Time-varying Copula Research Articles

Dynamic correlation between hog futures and industry chain: an empirical study based on time-varying copula model

Dynamic correlation between hog futures and industry chain: an empirical study based on time-varying copula model

Chinese Crude Oil Futures and Sectoral Stocks: Copula-Based Dependence Structure and Connectedness

Abstract China launched its first crude oil futures on 18th March 2018, with the primary objective of introducing its own hedging instrument in China. Our study responds to this objective by investigating the dependence structure based on time-variant and time-invariant copula, connectedness in various market conditions, and hedging effectiveness of oil futures with eleven GICS sectors in China using daily returns data from 19th June 2019 to 24th February 2024. Findings suggest that time-varying copula is a best fit for all the GICS sectors in China except for the Energy. Similarly, the results of time-varying (TVP-VAR) connectedness indicate that the oil futures are the net receiver of shocks in total, short (1–5 days), and long-time (5 to infinity) spillover. The oil futures can better offset portfolio losses during the down market than normal and up markets. Lastly, based on TVP-VAR, the hedging ratio, optimum portfolio weights, and hedge effectiveness are calculated. We find a positive hedge ratio across various pairs of assets, which reduces the assets’ volatility. Moreover, hedge ratios and optimum portfolio weights are non-constant over time. Our results significantly contribute to the scant literature on Chinese crude oil futures and have implications for investors and policymakers.

Dynamic Dependence between Oil and Stock Markets: International Evidence with Stochastic Copula Approach

Modeling the dependency structure between variables has recently received increasing attention in many disciplines, especially finance and economics. In this study, the dependence between oil prices and stock markets is investigated through the stochastic copula approach, which is a class of time-varying copulas. This model enables to capture whole dependency between variables dynamically. Unlike time-varying copulas, it takes into account the latent process as well as observations in modeling dependency and thus evaluates the dependency structure in a more comprehensive framework. Empirical findings suggest that dependency between oil and stock markets evolve over time. There is a symmetric dependence between oil and the UK stock market, but the relationship between oil and the US stock market is measured by upper tail dependence. This indicates that oil and the US stock market are more likely to move together during periods of market uptrend.

Dynamic Dependence and Hedging of Stock Markets: Evidence From Time-Varying Copula With Asymmetric Markovian Models

Dynamic Dependence and Hedging of Stock Markets: Evidence From Time-Varying Copula With Asymmetric Markovian Models

A Structural Reliability Analysis Method Considering Multiple Correlation Features

The paper analyzes the correlation features between stress strength, multiple failure mechanisms, and multiple components. It investigates the effects of different correlation features on reliability and proposes a method for structural reliability analysis that considers the joint effects of multiple correlation features. To portray the stress–strength correlation structure, the Copula function is utilized and the influence of the correlation degree parameter on reliability is clarified. The text describes the introduction of time-varying characteristics of structural strength and correlation parameters. A time-varying Copula is then constructed to calculate the structural reliability under the stress–strength correlation characteristics. Additionally, a time-varying hybrid Copula is constructed to characterize the intricate and correlation features of multiple failure mechanisms and components. The article proposes the variational adaptive sparrow search algorithm (VASSA) to obtain optimal parameters for the time-varying hybrid Copula. The effectiveness and accuracy of the proposed method are verified through actual cases. The results indicate that multiple correlation features significantly influence structural reliability. Incorporating multiple correlation features into the solution of structural reliability yields safer results that align with engineering practice.

Global shocks and local connectedness: evidence from dynamic tail risk interdependences among Chinese regional carbon markets

ABSTRACT This paper analyzes tail risk interdependence among four regional carbon markets in China to investigate the effect of exogenous shocks on local connectedness. We utilize a time-varying copulas approach and find that: (i) the Shenzhen carbon market has the highest level of tail risk due to the lower liquidity and weaker resilience. (ii) Carbon markets where prices are more responsive to information have higher levels of tail risk dependence. (iii) Compared to the China – US trade dispute and the armed conflict in Europe, the shocks from the extreme weather and the COVID-19 outbreak can prominently strengthen tail risk.

