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 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 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.

scholarly journals Volatility Modeling and Dependence Structure of ESG and Conventional Investments

Risks ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 20
Author(s):  
Joanna Górka ◽  
Katarzyna Kuziak
Keyword(s):  
Extreme Values ◽  
Tail Dependence ◽  
Dependence Structure ◽  
Rates Of Return ◽  
Short Time Series ◽  
Volatility Models ◽  
Financial Investments ◽  
European Banking ◽  
Short Time ◽  
Data Requirements

The question of whether environmental, social, and governance investments outperform or underperform other conventional financial investments has been debated in the literature. In this study, we compare the volatility of rates of return of selected ESG indices and conventional ones and investigate dependence between them. Analysis of tail dependence is important to evaluate the diversification benefits between conventional investments and ESG investments, which is necessary in constructing optimal portfolios. It allows investors to diversify the risk of the portfolio and positively impact the environment by investing in environmentally friendly companies. Examples of institutions that are paying attention to ESG issues are banks, which are increasingly including products that support sustainability goals in their offers. This analysis could be also important for policymakers. The European Banking Authority (EBA) has admitted that ESG factors can contribute to risk. Therefore, it is important to model and quantify it. The conditional volatility models from the GARCH family and tail-dependence coefficients from the copula-based approach are applied. The analysis period covered 2007 until 2019. The period of the COVID-19 pandemic has not been analyzed due to the relatively short time series regarding data requirements from models’ perspective. Results of the research confirm the higher dependence of extreme values in the crisis period (e.g., tail-dependence values in 2009–2014 range from 0.4820/0.4933 to 0.7039/0.6083, and from 0.5002/0.5369 to 0.7296/0.6623), and low dependence of extreme values in stabilization periods (e.g., tail-dependence values in 2017–2019 range from 0.1650 until 0.6283/0.4832, and from 0.1357 until 0.6586/0.5002). Diversification benefits vary in time, and there is a need to separately analyze crisis and stabilization periods.

scholarly journals Asymmetric Dependence between Oil Prices and Maritime Freight Rates: A Time-Varying Copula Approach

2020 ◽  
Vol 12 (24) ◽  
pp. 10687
Author(s):  
Ki-Hong Choi ◽  
Seong-Min Yoon
Keyword(s):  
Tail Dependence ◽  
Oil Prices ◽  
Dependence Structure ◽  
Time Varying ◽  
Freight Rates ◽  
Asymmetric Dependence ◽  
Long Run ◽  
Copula Approach ◽  
Short Run ◽  
The Baltic

Changes in crude oil price affect the shipping freight market via three different channels. This study explores the dependence structure between oil prices and maritime freight rates to identify the effective channel. Therefore, it investigates the relationship between oil prices and three major maritime freight rates; the Baltic Dry Index (BDI), the Baltic Dirty Tanker Index (BDTI), and the Baltic Clean Tanker Index (BCTI). We employ the decomposition method, not studied in the existing literature, and the copula approach which can identify the time-varying effects and asymmetry in the tail dependence structure between oil prices and freight rates. The main results of this analysis are as follows: the decomposed components display different conditional dependence patterns, and asymmetry is revealed in the upper and lower tail dependence. In the long-run, we find more dependence in extreme periods like the financial crises. In short-run fluctuations, we find the dependence increases in an economic boom. The implications of the results suggest that dependence can vary over time and may change depending on extreme events, implying that the complementary strategies should be different the long-run and short-run.

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 between Oil Prices and Maritime Freight Rates: A Time-Varying Copula Approach

2020 ◽  
Author(s):  
Ki-Hong Choi ◽  
Seong-Min Yoon
Keyword(s):  
Tail Dependence ◽  
Oil Prices ◽  
Dependence Structure ◽  
Time Varying ◽  
Freight Rates ◽  
Asymmetric Dependence ◽  
Long Run ◽  
Copula Approach ◽  
Short Run ◽  
The Baltic

Changes in crude oil price affect the shipping freight market in three different channels. This study explores the dependence structure between oil prices and maritime freight rates to identify the strongest channel. Therefore, it investigates the relationship between oil prices and three major maritime freight rates; the Baltic Dry Index (BDI), the Baltic Dirty Tanker Index (BDTI), and the Baltic Clean Tanker Index (BCTI). We employ the decomposition method, not studied in the existing literature. The copula approach identifies the time-varying effects and asymmetry in the tail dependence structure between oil prices and freight rates. The main results of this analysis are as follows. The decomposed components display different conditional dependence patterns, and asymmetry is revealed in the upper and lower tail dependence. In the long run, we find more dependence in extreme periods like the financial crises. In short-run fluctuations, we find the dependence increases in an economic boom. The implications of the results suggest that dependence can vary over time and may change depending on extreme events, implying that the complementary strategies of the long run and short run should be different.

