articles

Time-varying risk spillovers between renewable energy and Islamic stock markets: Evidence from the Russia-Ukraine conflict

This paper examines the downside and upside risk spillovers between renewable energy and Islamic stock markets before and during the Russia-Ukraine crisis. We employ the VAR-ADCC model and conditional value at risk (CoVaR) techniques to estimate downside and upside risk spillovers. The results show that the risk spillovers are asymmetric for the majority of cases, but the degree of asymmetry varies across distributions and sub-samples before and during the Russia-Ukraine conflict. Specifically, the downside and upside risk spillover magnitude is not significant between renewable energy and Islamic stock markets of Malaysia, Turkey, and India during the Russia-Ukraine war compared to the pre-crisis period. These results imply that renewable energy markets are not sensitivitive to the Islamic stock markets of the three countries (Malaysia, Turkey, and India) during the Russia-Ukraine war. Our findings also imply that the Canadian Islamic stock markets provide diversification opportunities against renewable energy during the extreme downside risk spillovers. These findings provide interesting insights to portfolio managers and policymakers regarding important decisions like portfolio construction, hedging, and market stability.

Risk spillovers and extreme risk between e-commerce and logistics markets in China

<p>We first utilized the Bayes positive diagonal BEKK generalized autoregressive conditional heteroskedasticity (Bayes-pdBEKK-GARCH) model to evaluate the risk spillovers between the e-commerce and logistics, then applied the adaptive Fourier decomposition method to measure the extent of these spillovers and detect structural changes. The results showed that there were structural breaks in both markets, which may lead to extreme risks. At last, we applied the GARCH-copula quantile regression model to analyze the extreme risks. We found that: (1) there were asymmetric volatility spillovers and positive correlations between them. (2) The dynamic risk spillovers exhibited heterogeneity over time. The logistics market had a smaller downside risk spillover, while the e-commerce market had a stronger upside risk spillover. (3) The study indicated that important events, such as the Chinese stock market crash, the Sino-U.S. trade friction, the COVID-19 epidemic, and the "either-or choice" monopoly policy of e-commerce platforms, had a significant influence on them, resulting in dramatic risk spillovers.</p>

The impact of the Russia-Ukraine conflict on the extreme risk spillovers between agricultural futures and spots

The ongoing Russia-Ukraine conflict between two major agricultural powers has posed significant threats and challenges to the global food system and world food security. Focusing on the impact of the conflict on the global agricultural market, we propose a new analytical framework for tail dependence, and combine the Copula-CoVaR method with the ARMA-GARCH-skewed Student-t model to examine the tail dependence structure and extreme risk spillover between agricultural futures and spots over the pre- and post-outbreak periods. Our results indicate that the tail dependence structures in the futures-spot markets of soybean, maize, wheat, and rice have all reacted to the Russia-Ukraine conflict. Furthermore, the outbreak of the conflict has intensified risks of the four agricultural markets in varying degrees, with the wheat market being affected the most. Additionally, all the agricultural futures markets exhibit significant downside and upside risk spillovers to their corresponding spot markets before and after the outbreak of the conflict, whereas the strengths of these extreme risk spillover effects demonstrate significant asymmetries at the directional (downside versus upside) and temporal (pre-outbreak versus post-outbreak) levels.

Measuring dependence structure and extreme risk spillovers in stock markets: An APARCH-EVT-DMC approach

A new APARCH-extreme value theory-dynamic mixture copula (APARCH-EVT-DMC) approach is proposed to investigate the dependence structure and risk spillovers. This method can illuminate the dynamic and complex tail dependence among stock markets. By analyzing the dynamic extreme risk spillovers in stock markets from China to G20 countries for the period from January 4, 2007 to February 7, 2023, firstly, our empirical results indicate that the EVT model is considerably better than the alternative model in fitting the tail distribution and the new DMC models outperform available copula models. Furthermore, the Russian and Argentine stock markets present the largest upside and downside risk spillovers for developed and emerging markets, respectively. Upside and downside risk spillovers from China to Canada and Saudi Arabia stock markets are the smallest for the two types of markets. Our results also provide evidence that upside and downside risk spillovers exhibit asymmetry, or more precisely, the downside risk spillovers are larger than the upside risk spillovers with exceptions in Brazil. Finally, the dynamic risk spillovers display heterogeneity and significant differences across countries.