An improved agricultural household margin insurance scheme with insured–insurer risk protection: a time-varying copula approach

Agricultural household margin insurance (AHMI) is a crop insurance scheme that considers a farmer's trading capability. The scheme examines farmers' losses in terms of both agricultural input and output using the farmer exchange rate index. In this article, we improve the previous design of the AHMI [A. Ahdika, D. Rosadi, A.R. Effendie, and Gunardi, Household margin insurance of agricultural sector in Indonesia using a farmer exchange rate index, Agric. Finance Rev. 81 (2020), pp. 169–188.] proposed to be implemented in Indonesia – which only provided risk protection for companies – by broadening the formula of losses so that risk protection covers both farmers and companies. We also provide premium payment scenarios, both seasonally and non-seasonally. We employ a time-varying Student-t copula with the extended dynamic parameter to identify the dynamic dependency between the indexes involved in determining the loss variable. We also determine the premium rate for the insurance scheme with various payment scenarios and investigate the implications for farmer survival and company management. Examining the farmer exchange rate index data of Indonesia, the empirical results show that the improved AHMI using a time-varying copula approach produces more reasonable loss estimates and various premium payment options with low premium rates for farmers, compared to either the previous AHMI design or the current crop insurance program in Indonesia.

Interconnectedness and risk profile of hydrogen against major asset classes

This study examines the interconnectedness and risk profile of hydrogen in comparison to other assets from December 2019 to April 2022, using a time-varying copula approach. The findings reveal that hydrogen's relationships with conventional commodities and markets can shift significantly during extreme uncertainty. The COVID-19 pandemic had a marked impact on hydrogen's market connections. However, hydrogen did not uniformly increase linkages, highlighting its distinct profile even as an energy commodity. Despite its sensitivity to crises, hydrogen's reactions were varied, emphasizing the necessity for customized interpretations. Calculation of value-at-risk, conditional value-at-risk and Delta CoVaR demonstrated that green bonds exhibited the lowest investment risks across frequencies, which can be attributed to factors such as government support and growing demand. In the case of hydrogen, the risk metrics were inconsistent, reflecting its status as an early-stage adoption asset with niche applications in transportation and industry. These findings have key implications for managing risk associated with energy transition and for facilitating the adoption of hydrogen. The empirical insights provided herein can aid policymakers in developing supportive regulations and incentives to expedite the scaling of hydrogen. Moreover, Investors can leverage hydrogen's risk quantification for portfolio optimization and hedging strategies. Overall, by elucidating hydrogen's unique dynamics, this study informs stakeholder decisions to facilitate the hydrogen economy.

A time-varying copula approach for describing seasonality in multivariate ocean data

Characterizing multivariate ocean variables is quite critical for reliability design and risk assessment of marine structures. A robust, precise, and practical multivariate statistic model is necessary for comprehending ocean characteristics. As the time-varying characteristics exist in the ocean data, it is unreasonable to employ a simple constant statistical model to characterize all the multivariate data at one time. Therefore, in this paper, a time-varying copula approach is developed for modeling time-varying multivariate ocean data. Considering climate variations, a time-varying formula for return period and environment contour is also derived. The developed approach is demonstrated based on a site-specific ocean dataset collected from a buoy on the US coast. The climate effects associated with the multivariate ocean variables are characterized. The developed time-varying copula approach is also compared to the conventional copula and the conditional model in estimating the return period. The results showed that the time-varying model is helpful to explore the most critical environmental conditions for marine structures.

