scholarly journals Analysis of Tail Dependence between Sovereign Debt Distress and Bank Non-Performing Loans

2020 ◽  
Vol 12 (2) ◽  
pp. 747
Author(s):  
Li Liu ◽  
Yu-Min Liu ◽  
Jong-Min Kim ◽  
Rui Zhong ◽  
Guang-Qian Ren
Keyword(s):  
Sovereign Debt ◽  
Tail Dependence ◽  
Bank Loan ◽  
Gaussian Copula ◽  
Dependence Structure ◽  
Financial Policy ◽  
Copula Models ◽  
Marginal Regression ◽  
Fiscal Distress ◽  
Shed Light

We investigate the tail dependence between sovereign debt distress and bank non-performing loans (NPLs) using a large sample of developed and emerging countries in recent decades. Considering the feedback loop of sovereign debt and bank loan distress, we use three copula models to analyze the asymmetry of tail dependence structure between sovereign debt exposure and bank NPLs. We use the Gaussian copula marginal regression to control the concurrent impact of other macroeconomic variables. We provide evidence that sovereign debt indicates an important determinant of NPLs. We also find that there is tail dependence between sovereign debt distress and bank NPLs, whereas the tail dependence coefficients vary across countries. Our findings shed light on the influence of fiscal distress on bank loan distress and provide immediate implications for the design of macro prudential and financial policy.

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 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.

The dependence structure in credit risk between money and derivatives markets

2014 ◽  
Vol 40 (8) ◽  
pp. 758-769
Author(s):  
Weiou Wu ◽  
David G. McMillan
Keyword(s):  
Credit Risk ◽  
Sovereign Debt ◽  
Debt Crisis ◽  
Money Market ◽  
Distribution Functions ◽  
Gaussian Copula ◽  
Dependence Structure ◽  
Content Type ◽  
Dimensional Distribution ◽  
Derivatives Markets

Purpose – The purpose of this paper is to examine the dynamic dependence structure in credit risk between the money market and the derivatives market during 2004-2009. The authors use the TED spread to measure credit risk in the money market and CDS index spread for the derivatives market. Design/methodology/approach – The dependence structure is measured by a time-varying Gaussian copula. A copula is a function that joins one-dimensional distribution functions together to form multivariate distribution functions. The copula contains all the information on the dependence structure of the random variables while also removing the linear correlation restriction. Therefore, provides a straightforward way of modelling non-linear and non-normal joint distributions. Findings – The results show that the correlation between these two markets while fluctuating with a general upward trend prior to 2007 exhibited a noticeably higher correlation after 2007. This points to the evidence of credit contagion during the crisis. Three different phases are identified for the crisis period which sheds light on the nature of contagion mechanisms in financial markets. The correlation of the two spreads fell in early 2009, although remained higher than the pre-crisis level. This is partly due to policy intervention that lowered the TED spread while the CDS spread remained higher due to the Eurozone sovereign debt crisis. Originality/value – The paper examines the relationship between the TED and CDS spreads which measure credit risk in an economy. This paper contributes to the literature on dynamic co-movement, contagion effects and risk linkages.

scholarly journals On the Relationship of Cryptocurrency Price with US Stock and Gold Price Using Copula Models

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1859
Author(s):  
Jong-Min Kim ◽  
Seong-Tae Kim ◽  
Sangjin Kim
Keyword(s):  
Gold Price ◽  
Gaussian Copula ◽  
Time Varying ◽  
Conditional Correlation ◽  
Copula Models ◽  
Marginal Regression ◽  
High Volatility ◽  
Asymmetric Garch ◽  
Relationship Of ◽  
The Relationship

This paper examines the relationship of the leading financial assets, Bitcoin, Gold, and S&P 500 with GARCH-Dynamic Conditional Correlation (DCC), Nonlinear Asymmetric GARCH DCC (NA-DCC), Gaussian copula-based GARCH-DCC (GC-DCC), and Gaussian copula-based Nonlinear Asymmetric-DCC (GCNA-DCC). Under the high volatility financial situation such as the COVID-19 pandemic occurrence, there exist a computation difficulty to use the traditional DCC method to the selected cryptocurrencies. To solve this limitation, GC-DCC and GCNA-DCC are applied to investigate the time-varying relationship among Bitcoin, Gold, and S&P 500. In terms of log-likelihood, we show that GC-DCC and GCNA-DCC are better models than DCC and NA-DCC to show relationship of Bitcoin with Gold and S&P 500. We also consider the relationships among time-varying conditional correlation with Bitcoin volatility, and S&P 500 volatility by a Gaussian Copula Marginal Regression (GCMR) model. The empirical findings show that S&P 500 and Gold price are statistically significant to Bitcoin in terms of log-return and volatility.

scholarly journals Dependence Modelling in Insurance via Copulas with Skewed Generalised Hyperbolic Marginals

2019 ◽  
Vol 0 (0) ◽  
Author(s):  
Vitali Alexeev ◽  
Katja Ignatieva ◽  
Thusitha Liyanage
Keyword(s):  
Value At Risk ◽  
Tail Dependence ◽  
Dependence Structure ◽  
Portfolio Risk ◽  
Total Risk ◽  
Copula Models ◽  
Out Of Sample ◽  
Upper Tail Dependence ◽  
Dependence Modelling ◽  
Best Fit

Abstract This paper investigates dependence among insurance claims arising from different lines of business (LoBs). Using bivariate and multivariate portfolios of losses from different LoBs, we analyse the ability of various copulas in conjunction with skewed generalised hyperbolic (GH) marginals to capture the dependence structure between individual insurance risks forming an aggregate risk of the loss portfolio. The general form skewed GH distribution is shown to provide the best fit to univariate loss data. When modelling dependency between LoBs using one-parameter and mixture copula models, we favour models that are capable of generating upper tail dependence, that is, when several LoBs have a strong tendency to exhibit extreme losses simultaneously. We compare the selected models in their ability to quantify risks of multivariate portfolios. By performing an extensive investigation of the in- and out-of-sample Value-at-Risk (VaR) forecasts by analysing VaR exceptions (i.e. observations of realised portfolio value that are greater than the estimated VaR), we demonstrate that the selected models allow to reliably quantify portfolio risk. Our results provide valuable insights with regards to the nature of dependence and fulfils one of the primary objectives of the general insurance providers aiming at assessing total risk of an aggregate portfolio of losses when LoBs are correlated.

