Energy markets and green bonds: A tail dependence analysis with time-varying optimal copulas and portfolio implications

2021 ◽  
Vol 74 ◽  
pp. 102418
Author(s):  
Muhammad Abubakr Naeem ◽  
Elie Bouri ◽  
Mabel D. Costa ◽  
Nader Naifar ◽  
Syed Jawad Hussain Shahzad
Keyword(s):  
Tail Dependence ◽  
Energy Markets ◽  
Dependence Analysis ◽  
Time Varying

scholarly journals Asymmetric Dependence Analysis of International Crude Oil Spot and Futures Based on the Time Varying Copula-GARCH

2015 ◽  
Vol 8 (1) ◽  
pp. 463-467
Author(s):  
He Xin ◽  
Zhang Jun
Keyword(s):  
Parameter Estimation ◽  
Crude Oil ◽  
Significant Influence ◽  
Tail Dependence ◽  
Sliding Window ◽  
Dependence Structure ◽  
Dependence Analysis ◽  
Time Varying ◽  
Daily Return ◽  
Asymmetric Dependence

Taking daily return of international crude oil spot and futures as sample, this paper analyzed the time varying and asymmetric dependence structure of them by time varying Copula-GARCH model based on sliding window and semi parameter estimation. This paper analyzed the regular changing between dependence structure of crude oil spot and futures and the return fluctuation, and confirmed that there is significant time varying asymmetric tail dependence. This paper found that the size of the sliding window had no significant influence on the conclusion, and the data of weekly return is more suitable for analysis of the trend of dependence structure of spot.

scholarly journals Dynamic dependence structure between energy markets and the Italian stock index

2018 ◽  
Vol 15 (2) ◽  
pp. 60-67
Author(s):  
Giovanni Masala
Keyword(s):  
Extreme Values ◽  
Tail Dependence ◽  
Energy Markets ◽  
Stock Index ◽  
Dependence Structure ◽  
Time Varying ◽  
Empirical Distributions ◽  
Proposed Model ◽  
Log Likelihood ◽  
Tail Dependence Coefficient

The dependence structure between the main energy markets (such as crude oil, natural gas, and coal) and the main stock index plays a crucial role in the economy of a given country. As the dependence structure between these series is dramatically complex and it appears to change over time, time-varying dependence structure given by a class of dynamic copulas is taken into account.To this end, each pair of time series returns with a dynamic t-Student copula is modelled, which takes as input the time-varying correlation. The correlation evolves with the DCC(1,1) equation developed by Engle.The model is tested through a simulation by employing empirical data issued from the Italian Stock Market and the main connected energy markets. The author considers empirical distributions for each marginal series returns in order to focus on the dependence structure. The model’s parameters are estimated by maximization of the log-likelihood. Also evidence is found that the proposed model fits correctly, for each pair of series, the left tail dependence coefficient and it is then compared with a static copula dependence structure which clearly underperforms the number of joint extreme values at a given confidence level.

scholarly journals Multi-Horizon Dependence between Crude Oil and East Asian Stock Markets and Implications in Risk Management

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 294 ◽  
Author(s):  
Xiaojing Cai ◽  
Shigeyuki Hamori ◽  
Lu Yang ◽  
Shuairu Tian
Keyword(s):  
Risk Management ◽  
Crude Oil ◽  
Stock Markets ◽  
East Asian ◽  
Tail Dependence ◽  
Dependence Structure ◽  
Time Varying ◽  
Risk Hedging ◽  
Long Run ◽  
East Asian Stock Markets

This paper examines the dynamic dependence structure of crude oil and East Asian stock markets at multiple frequencies using wavelet and copulas. We also investigate risk management implications and diversification benefits of oil-stock portfolios by calculating and comparing risk and tail risk hedging performance. Our results provide strong evidence of time-varying dependence and asymmetric tail dependence between crude oil and East Asian stock markets at different frequencies. The level and fluctuation of their dependencies increase as time scale increases. Furthermore, we find the time-varying hedging benefits differ at investment horizons and reduced over the long run. Our results suggest that crude oil could be used as a hedge and safe haven against East Asian stock markets, especially in the short- and mid-term.