Thermoelectric optimization of integrated energy system considering wind-photovoltaic uncertainty, two-stage power-to-gas and ladder-type carbon trading

Integrated energy system (IES) coupled with renewable energy generation and power-to-gas (P2G) technology provides an effective solution to alleviate the current urgent carbon peak demand. Therefore, This paper develops a novel IES low-carbon economic operation strategy incorporating market mechanism. Firstly, a 24-h joint wind turbine and photovoltaic (WT-PV) output scenario generation method is proposed based on the time-varying Copula function. Secondly, the introduction of carbon capture technology (CCT), electrolyzer (EL), methane reactor (MR) and hydrogen fuel cell (HFC) refines the operation process of p2g into two stages. The combined heat and power (CHP) and HFC variable power operation strategy with adjustable thermoelectric ratio is proposed. Finally, the ladder-type carbon trading mechanism is used to guide IES to control carbon emission. The results of case study show that 1) The time-varying Copula function is effective in improving the scenario accuracy and fitting the data closely to reality 2) The introduction of hydrogen improves the energy utilization efficiency while promoting the consumption of WT-PV. 3) The variable power operation reduces the total cost and carbon emission by 2.4 % and 3.14 %, respectively, compared with the constant thermoelectric ratio. 4) The proposed IES operates reliably and economically under reasonable carbon trading parameters.

Differential Tail Dependence between Crude Oil and Forex Markets in Oil-Importing and Oil-Exporting Countries during Recent Crisis Periods

The relationship between foreign exchange rates and crude oil prices holds significant importance in comprehending the dynamics of oil markets and their implications for diverse economies. This study utilizes the time-varying copula to examine the interrelationships between foreign exchange rates (FX) and West Texas Intermediate (WTI) crude oil prices, with a focus on time-varying tail dependence and time-varying linear correlation. We found that the tail dependence between foreign exchange rates (FX) and WTI crude oil prices is higher for oil-exporting countries compared to oil-importing countries. Moreover, the COVID-19 pandemic has further amplified the tail dependence for oil-exporting countries while simultaneously increasing the correlation of FXs–WTI for oil-importing countries. However, the 2022 Russian–Ukrainian conflict has exerted a significant receding effect on both the tail dependence and linear correlation of FXs–WTI, reaching or even surpassing levels comparable to those witnessed during the 2008 financial crisis. These results facilitate policymakers, investors, and market participants in making well-informed decisions and developing effective risk management strategies.

Multi-objective robust optimization strategy for community virtual cloud power plant considering different demand responses based on correlative confidence gap decision theory

To study the effect of different demand responses (DRs) on solving uncertainty, a multi-objective robust optimization model for a community virtual cloud power plant (CVCPP) is proposed based on the correlative confidence gap decision theory (CCGDT) and considering different DRs. Firstly, a CVCPP framework with multi-energy conversion equipment, multi-load DR, and a rapid energy scheduling information processing approach is proposed. Secondly, a two-layer community comprehensive demand response model is developed, which includes the CDR of CIES internal consumers and the CDR of the external energy network that connects to CIES. Thirdly, the CCGDT is presented using the confidence gap decision model and the time-varying Copula theory. A multi-objective robust economy-energy-environment optimization model is developed. Fourthly, the Pareto solution set is solved using the multi-objective grey wolf optimization algorithm. The full consistency method is employed to identify an optimal scheduling strategy for decision-makers. Finally, the model's validity is verified by a multi-scene case of a residential area. The results show that: 1) Whether uncertainty is considered or not, different DRs have varied effects on the results. Combination DR are better than single, and the same DR has different effects on different residents. 2) The multi-objective values improve after adopting the integrated energy system. The resident cost of various residents is decreased by 0.054%-12.07% when uncertainty is included. Meanwhile, the profit of energy providers is improved by 0.95%-10.98%, the carbon treatment amount is lowered by 26.85%-51.31%, and the renewable energy utilization rate is enhanced by 0.85%-7.94%. 3) The results with different significance have different robustness to uncertainty changes, and the greater the significance, the stronger the robustness. Therefore, the decision-maker can flexibly control the optimization result by setting the confidence of the target value. The model plays a good role in the economy-energy-environment aspects and provides a reference for community decision-makers to choose the comprehensive multi-objective energy scheduling strategy considering the uncertainty.