Dependence Structure Analysis and VaR Estimation Based on China’s and International Gold Price: A Copula Approach

2020 ◽  
Vol 19 (01) ◽  
pp. 169-193
Author(s):  
Zhicheng Liang ◽  
Junwei Wang ◽  
Kin Keung Lai
Keyword(s):  
Tail Dependence ◽  
Gold Price ◽  
Dependence Structure ◽  
Copula Models ◽  
Simulation Based ◽  
Copula Approach ◽  
Weighted Portfolio ◽  
Pricing Power ◽  
Gold Producer ◽  
Research Period

Since 2013, China has become the world’s largest gold producer and consumer. To gain the corresponding global pricing power in gold, many actions have been taken by China in recent years, including the International Board at Shanghai Gold Exchange, Shanghai-Hong Kong Gold Connect and Shanghai Gold Fix. Our work studies the dependence structure between China’s and international gold price and examines whether these moves are changing the dependence structure. We use GARCH-copula models to detect the dynamic dependence and tail dependence. The research period is set to contain the Financial Crisis in 2008, the dramatical plunge of gold price in 2013 and a series of black swan events in 2016. The empirical study shows that some event driven dependence structure breaks are statistically insignificant. And the time-varying Symmetrized Joe-Clayton copula is the best copula to model the dependence structure based on AIC value. Finally, an example of applications of this dependence structure is given by estimating the VaR of an equally weighted portfolio with a simulation-based method.

Simulation for Mixture of Archimedean Copulas

2012 ◽  
Vol 195-196 ◽  
pp. 738-743
Author(s):  
Shi De Ou
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Tail Dependence ◽  
Likelihood Estimation ◽  
Numerical Results ◽  
Dependence Structure ◽  
Archimedean Copulas ◽  
Copula Models ◽  
Random Samples ◽  
Dependence Structures

Many dependence structures can consist of mixed copulas. In order to analyze the dependence of stock, we present the method of estimation for mixed copula models. Via generating random samples and using maximum likelihood estimation, the parameters of mixture of Archimedean copulas are estimated. Numerical results show that this method estimates effectively the parameters and tail dependence coefficients. Therefore we can use the method to analyze dependence structure for stocks.

scholarly journals Dependence Modelling using GARCH, EGARCH, and Copula Models:

2018 ◽  
Vol 2 (2) ◽  
pp. 55-59
Author(s):  
Nurul Hanis Aminuddin Jafry ◽  
Ruzanna Ab Razak ◽  
Noriszura Ismail
Keyword(s):  
Gaussian Copula ◽  
Dependence Structure ◽  
Time Varying ◽  
Strongly Correlated ◽  
Copula Models ◽  
Egarch Model ◽  
Copula Approach ◽  
Model Finding ◽  
Dependence Modelling ◽  
The Impact

Copula become a popular tool to measure the dependency between financial data due to its ability to capture the non-normal distributions. Hence, this paper will inspect the impact of input models towards the parameter estimation of marginal and copula models for KLCI and FBMHS returns series by considering the ARMA-GARCH model and the ARMA-EGARCH model. This study also investigates the dependency of Islamic-conventional pair for Malaysia indices by using static copula and time-varying copula approach. The closing prices of Malaysia indices represented by KLCI (conventional) index and FBMHS (Islamic) index for the period of 21 May 2007 until 28 September 2018 are used as a sample data. The results show that KLCI-FBMHS pair is strongly correlated, different input models (ARMA-GARCH and ARMA-EGARCH) have identical dependence structure but slightly different value of parameter estimated, and the time-varying Gaussian copula is chosen as the best dependence model. Finding suggest that the diversification between Islamic-conventional pair is worthwhile during stable period.  

scholarly journals Co-Movement of Forex Rate and Share Price of Pakistan Stock Exchange - An Application of Copula Models

2020 ◽  
Vol V (III) ◽  
pp. 78-87
Author(s):  
Muhammad Nouman Latif ◽  
Nasir Ali ◽  
Anjum Shahzad
Keyword(s):  
Time Series ◽  
Joint Distribution ◽  
Stock Exchange ◽  
Tail Dependence ◽  
Share Price ◽  
Series Data ◽  
Dependence Structure ◽  
Complex Nature ◽  
Copula Models ◽  
The Relationship

This paper examines the relationship between the forex rate and the share price of the Pakistan Stock Exchange. The study provides additional understating of the complex nature of the relationship among bi-variate time series using the Copula model. Copula models are best suited to find the co-movement of time series data integrating the possible latent structure of the relationship through estimation of joint distribution with the help of marginal distribution of each time series variable. Alike from the traditional time series analysis, Copula models are best suited to estimate the complex relationship, specifically the tail dependence structure of joint distribution of the variables. Results of the study highlight a significant two-sided tail dependence structure between the Forex rate and share price of the Pakistan Stock Exchange.

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.

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