Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns

By Lorenzo Cappiello, Robert F. Engle, Kevin Sheppard. Social Science Research Network, 2006

1,707 citations recorded by the source corpus.

This paper proposes a new generalized autoregressive conditionally heteroskedastic (GARCH) process, the asymmetric generalized dynamic conditional correlation (AG-DCC) model. The AG-DCC process extends previous specifications along two dimensions: it allows for series-specific news impact and smoothing parameters and permits conditional asymmetries in correlation dynamics. The AG-DCC specification is well suited to examine correlation dynamics among different asset classes and investigate the presence of asymmetric responses in conditional variances and correlations to negative returns. We employ the AG-DCC model to analyze the behavior of international equities and government bonds. While equity returns show strong evidence of asymmetries in conditional volatility, little is found for bond returns. However, both equities and bonds exhibit asymmetries in conditional correlations, with equities responding stronger than bonds to joint bad news. The article also finds that, during periods of financial turmoil, equity market volatilities show important linkages, and conditional equity correlations among regional groups increase dramatically. Furthermore, in January 1999 with the introduction of the euro, we document significant evidence of a structural break in correlation although not in volatility. The introduction of a fixed exchange rate regime leads to near-perfect correlation among bond returns within the European Monetary Union (EMU) countries, which is not surprising when considering the harmonization in monetary policy. However, the increase in return correlation is not restricted to bond returns in EMU countries: equity return correlation both within and outside the EMU also increases. Copyright 2006, Oxford University Press.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/ca2b80247d941e9680ad5e94c82ee671b43b8a35

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Citations in the corpus

Spillover Effects of the US Financial Crisis on Financial Markets in Emerging Asian Countries (2015, neutral): “11 = 1 + 1 2 1−1 + 111−1 (11)”
Asymmetric Connectedness on the U.S. Stock Market: Bad and Good Volatility Spillovers (2015, positive): “Diebold and Yilmaz (2009) introduce a volatility spillover measure based on forecast error variance decompositions from vector auto regressions (VARs).”
U.S. and Latin American Stock Market Linkages (2011, neutral): “(See also Engle (2002) and Cappiello et al. (2006)).”
New HEAVY Models for Fat-Tailed Realized Covariances and Returns (2017, neutral): “Note that further lags of Vt and St can be added on the right-hand side of (4), as well as “asymmetry” effects as in Cappiello, Engle, and Sheppard (2006).”
QML ESTIMATION OF A CLASS OF MULTIVARIATE ASYMMETRIC GARCH MODELS (2011, methodology): “Since the inception of the univariate ARCH and GARCH models by Engle (1982) and Bollerslev (1986), a wide variety of multivariate extensions have been proposed.”
The Conditional Autoregressive Wishart Model for Multivariate Stock Market Volatility (2010, positive): “A further extension we currently investigate allows for asymmetric e ects of positive and negative news on the covariance matrix using a speci cation which is in line with that of Cappiello et al. (2006) for a multivariate GARCH model.”
Dynamic correlation analysis of financial contagion: Evidence from the Central and Eastern European markets☆ (2011, neutral): “The main body of the current literature explores the links among the developed stock markets (Hamao et al., 1990; Theodossiou and Lee, 1993; Longin and Solnik, 1995; Meric and Meric, 1997; Goetzmann et al. 2001; Cappiello et al., 2006; Kim et al., 2005), while some recent studies have extended this ”
Glossary to ARCH (GARCH) (2008, methodology): “ADCC (Asymmetric Dynamic Conditional Correlations) The ADCC GARCH model of Cappiello, Engle and Sheppard (2006) extends the DCC model (see DCC) to allow for asymmetries in the time-varying conditional correlations based on a GJR threshold-type formulation (see GJR).”
Asymmetric volatility spillover among Chinese sectors during COVID-19 (2021, background): “frequency-domain connectedness Table 2 presents the definitions of various time-domain spillover measures based on Diebold and Yilmaz (2012); Diebold and Yılmaz (2014) and the frequency-domain spillover measures based on Baruník and Křehlík (2018) and Ferrer et al.”