The General Tail Dependence Function in the Marshall-Olkin and Other Parametric Copula Models with an Application to Financial Time Series

Sankhya B ◽  
2021 ◽  
Author(s):  
Yuri Salazar Flores ◽  
Adán Díaz-Hernández
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Tail Dependence ◽  
Dependence Function ◽  
Copula Models ◽  
Financial Time ◽  
Parametric Copula

Time-varying copula models for financial time series

2016 ◽  
Vol 48 (A) ◽  
pp. 159-180
Author(s):  
Rüdiger Kiesel ◽  
Magda Mroz ◽  
Ulrich Stadtmüller
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Multivariate Time Series ◽  
Local Maximum ◽  
Time Varying ◽  
Monte Carlo Simulation Study ◽  
Copula Models ◽  
Copula Theory ◽  
Statistical Homogeneity ◽  
Financial Time

AbstractWe perform an analysis of the potential time inhomogeneity in the dependence between multiple financial time series. To this end, we use the framework of copula theory and tackle the question of whether dependencies in such a case can be assumed constant throughout time or rather have to be modeled in a time-inhomogeneous way. We focus on parametric copula models and suitable inference techniques in the context of a special copula-based multivariate time series model. A recent result due to Chan et al. (2009) is used to derive the joint limiting distribution of local maximum-likelihood estimators on overlapping samples. By restricting the overlap to be fixed, we establish the limiting law of the maximum of the estimator series. Based on the limiting distributions, we develop statistical homogeneity tests, and investigate their local power properties. A Monte Carlo simulation study demonstrates that bootstrapped variance estimates are needed in finite samples. Empirical analyses on real-world financial data finally confirm that time-varying parameters are an exception rather than the rule.

DETECTING AND MODELING TAIL DEPENDENCE

2004 ◽  
Vol 07 (03) ◽  
pp. 269-287 ◽  
Author(s):  
FABIO BELLINI ◽  
GIANNA FIGÀ-TALAMANCA
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Tail Dependence ◽  
Financial Data ◽  
Garch Models ◽  
Serial Dependence ◽  
Simple Method ◽  
Financial Time ◽  
Empirical Investigations

The aim of this work is to develop a nonparametric tool for detecting dependence in the tails of financial data. We provide a simple method to locate and measure serial dependence in the tails, based on runs tests. Our empirical investigations on many financial time series reveal a strong departure from independence for daily logreturns, which is not filtered out by usual Garch models.

Dynamic tail dependence clustering of financial time series

2015 ◽  
Vol 58 (3) ◽  
pp. 641-657 ◽  
Author(s):  
Giovanni De Luca ◽  
Paola Zuccolotto
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Tail Dependence ◽  
Financial Time

scholarly journals A double clustering algorithm for financial time series based on extreme events

2017 ◽  
Vol 34 (1-2) ◽  
pp. 1-12 ◽  
Author(s):  
Giovanni De Luca ◽  
Paola Zuccolotto
Keyword(s):  
Time Series ◽  
Extreme Events ◽  
Clustering Algorithm ◽  
Financial Time Series ◽  
Tail Dependence ◽  
Copula Functions ◽  
Financial Time ◽  
Crisis Scenarios ◽  
Double Clustering ◽  
Extreme Returns

AbstractThis paper is concerned with a procedure for financial time series clustering, aimed at creating groups of time series characterized by similar behavior with regard to extreme events. The core of our proposal is a double clustering procedure: the former is based on the lower tail dependence of all the possible pairs of time series, the latter on the upper tail dependence. Tail dependence coefficients are estimated with copula functions. The final goal is to exploit the two clustering solutions in an algorithm designed to create a portfolio that maximizes the probability of joint positive extreme returns while minimizing the risk of joint negative extreme returns. In financial crisis scenarios, such a portfolio is expected to outperform portfolios generated by the traditional methods. We describe the results of a simulation study and, finally, we apply the procedure to a dataset composed of the 50 assets included in the EUROSTOXX index.

scholarly journals A Maximal Tail Dependence-Based Clustering Procedure for Financial Time Series and Its Applications in Portfolio Selection

Risks ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 115 ◽  
Author(s):  
Xin Liu ◽  
Jiang Wu ◽  
Chen Yang ◽  
Wenjun Jiang
Keyword(s):  
Time Series ◽  
Portfolio Selection ◽  
Financial Time Series ◽  
Tail Dependence ◽  
Simulation Studies ◽  
Lower Tail ◽  
Financial Time ◽  
The Matrix ◽  
Mean Variance Portfolio ◽  
Mean Variance

In this paper, we propose a clustering procedure of financial time series according to the coefficient of weak lower-tail maximal dependence (WLTMD). Due to the potential asymmetry of the matrix of WLTMD coefficients, the clustering procedure is based on a generalized weighted cuts method instead of the dissimilarity-based methods. The performance of the new clustering procedure is evaluated by simulation studies. Finally, we illustrate that the optimal mean-variance portfolio constructed based on the resulting clusters manages to reduce the risk of simultaneous large losses effectively.

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