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Measuring Asset Market Linkages : Nonlinear Dependence and Tail Risk

Traditional measures of dependence in time series are based on correlations or periodograms. These are adequate in many circumstances but, in others, especially when trying to assess market linkages and tail risk during abnormal times (e.g., financial contagion), they might be inappropriate. In particular, popular tail dependence measures based on exceedance correlations and marginal expected shortfall (MES) have large variances and also contain limited information on tail risk. Motivated by these limitations, we introduce the (tail-restricted) integrated regression function, and we show how it characterizes conditional dependence and persistence. We propose simple estimates for these measures and establish their asymptotic properties. We employ the proposed methods to analyze the dependence structure of some of the major international stock market indices before, during, and after the 2007–2009 financial crisis. Monte Carlo simulations and the application show that our new measures are more reliable and accurate than competing methods based on MES or exceedance correlations for testing tail dependence.

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Informasi Detail

Judul Seri

JOURNAL OF BUSINESS AND ECONOMIC STATISTICS; Vol.39 No.2 April 2021

No. Panggil

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Penerbit

: .,

Deskripsi Fisik

p. 453-465

Bahasa

English

ISBN/ISSN

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Klasifikasi

NONE

Tipe Isi

-

Tipe Media

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Tipe Pembawa

-

Edisi

-

Subjek

NONLINEAR DEPENDENCE
MARKET CRASHES
EXPECTED SHORTFALL
TAIL RISK

Info Detail Spesifik

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Pernyataan Tanggungjawab

Juan Carlos Escanciano, Javier Hualde

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