Downside and upside risk spillovers between financial industry and real economy based on linear and nonlinear networks

The spillover effects between financial sectors and real economy in different risk levels are of concern for investors and regulatory authorities. Firstly, based on the sector returns of China, the VaRs at different quantiles are estimated by MVMQ-CAViaR model. Then, we construct linear and nonlinear risk spillover networks in downside, normal and upside cases by employing DY and nonlinear Granger causality methods. Finally, we analyze topological characteristics of linear and nonlinear risk spillover networks at the system and sector levels. The results show that linear and nonlinear risk spillover networks in different risk levels exhibit various topology properties, they are unevenly spread over each risk level. At the system-level, we observe that there is a significant difference between linear and nonlinear risk spillover networks in uniqueness and overlapping. At the sector-level, the financial sectors (such as DF and RE sectors) can form a certain spillover effect on sectors of real economy across quantiles, but the net spillover effects of the financial sectors are smaller than that of some real economy sectors. Finally, the crisis shocks have impact on risk spillover effects between financial sectors and real economy. In downside and upside cases, the spillover effects between sectors during crisis period is higher than that during pre-crisis period.

Geopolitical risk and commodity future returns: Fresh insights from dynamic copula conditional value-at-risk approach

In this study, we investigated the effects of geopolitical risk (GPR) on commodity future returns by computing the conditional value at risk (CoVaR) and delta CoVaR using time-varying and static bivariate copula models. Our results indicate that there is generally a positive dependence between commodity returns and changes in GPR. Empirical evidence also suggests that there are only upside risk spillovers from GPR to commodity markets. The delta CoVaR results locate the greatest systemic risk in heating oil and maize commodities. Future research avenues, as well as policy and practical implications, are outlined.

Asymmetric risk spillovers and its determinants in global equity markets

This study examines the asymmetric volatility spillover effect using realized measures across 22 international stock markets. The findings suggest a high connectedness among global equity markets, with downside risk spillover significantly higher than upside risk spillover over time. Besides, Europe has a higher connectedness than the American and Asia-Pacific markets, and risk contagion is from the European and American developed markets to the Asia-Pacific regions. The study further identifies implied volatility index and market liquidity as the main positive determinants of risk spillover, which are robust across different rolling window lengths and horizons. We also find that the explanatory power is more significant during financial crises.

Downside and upside risk spillovers between precious metals and currency markets: Evidence from before and during the COVID-19 crisis

This paper investigates the tail dependence dynamics and asymmetric risk spillovers between the futures of four important precious metals (gold, silver, platinum, and palladium) and seven leading currencies (EUR, GBP, JPY, CAD, AUD, CHF, and CNY) before and during the COVID-19 crisis using the time-varying-parameter copula and the conditional Value-at-Risk (CoVaR) method. The results show the symmetric dependence between currencies and precious metals before the COVID-19 crisis. In contrast, we show negative and positive tail asymmetric dependences during the pandemic crisis. The COVID-19 crisis significantly amplifies the magnitude of spillover effects among the studied markets where the AUD currency exhibits the largest transmission and reception of downside and upside spillover to/from most precious metals before and during the pandemic crisis. Currency investors and portfolio managers could use the obtained results to better hedge and manage their investment positions when markets are affected by health crises.

High-dimensional CoVaR risk spillover network from oil market to global stock markets—Lessons from the Kyoto Protocol

This paper explores the impact of the Kyoto Protocol by investigating the correlation and risk spillover between the crude oil market and the stock markets of 28 countries during its two commitment periods. Besides time-varying Copula-CoVaR models, the Adaptive Lasso-VAR model with oracle properties is employed in generalized variance decomposition, and a risk connectedness network is constructed to explore risk spillovers between the stock markets of various countries when the crude oil market is at risk. The results reveal positive correlations between the crude oil market and stock markets, which become weaker in the second commitment period than in the first. The crude oil market has both upside and downside spillover effects to most stock markets during both commitment periods, and the upside risk spillover effect is stronger than the downside effect. Overall, most non-signatories of the Kyoto Protocol are net receivers of risk spillovers when the crude oil market is at risk, while most signatories are net exporters of risk spillovers.

