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

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.

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.

scholarly journals Multiscale Quantile Correlation Coefficient: Measuring Tail Dependence of Financial Time Series

2020 ◽  
Vol 12 (12) ◽  
pp. 4908
Author(s):  
Chao Xu ◽  
Jinchuan Ke ◽  
Xiaojun Zhao ◽  
Xiaofang Zhao
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Financial Markets ◽  
Correlation Coefficient ◽  
Multiscale Analysis ◽  
Financial Time Series ◽  
Tail Dependence ◽  
Coarse Graining ◽  
Explanatory Variables ◽  
Financial Time

In the context of the frequent occurrence of extreme events, measuring the tail dependence of financial time series is essential for maintaining the sustainable development of financial markets. In this paper, a multiscale quantile correlation coefficient (MQCC) is proposed to measure the tail dependence of financial time series. The new MQCC method consists of two parts: the multiscale analysis and the correlation analysis. In the multiscale analysis, the coarse graining approach is used to study the financial time series on multiple temporal scales. In the correlation analysis, the quantile correlation coefficient is applied to quantify the correlation strength of different data quantiles, especially regarding the difference and the symmetry of tails. One reason to adopt this method is that the conditional distribution of the explanatory variables can be characterized by the quantile regression, rather than simply by the conditional expectation analysis in the traditional regression. By applying the MQCC method in the financial markets of different regions, many interesting results can be obtained. It is worth noting that there are significant differences in tail dependence between different types of financial markets.

scholarly journals Nonlinear Behaviors of Tail Dependence and Cross-Correlation of Financial Time Series Model

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Wei Deng ◽  
Jun Wang
Keyword(s):  
Time Series ◽  
Cross Correlation ◽  
Financial Time Series ◽  
Tail Dependence ◽  
Return Time ◽  
Financial Time ◽  
Cross Correlation Analysis ◽  
Market Inefficiency ◽  
Cross Correlations ◽  
Nonlinear Behaviors

Nonlinear behaviors of tail dependence and cross-correlation of financial time series are reproduced and investigated by stochastic voter dynamic system. The voter process is a continuous-time Markov process and is one of the interacting dynamic systems. The tail dependence of return time series for pairs of Chinese stock markets and the proposed financial models is studied by copula analysis, in an attempt to detect and illustrate the existence of relevant correlation relationships. Further, the multifractality of cross-correlations for return series is studied by multifractal detrended cross-correlation analysis, which indicates the analogous cross-correlations and some fractal characters for both actual data and simulative data and provides an intuitive evidence for market inefficiency.

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