scholarly journals Dependence Modeling in Energy Markets using Sibuya-type Copulas

2017 ◽  
Vol 6 (3) ◽  
pp. 43
Author(s):  
Nikolai Kolev ◽  
Jayme Pinto
Keyword(s):  
Goodness Of Fit ◽  
Basic Assumption ◽  
Energy Markets ◽  
Dependence Structure ◽  
Dependence Function ◽  
Dependence Modeling ◽  
Goodness Of Fit Tests ◽  
Oil And Natural Gas ◽  
Error Terms ◽  
Time Invariant

The dependence structure between 756 prices for futures on crude oil and natural gas traded on NYMEX is analyzed  using  a combination of novel time-series and copula tools.  We model the log-returns on each commodity individually by Generalized Autoregressive Score models and account for dependence between them by fitting various copulas to corresponding  error terms. Our basic assumption is that the dependence structure may vary over time, but the ratio between the joint distribution of error terms and the product of marginal distributions (e.g., Sibuya's dependence function) remains the same, being time-invariant.  By performing conventional goodness-of-fit tests, we select the best copula, being member of the currently  introduced class of  Sibuya-type copulas.

scholarly journals A Neutrosophic Forecasting Model for Time Series Based on First-Order State and Information Entropy of High-Order Fluctuation

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 455 ◽  
Author(s):  
Hongjun Guan ◽  
Zongli Dai ◽  
Shuang Guan ◽  
Aiwu Zhao
Keyword(s):  
Time Series ◽  
Information Entropy ◽  
Stock Exchange ◽  
Time Series Forecasting ◽  
High Order ◽  
Stock Index ◽  
Neutrosophic Set ◽  
Forecasting Model ◽  
Forecasting Models ◽  
Proposed Model

In time series forecasting, information presentation directly affects prediction efficiency. Most existing time series forecasting models follow logical rules according to the relationships between neighboring states, without considering the inconsistency of fluctuations for a related period. In this paper, we propose a new perspective to study the problem of prediction, in which inconsistency is quantified and regarded as a key characteristic of prediction rules. First, a time series is converted to a fluctuation time series by comparing each of the current data with corresponding previous data. Then, the upward trend of each of fluctuation data is mapped to the truth-membership of a neutrosophic set, while a falsity-membership is used for the downward trend. Information entropy of high-order fluctuation time series is introduced to describe the inconsistency of historical fluctuations and is mapped to the indeterminacy-membership of the neutrosophic set. Finally, an existing similarity measurement method for the neutrosophic set is introduced to find similar states during the forecasting stage. Then, a weighted arithmetic averaging (WAA) aggregation operator is introduced to obtain the forecasting result according to the corresponding similarity. Compared to existing forecasting models, the neutrosophic forecasting model based on information entropy (NFM-IE) can represent both fluctuation trend and fluctuation consistency information. In order to test its performance, we used the proposed model to forecast some realistic time series, such as the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), the Shanghai Stock Exchange Composite Index (SHSECI), and the Hang Seng Index (HSI). The experimental results show that the proposed model can stably predict for different datasets. Simultaneously, comparing the prediction error to other approaches proves that the model has outstanding prediction accuracy and universality.

Dependence structure between money and economic activity: A Markov-switching copula VEC approach

2021 ◽  
pp. 1-17
Author(s):  
Apostolos Serletis ◽  
Libo Xu
Keyword(s):  
Error Correction ◽  
Economic Activity ◽  
Regime Switching ◽  
Markov Switching ◽  
Tail Dependence ◽  
Error Correction Model ◽  
Dependence Structure ◽  
Copula Models ◽  
Correction Model ◽  
Dependence Structures

Abstract This paper examines correlation and dependence structures between money and the level of economic activity in the USA in the context of a Markov-switching copula vector error correction model. We use the error correction model to focus on the short-run dynamics between money and output while accounting for their long-run equilibrium relationship. We use the Markov regime-switching model to account for instabilities in the relationship between money and output, and also consider different copula models with different dependence structures to investigate (upper and lower) tail dependence.

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