Forecasting nonlinear dependency between cryptocurrencies and foreign exchange markets using dynamic copula: evidence from GAS models

PurposeThis paper examines and forecasts correlations between cryptocurrencies and major fiat currencies using Generalized Autoregressive Score (GAS) time-varying copulas. The authors examine to which extent the multivariate GAS method captures the volatility persistence and the nonlinear interaction effects between cryptocurrencies and major fiat currencies.Design/methodology/approachThe authors model tail dependence between conventional currencies and Bitcoin utilizing a Glosten-Jagannathan-Runkle Generalized Autoregressive Conditional Heteroscedastic model (GJR-GARCH)-GAS copula specification, which allows detecting the leptokurtic feature and clustering effects of currency returns distribution.FindingsThe authors' results show evidence of multiple tail dependence regimes, implying the unsuitability of applying static models to entirely describe the extreme dependence between Bitcoin and fiat currencies. Compared to the most common constant copulas, the authors find that the multivariate GAS copulas better forecast the volatility and dependency between cryptocurrencies and foreign exchange markets. Furthermore, based on the value-at-risk (VaR) and expected shortfall (ES) analyses, the authors show that the multivariate GAS models produce accurate risk measures by adding cryptocurrencies to a portfolio of fiat currencies.Originality/valueThis paper has two main contributions to the existing literature on cryptocurrencies. First, the authors empirically examine the tail dependence structure between common conventional currencies and bitcoin using GJR-GARCH GAS copulas which consider the leptokurtic feature and clustering effects of currency returns distribution. Second, by modeling VaR and ES, the authors test the implication of using time-varying models on the performance of currency portfolios, including cryptocurrencies.

Further evidence of contagion effect between the Chinese and the G20 stock markets during the COVID-19 pandemic: A time-varying copula approach

Further evidence of contagion effect between the Chinese and the G20 stock markets during the COVID-19 pandemic: A time-varying copula approach

Assessing the structure dependence between the Spanish stock market and some international financial markets. A time-varying copula analysis.

In this study we use time-varying copula analysis to investigate the dependence between the Spanish stock market, represented by the IBEX35 index, and some international stocks and commodities markets. The results indicate that: first, the European stock markets offer limited diversification possibilities. Second, American markets offer higher diversification possibilities than the European markets but the diversification may not work in an extreme market condition; here we find strong evidence of contagion effect. Third, the Asian markets outperform to the American markets offering higher diversifications possibilities even in extreme market conditions. Fourth, the assets negotiated in the Shanghai market may be considered hedge assets instead of diversifier assets; this feature is shared by the Bitcoin and Gold although the role of this last asset is highly volatile. These results provide useful information for those who seek to actively diversify their international portfolios and to manage their worldwide assets. Finally, we observe that degree of dependence derived from the correlation analysis is notably higher than the suggested by the copula analysis; this may be due the fact that correlation coefficient does not consider conditional heteroscedasticity so that correlations will be biased upwards.

Risk Dependence and Risk Spillovers Effect from Crude Oil on the Chinese Stock Market and Gold Market: Implications on Portfolio Management

We analyze crude oil’s dependence and the risk spillover effect on the Chinese stock market and the gold market. We compare both static and dynamic copula functions and calculate the average upward and downward spillover effect using the time-varying Copula model and the conditional value-at-risk approach. By utilizing daily data on crude oil prices, China’s stock market, and the gold market, we observe an asymmetric spillover effect: the downside spillover effects from crude oil prices on the Chinese stock market and gold market are larger than the upside spillover effect. We then identify changes in the structure of the sample periods and calculate the dynamic conditional correlation between them. In addition, we explore the optimal weight and hedge ratios in diversified portfolios to mitigate potential risks. Our results suggest that investors and portfolio managers should frequently adjust their portfolio strategies, particularly during extreme events like COVID-19, when financial assets become more volatile. Furthermore, crude oil can help reduce the risk in the Chinese stock market and gold market to some extent during different sub-periods.