Dynamic risk spillovers from oil to stock markets: Fresh evidence from GARCH copula quantile regression-based CoVaR model

This study proposes a GARCH copula quantile regression model to capture the downside and upside tail dependence between oil price change and stock market returns at different risk levels. In the model, ten copulas are provided to measure the nonlinearity of the tail dependence with the marginal distribution built on the GARCH family models. Using daily price data of stock markets in ten important countries and Brent oil market, we estimate the downward and upward risk spillovers from oil to stock markets. The empirical results suggest strong evidence of risk spillover effects from oil to stock markets. Furthermore, downside and upside risk spillovers from oil to the Italian and German stock markets, and the Brazilian and Russian stock markets are the largest for developed countries and emerging market countries, respectively. The US and Mexican stock markets display the smallest downside and upside risk spillovers for two types of countries. We also find evidence that the downside risk spillovers are larger than upside risk spillovers, a finding which is consistent with the flight-to-quality phenomenon. Finally, the dynamic risk spillover effects show heterogeneity over time and are comparatively different for each country. Our results provide significant implications for portfolio managers and international regulators who want to optimize their investment portfolios and maintain stock market stability.

Downside and upside risk spillovers from commercial banks into China’s financial system: a new copula quantile regression-based CoVaR model

In this paper, we investigate the downside and upside risk spillovers from three kinds of commercial banks (state-owned commercial banks (SOCBs), joint-stock commercial banks (JSCBs) and city commercial banks (CCBs)) to China’s financial system by proposing a new copula quantile regression-based CoVaR model. We find that (i) the dynamic risk spillovers show heterogeneity over time, specifically that its downward trend is significant after the stock market disaster in 2015; (ii) JSCBs display the largest risk spillovers, indicating that JSCBs are the main contributors to systemic risk in China’s financial system; and (iii) the risk spillovers are not symmetrical, as the upside risk spillovers are smaller than the downside risk spillovers. Our results have crucial implications for financial regulators and investors who want to measure and prevent systemic financial risk and optimise their investment strategies.

Dynamic dependence and risk spillovers between RMB onshore spot and offshore NDF markets

ABSTRACT This paper investigates the dependence structure between the renminbi (RMB) onshore spot and offshore NDF markets using a GARCH-dynamic copula model. We document that the central parity reform in August 2015 marked a structural change in the dependence structure between the onshore spot and offshore NDF markets. Since the reform, the conditional correlation and tail dependence between the two markets have significantly increased and exhibited apparent time-varying patterns. We show that the difference between the central parity and spot rates and the degree of market segmentation between CNY and NDF markets are two crucial factors driving the time-varying CNY-NDF dependence. Furthermore, we find symmetric downside and upside risk spillovers between the two markets transmitted in both directions. The magnitude of spillovers has significantly increased since the reform. Our findings are particularly relevant for policy-making and portfolio risk management.

Extreme dependence and risk spillover across G7 and China stock markets before and during the COVID-19 period

PurposeThe paper analyzes downside and upside risk spillovers between stock markets of G7 countries and China before and during the COVID-19 pandemic.Design/methodology/approachBy using VAR-ADCC models and conditional value at risk (CoVaR) techniques, downside and upside risk spillovers between stock markets of G7 countries and China are analyzed before and during the COVID-19 pandemic.FindingsThe results suggested existence of a significant and asymmetrical two-way risk transmission between majority of pair markets, but the degree of asymmetry differs according to the use of the entire cumulative distributions or distribution tails. Downside and upside risk spillovers are significantly larger before the COVID-19 pandemic in all cases except between CAC 40/DAX and S&P/SSE pairs.Originality/valueThe paper used CoVaR and delta-CoVaR to investigate the downside and upside spillovers between stock markets of G7 countries and China before and during the COVID-19 pandemic.

Downside and upside risk spillovers between green finance and energy markets

This paper aims to build an incentive to mobilize the financial resources needed to accelerate the transition to a climate resilient economy. To this end, we examine the dependence structure using copulas theory and then the risk transmissions between green financial products and the energy commodity market index. This methodology provides opportunities to investors in green finance to protect their portfolios against downside or upside risk by taking long or long position. In our empirical study for the period July 2014 to September 2020, marginal equities show a long memory in the volatility process captured by FIGARCH model, justifying by the various crisis, the last of which is the ongoing COVID-19 pandemic. Using VaRs and CoVaRs measures, we find that green instruments (mainly the green bonds) are significantly affected by substantial price spillovers from energy commodity market during critical periods. Many obstacles to set up investments’ opportunities are discussed.

How do USDA announcements affect international commodity prices?

