An economic evaluation of stock–bond return comovements with copula-based GARCH models

Abstract

Owing to their importance in asset allocation strategies, the comovements between the stock and bond markets have become an increasingly popular issue in financial economics. Moreover, the copula theory can be utilized to construct a flexible joint distribution that allows for skewness in the distribution of asset returns as well as asymmetry in the dependence structure between asset returns. Therefore, this paper proposes three classes of copula-based GARCH models to describe the time-varying dependence structure of stock-bond returns, and then examines the economic value of copula-based GARCH models in the asset allocation strategy. We compare their out-of-sample performance with other models, including the passive, the constant conditional correlation (CCC) GARCH and the dynamic conditional correlation (DCC) GARCH models. From the empirical results, we find that a dynamic strategy based on the GJR-GARCH model with Student-t copula yields larger economic gains than passive and other dynamic strategies. Moreover, a less risk-averse investor will pay higher performance fees to switch from a passive strategy to a dynamic strategy based on copula-based GARCH models.

Time-varying dependence between stock and government bond returns: International evidence with dynamic copulas

“…This parameter is always positive with the only exceptions of Ireland and Greece. This result implies a high degree of persistence over time in the dependence pattern between international stock and government bond returns, consistent with findings from earlier related studies (Chang & Hsueh, 2013;Wu & Liang, 2011;Wu & Lin, 2014). The parameter « 2 is also strongly significant for nearly all countries, indicating substantial variations over time in the stock-bond dependence.…”

“…The dynamics of stock-bond comovement in the US is fully in line with that documented by various recent copula-based studies (Chui & Yang, 2012;Durand et al, 2010;Nguyen & Nguyen, 2014;Wu & Lin, 2014). Specifically, a positive relationship between stock and government bond returns is found in the 1990s, a period of relatively high inflation and low uncertainty about real economic activity.…”

“…All return series have been collected from Thomson Financial Datastream. Following several other studies (EvrimMandaci, Kahyaoglu, & Cagli, 2011;Wu & Liang, 2011;Wu & Lin, 2014), weekly return series (from Wednesday to Wednesday) are used (a total of 1058 observations). The weekly frequency is preferred to daily and monthly frequencies for various reasons.…”

“…An obvious limitation of multivariate GARCH processes is that they are built on the assumption that the conditional joint distribution of stock and bond returns follows an elliptical distribution with linear correlation, such as the multivariate Gaussian or Student-t. Nevertheless, this assumption is violated in many cases as the real association between financial returns is often more complicated, including for example nonlinear or tail dependence (Wu & Lin, 2014).…”

“…Initial copula-based studies in this field concentrated on constant copulas (Durand, Junker, & Szimayer, 2010;Gonzalo & Olmo, 2005). However, more recent contributions consider time-varying (conditional) copulas, which allow better characterizing the dynamic comovement between stock and government bond markets (Chang & Hsueh, 2013;Chui & Yang, 2012;Nguyen & Nguyen, 2014;Wu & Liang, 2011;Wu & Lin, 2014). These studies are predominantly focused on the US case (with the exception of Chang & Hsueh, 2013) and document a significant variation over time in the stock-bond nexus.…”