Extreme linkages of carbon futures, energy markets, and economic indicators: A copula approach

ABSTRACT The interdependence of carbon allowances and different energy sources in extreme market behavior is still unsettled in the literature. Using different types of static and time-varying copulas, this piece of research aims to quantify the dependence structures of Europe-based carbon future returns and selected energy future returns (i.e. coal, electricity, oil, and natural gas), and to investigate whether or not these dependence structures are influenced by economic indicators. Our results show strong evidence that time-varying parameter copulas with extreme tails are the best fit to the dependence structure. We also find that the speculation activity and the uncertainty of the state of the global economy are two important components of this robust dependence structure in the period of oil price crises. These findings are relevant for the implementation of effective policies to make the carbon market operate more efficiently and stably.

Dependences and risk spillover effects between Bitcoin, crude oil and other traditional financial markets during the COVID-19 outbreak.

This study examines the relationship and risk spillover between Bitcoin, crude oil, and six traditional markets (the US stock, Chinese stock, gold, bond, currency, and real estate markets) from 2019 to 2020, during which the coronavirus disease 2019 (COVID-19) outbreak occurred as well. We first discuss the static relationship between Bitcoin and these markets using a quantile-on-quantile model and examine the dynamic relationship using a time-varying copula model. A conditional value-at-risk model is subsequently used to estimate the risk spillover between the markets studied. The empirical results reveal that the relationship between these markets is always time-varying, and the COVID-19 outbreak has revealed such changes in the relationship between Bitcoin and other traditional financial markets. The risk of all single markets has enhanced because of the pandemic. Further, the risk spillover of these markets has also changed dramatically since the COVID-19 outbreak during which the Bitcoin market has played an important role and exerted a significant impact on the crude oil market, and the four other markets (US stock, gold, Chinese stock, and real estate markets). Overall, our findings indicate that investors and policymakers need to be made aware of the risk spillover between Bitcoin, crude oil, and other traditional markets and that flexible hedge strategies and policies should be implemented in response to the challenges and economic recession observed following the COVID-19 outbreak.

Risk spillover between climate variables and the agricultural commodity market in East Africa

This paper assesses the effect of extreme weather variability in predicting the impact on two agricultural crop-related variables: yield and production. We use a Markov-Switching time-varying copula to describe the joint dependence structure between extreme weather variability and crops in East Africa during the period 1961–2018. Understanding the risk associated with weather variability on agricultural production is crucial, as mitigation, and even adaptation, can then be made more effective. Climate data are divided into regimes: higher and lower regimes. The abnormal or higher regime is the period during which the temperature exceeds a certain threshold, while the lower regime is the period during which the rainfall is below a certain threshold. The findings show that there is strong dependence between weather variability and crops, meaning an increase in temperature or a decrease in rainfall is associated with a decrease in crop yield or production. The dependence is more significant when weather variability moves into either regime compared to the normal condition. The dependency in the higher regime tends to be more significant. This highlights the need to formulate policies that consider crop improvement strategies such as genetic crops, irrigation, and adaption under carbon dioxide (CO2) fertiliser to mitigate the impact on food supply in the region.

Nexus Between Travel & Leisure Stocks and Uncertainties: An Extreme Risk Spillover Analysis

The paper examines the presence of risk spillover to the US travel & leisure industry from the extreme changes in the uncertainties. More specifically, using a time-varying copula based conditional Value-at-Risk (CoVaR) framework, we evaluate the dynamic impact of the uncertainties on the extreme risk of the US travel & leisure industry by taking into consideration the uncertainty in economic policy, equity market conditions, and crude oil prices. The findings indicate a significant dynamic inverse relationship between the returns of travel & leisure industry and changes in uncertainty variables. The results further indicate the stronger sensitivity of the travel & leisure industry toward the uncertainties in financial market and crude oil prices. We find significant evidence of extreme upside and downside risk spillover to the US travel & leisure industry from excessive downward and upward changes in uncertainties respectively. The findings also demonstrate the asymmetric effect of extreme movements in uncertainty factors on the tail risk of the US travel & leisure industry. The findings of the study have ramifications for risk managers, portfolio managers and investors.