Our event study results show that Chinese soybean futures and to a lesser extent Chinese corn futures react to the release of World Agricultural Supply and Demand Estimate reports. Second, using a copula based Conditional Value at Risk (CoVaR) approach we find strong evidence that risk spillovers from U.S. to Chinese futures markets are much larger on report release days than on pre and post-release days. In addition, our results indicate that risk spillovers from U.S. to Chinese futures markets, irrespective of trading day, are asymmetric in nature, with upside risk spillovers much higher than downside risk spillovers.

European travel and leisure sector and uncertainties: A risk spillover analysis

The study analyzes the impact of uncertainty changes on the European travel and leisure sector stocks. We use economic policy uncertainty, geopolitical uncertainty, financial market uncertainty, and crude oil price uncertainty as uncertainty variables. We also analyze the extreme risk transmission from the uncertainty variables to the European travel and leisure sector stocks using the copula-based conditional Value-at-Risk (CoVaR) approach. The findings provide evidence of a significant impact of uncertainty variables on travel and leisure stocks mainly on the lower quantiles. The findings also indicate the significant downside and upside risk spillover effect from the extreme upside and downside movements in the uncertainty variables, respectively. The findings have implications for individual investors, portfolio managers, and institutional investors.

Tail dependence risk and spillovers between oil and food prices

We examine the nonlinear dependence dynamics and downside and upside risk spillovers between oil prices and world food prices captured by a world food price index and its subcategories of dairy, cereals, vegetable oil, and sugar. We draw our empirical results using static and dynamic bivariate copulas, Value-at-Risk (VaR) and conditional VaR (CoVaR) methods. Our empirical findings reveal that oil prices and aggregate food prices, as measured by the world food price index, independently move during market upturns and downturns. However, lower and upper tail dependence is observed between oil prices and cereals, vegetable oil, and sugar prices. We also identify upside and downside asymmetric risk spillovers from individual food commodities to oil and from oil to food commodities. Oil prices most strongly affect sugar and vegetable oil prices (downside and upside) whereas oil prices are most strongly impacted in the downside and upside by vegetable oil and sugar prices, respectively. The implications of the results are discussed.

Time‐frequency analysis of risk spillovers from oil to BRICS stock markets: A long‐memory Copula‐CoVaR‐MODWT method

AbstractWe document the risk spillovers from oil to BRICS stock markets using a relatively novel long‐memory Copula‐CoVaR‐MODWT method from the time and frequency domain. Overall, the empirical results show that there are significant risk spillovers with time‐varying and heterogeneous characteristics. More importantly, we find that the heterogeneity among countries depends on the degree of oil dependence, energy policy and risk management strategy. Furthermore, we reveal there exist significant long‐ and short‐term risk spillovers and that the long‐term spillover effects are generally lower than the short‐term ones. Finally, we confirm that downside and upside risk spillovers are asymmetric.

Dependence and risk spillovers between green bonds and clean energy markets

In this study, we first examine the dynamic dependence structure between green bonds (GBs) and several global and sectoral clean energy (CE) markets by using several time-invariant and time-varying copula approaches over the period from 5 July 2011 to 24 February 2020. We then apply conditional value-at-risk (CoVaR) and delta CoVaR to capture downside and upside risk spillovers from CE to GB, and vice versa. Our empirical analysis shows that there is positive time-varying average and tail dependence between GBs and CE stock markets. Moreover, extreme downward or upward movements in the CE stock market have a spillover effect on the GB market, and vice versa. Furthermore, the risk spillover between these markets is asymmetric. These results make an important contribution to policymakers and environmentally friendly investors with GB positions by adding unexpected tail losses. It is critical for the GB investors to invest their capital effectively in economic activities that are consistent with a low-carbon economy.

Characterizing electricity market integration in Nord Pool

We empirically study market integration and the propagation of shocks in the interconnected market of Nord Pool. We document an increasing trend towards market integration over recent decades in Nord Pool and identify clear cycles accounting for greater integration (larger transmission of shocks) in the cold seasons. Greater market integration permits a higher level of risk sharing between electricity markets and, as a result, can be expected to reduce the probability of energy crises and energy shortages occurring in any given market. Furthermore, we differentiate between shock propagation in the two tails of the price variation distribution and, so, distinguish downside risk from upside risk spillovers. Market spillovers following price increments are transmitted to a greater degree than are those following price reductions in the market. We also document asymmetries related both to the size of the transaction area and as to whether a given area behaves as a net-exporter or net-importer of electricity. For instance, we show that the larger the transaction area, the smaller are the volatility shocks on prices that it receives from the rest of the system.