Measuring Dependence Between the USA and the Asian Economies: A Time-varying Copula Approach

2019 ◽  
Vol 20 (4) ◽  
pp. 962-980 ◽  
Author(s):  
Shegorika Rajwani ◽  
Dilip Kumar
Keyword(s):  
Financial Crisis ◽  
Stock Market ◽  
Financial Markets ◽  
Stock Markets ◽  
Global Financial Crisis ◽  
Tail Dependence ◽  
Time Varying ◽  
The Us ◽  
Asian Economies ◽  
Copula Approach

During the past few years, many of the financial markets have gone through devastating effects due to the crisis in one or the other economy of the world. The recent global financial crisis has triggered dramatic movements in various stock markets which may arise from interdependence or contagion between the markets. This article attempts to measure the contagion between the equity markets of Asia and the US stock market. The countries considered in the Asian group are China, India, Indonesia, South Korea, Taiwan, Hong Kong, Malaysia and Japan. Most of the Asian economies have experienced drastic higher volatility and uncertainty in the financial markets. If the markets are contagious, then the investors will be unable to reap benefits through international diversification of the portfolio. In such a case, the policymakers will further frame policies so that they can insulate themselves from inflicting heavy damage from various crises. To achieve our goal, we make use of the time-varying copula approach which helps us to study the joint behaviour of the series based on their marginal distribution. Time-varying copula approach can also capture the non-linear dependence in the series and exhibits a rich pattern of tail behaviour. Our findings support the contagion between the Asian stock markets and the US stock market during the global financial crisis. This article also highlights that the increased tail dependence is an important factor for the contagion between the Asian stock markets and the US market.

scholarly journals Research on Time-Varying Two-Way Spillover Effects Between Carbon and Energy Markets: Empirical Evidence From China

2021 ◽  
Vol 9 ◽  
Author(s):  
Sen Qiao ◽  
Chen Xi Zhao ◽  
Kai Quan Zhang ◽  
Zheng Yu Ren
Keyword(s):  
Crude Oil ◽  
Spillover Effect ◽  
Energy Markets ◽  
Carbon Market ◽  
Energy Market ◽  
Time Varying ◽  
Carbon Markets ◽  
Oil Futures ◽  
New Energy ◽  
Crude Oil Futures

With the improvement of China’s carbon emission trading system, the spillover effect between carbon and energy markets is becoming more and more prominent. This paper selects four representative pilot carbon markets, including Beijing (BEA), Guangdong (GDEA), Hubei (HBEA) and Shanghai (SHEA). And three representative energy markets, including Crude Oil Futures (SC), power index (L11655) and China Securities new energy index (NEI). Combining the rolling window technology with DY spillover index, set a 50-weeks rolling window to measure the spillover index, and deeply analyze the time-varying two-way spillover effect between China’s carbon and energy markets. The results show that the spillover effect between China’s carbon and energy markets has significant time variability and two-way asymmetry. The time-varying spillover effect of different carbon pilot markets on the energy market has regional heterogeneity. The volatility spillover effect of Beijing and Shanghai carbon markets mainly comes from the crude oil futures market, Guangdong carbon market mainly comes from the new energy market, and Hubei carbon market mainly comes from crude oil and electricity market. The above research results contribute to the prevention of potential risk spillover between carbon and energy markets, which can promote the establishment of China’s unified carbon market and the prevention of systemic financial risks in energy market.

Tail dependence analysis of stock markets using extreme value theory

2017 ◽  
Vol 49 (45) ◽  
pp. 4588-4599
Author(s):  
Abhay K. Singh ◽  
David E. Allen ◽  
Robert J. Powell
Keyword(s):  
Extreme Value Theory ◽  
Stock Markets ◽  
Tail Dependence ◽  
Value Theory ◽  
Extreme Value ◽  
Dependence Analysis
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