Jump tail dependence in Lévy copula models

Extremes ◽  
2012 ◽  
Vol 16 (3) ◽  
pp. 303-324 ◽  
Author(s):  
Oliver Grothe
Keyword(s):  
Tail Dependence ◽  
Copula Models ◽  
Levy Copula

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.

scholarly journals Multidimensional Copula Models for Parallel Development of the Us Bond Market Indices

2017 ◽  
Vol 69 (1) ◽  
pp. 61-73
Author(s):  
Jozef Komorník ◽  
Magdaléna Komorníková ◽  
Tomáš Bacigál ◽  
Cuong Nguyen
Keyword(s):  
Degrees Of Freedom ◽  
Heavy Tails ◽  
Tail Dependence ◽  
Corporate Bond ◽  
Bond Markets ◽  
Copula Models ◽  
Derivative Instruments ◽  
The Us ◽  
Bond Indices ◽  
Investment Grade

Abstract Stock and bond markets co-movements have been studied by many researchers. The object of our investigation is the development of three U.S. investment grade corporate bond indices. We concluded that the optimal 3D as well as partial pairwise 2D models are in the Student class with 2 degrees of freedom (and thus very heavy tails) and exhibit very high values of tail dependence coefficients. Hence the considered bond indices do not represent suitable components of a well-diversified investment portfolio. On the other hand, they could make good candidates for underlying assets of derivative instruments.

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 Copula-based statistical refinement of precipitation in RCM simulations over complex terrain

2011 ◽  
Vol 15 (7) ◽  
pp. 2401-2419 ◽  
Author(s):  
P. Laux ◽  
S. Vogl ◽  
W. Qiu ◽  
H. R. Knoche ◽  
H. Kunstmann
Keyword(s):  
Time Series ◽  
Complex Terrain ◽  
Large Scale ◽  
Extreme Values ◽  
Regional Climate ◽  
Tail Dependence ◽  
Copula Models ◽  
Climate Simulations ◽  
Weather Situation ◽  
The Common

Abstract. This paper presents a new Copula-based method for further downscaling regional climate simulations. It is developed, applied and evaluated for selected stations in the alpine region of Germany. Apart from the common way to use Copulas to model the extreme values, a strategy is proposed which allows to model continuous time series. As the concept of Copulas requires independent and identically distributed (iid) random variables, meteorological fields are transformed using an ARMA-GARCH time series model. In this paper, we focus on the positive pairs of observed and modelled (RCM) precipitation. According to the empirical copulas, significant upper and lower tail dependence between observed and modelled precipitation can be observed. These dependence structures are further conditioned on the prevailing large-scale weather situation. Based on the derived theoretical Copula models, stochastic rainfall simulations are performed, finally allowing for bias corrected and locally refined RCM simulations.

scholarly journals Aggregation of underwriting risks in insurance industry of Iran using vine copula

2015 ◽  
Vol 5 (4) ◽  
pp. 149-161
Author(s):  
Mohammad Mirbagherijam ◽  
Mohammad Nabi Shahiki Tash ◽  
Gholamreza Zamanian ◽  
Amir Safari
Keyword(s):  
Standard Model ◽  
Risk Measures ◽  
Insurance Industry ◽  
Tail Dependence ◽  
Economic Capital ◽  
Aggregation Process ◽  
Copula Models ◽  
Risk Aggregation ◽  
Copula Method ◽  
Vine Copula

In this paper, the underwriting risks of the insurance industry of Iran were aggregated using various vine copula classes and historical data of loss ratios which corresponds to each business line. The estimated economic capital (EC) for the entire insurance industry considerably varies across different risk measures and vine copula models. In addition, less than the risk-based capital (RBC) charge assessed based on the standard model of RN69 and amounted to 96,943,391 million of Iran Rials. Therefore, it was concluded that using the Vine copula method and allowing symmetry and tail dependence for pairs of business lines’ risks in the risk aggregation process leads to overestimation of the RBC risk charge, as compared to the estimated results of simple and linear aggregation methods of such standard model. Furthermore, the choice of dependency structure and risk measures have a paramount effect on the aggregate economic capital. Highlights: Estimated aggregated economic capital varies across different risk measures and vine copula models; Selecting the appropriate copula model is an important consideration in risk aggregation process; Using the Vine copula method in the risk aggregation leads to overestimation of the RBC risk charge; The estimated economic capital is less than RBC risk charge calculated under standard model of RN69.

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