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A New Multivariate Nonlinear Time Series Model for Portfolio Risk Measurement: The Threshold Copula‐Based TAR Approach
Author(s) -
Wong Shiu Fung,
Tong Howell,
Siu Tak Kuen,
Lu Zudi
Publication year - 2017
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/jtsa.12206
Subject(s) - copula (linguistics) , econometrics , mathematics , value at risk , univariate , multivariate statistics , portfolio , bivariate analysis , nonlinear system , series (stratigraphy) , statistics , risk management , finance , economics , paleontology , physics , quantum mechanics , biology
We propose a threshold copula‐based nonlinear time series model for evaluating quantitative risk measures for financial portfolios with a flexible structure to incorporate nonlinearities in both univariate (component) time series and their dependent structure. We incorporate different dependent structures of asset returns over different market regimes, which are manifested in their price levels. We estimate the model parameters by a two‐stage maximum likelihood method. Real financial data and appropriate statistical tests are used to illustrate the efficacy of the proposed model. Simulated results for sampling distribution of parameters estimates are given. Empirical results suggest that the proposed model leads to significant improvement of the accuracy of value‐at‐risk forecasts at the portfolio level.