scholarly journals Leverage and Volatility Feedback Effects and Conditional Dependence Index: A Nonparametric Study

2018 ◽  
Author(s):  
Yiguo Sun ◽  
Ximing Wu
Keyword(s):  
Tail Dependence ◽  
Dependence Structure ◽  
Market Returns ◽  
Dependence Relation ◽  
Feedback Effects ◽  
Conditional Dependence ◽  
Market Return ◽  
Volatility Index ◽  
Copula Method ◽  
Volatility Feedback

This paper studies the contemporaneous relationship between S&P 500 index returns and log-increments of the market volatility index (VIX) via a nonparametric copula method. Specifically, we propose a conditional dependence index to investigate how the dependence between the two series varies across different segments of the market return distribution. We find that: (a) the two series exhibit strong, negative, extreme tail dependence; (b) the negative dependence is stronger in extreme bearish markets than in extreme bullish markets; (c) the dependence gradually weakens as the market return moves toward the center of its distribution, or in quiet markets. The unique dependence structure supports the VIX as a barometer of markets' mood in general. Moreover, applying the proposed method to the S&P 500 returns and the implied variance (VIX²), we find that the nonparametric leverage effect is much stronger than the nonparametric volatility feedback effect, although, in general, both effects are weaker than the dependence relation between the market returns and the log-increments of the VIX.

scholarly journals On The Dynamic Dependence Between Oil Prices and Stock Market Returns: A Copula-GARCH Approach

2019 ◽  
Vol 9 (1) ◽  
pp. 414
Author(s):  
Mondher Kouki ◽  
Samia Ben Massoud ◽  
Achouak Barguellil
Keyword(s):  
Stock Returns ◽  
Tail Dependence ◽  
Dependence Structure ◽  
Market Returns ◽  
Copula Functions ◽  
Stock Market Returns ◽  
Asset Return ◽  
Asset Pricing Model ◽  
Conditional Dependence ◽  
Show Evidence

This article investigates the conditional dependence structure between crude oil price and stock returns markets. Our empirical analysis relies on an asset pricing model that accommodates the asset return dependence through the copula functions. The obtained results indicate the superiority of our approach and show evidence of significant tail dependence of the returns in unstable financial environment.

scholarly journals Dependence Structure between Conventional and Islamic Indexes: A Copula Approach

2020 ◽  
Vol 4 (2) ◽  
pp. 22-30
Author(s):  
Samia Ben Messaoud ◽  
Mondher Kouki
Keyword(s):  
Empirical Analysis ◽  
Tail Dependence ◽  
Dependence Structure ◽  
Lower Tail ◽  
Copula Models ◽  
Conditional Dependence ◽  
Copula Approach ◽  
Upper Tail Dependence ◽  
Stock Indexes

This article examines the conditional dependence structure between Islamic stock indexes and conventional counterparts. Our empirical analysis relies on Islamic and conventional indexes of dependence distribution using copula methods over the period 1999–2014. The results from the copula models denote that the dependence is not formally symmetric in that the lower tail dependence is significantly larger than the upper tail dependence.

scholarly journals Conditional Dependence between Oil Prices and Exchange Rates in BRICS Countries: An Application of the Copula-GARCH Model

2019 ◽  
Vol 12 (2) ◽  
pp. 99
Author(s):  
Yijin He ◽  
Shigeyuki Hamori
Keyword(s):  
Exchange Rates ◽  
Value At Risk ◽  
Tail Dependence ◽  
Oil Prices ◽  
Dependence Structure ◽  
Copula Models ◽  
Conditional Dependence ◽  
Brics Countries ◽  
T Copula ◽  
Student’S T

We studied the dependence structure between West Texas Intermediate (WTI) oil prices and the exchange rates of BRICS1 countries, using copula models. We used the Normal, Plackett, rotated-Gumbel, and Student’s t copulas to measure the constant dependence, and we captured the dynamic dependence using the Generalized Autoregressive Score with the Student’s t copula. We found that negative dependence and significant tail dependence exist in all pairs considered. The Russian Ruble (RUB)–WTI pair has the strongest dependence. Moreover, we treated five exchange rate–oil pairs as portfolios and evaluated the Value at Risk and Expected Shortfall from the time-varying copula models. We found that both reach low values when the oil price falls sharply.

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.

scholarly journals Extremal behavior of Archimedean copulas

2011 ◽  
Vol 43 (1) ◽  
pp. 195-216 ◽  
Author(s):  
Martin Larsson ◽  
Johanna Nešlehová
Keyword(s):  
Domain Of Attraction ◽  
Tail Dependence ◽  
Dependence Structure ◽  
Tail Behavior ◽  
Archimedean Copulas ◽  
Radial Part ◽  
Stochastic Representation ◽  
Lower Tail ◽  
Extremal Behavior ◽  
Practical Implications

We show how the extremal behavior of d-variate Archimedean copulas can be deduced from their stochastic representation as the survival dependence structure of an ℓ1-symmetric distribution (see McNeil and Nešlehová (2009)). We show that the extremal behavior of the radial part of the representation is determined by its Williamson d-transform. This leads in turn to simple proofs and extensions of recent results characterizing the domain of attraction of Archimedean copulas, their upper and lower tail-dependence indices, as well as their associated threshold copulas. We outline some of the practical implications of their results for the construction of Archimedean models with specific tail behavior and give counterexamples of Archimedean copulas whose coefficient of lower tail dependence does not exist.

scholarly journals A Method for Systemic Risk Estimation Based on CDS Indices

2015 ◽  
Vol 8 (1) ◽  
pp. 103-124
Author(s):  
Gabriel Gaiduchevici
Keyword(s):  
Goodness Of Fit ◽  
Risk Estimation ◽  
Multivariate Time Series ◽  
Tail Dependence ◽  
Dependence Structure ◽  
Parameter Estimates ◽  
Copula Models ◽  
Goodness Of Fit Tests ◽  
Multi Stage ◽  
Stage Estimation

AbstractThe copula-GARCH approach provides a flexible and versatile method for modeling multivariate time series. In this study we focus on describing the credit risk dependence pattern between real and financial sectors as it is described by two representative iTraxx indices. Multi-stage estimation is used for parametric ARMA-GARCH-copula models. We derive critical values for the parameter estimates using asymptotic, bootstrap and copula sampling methods. The results obtained indicate a positive symmetric dependence structure with statistically significant tail dependence coefficients. Goodness-of-Fit tests indicate which model provides the best fit to data.

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