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

Jammazi, Rania; Tiwari, Aviral Kumar; Ferrer, Román; Moya, P.

doi:10.1016/j.najef.2015.03.005

Cited by 55 publications

(23 citation statements)

References 42 publications

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“…These findings suggest that flight to safety phenomenon is short-lived because of positive dependence between stock and benchmark bonds at various quantiles. This is in line with the conventional wisdom that common macroeconomic conditions, such as the expected inflation or economic prospects, drive both stock and bond markets (Jammazi et al 2015;Bodart and Reding, 1999;Ilmanen, 2003). These observations suggest that benchmark bond indices of these countries are not perfect hedge in most of the quantiles.…”

Section: Estimates Of the Qq Approachsupporting

confidence: 85%

Dependence of stock markets with gold and bonds under bullish and bearish market states

et al. 2017

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This paper examines the dependence of gold and benchmark bonds with ten stock markets including five larger developed markets (e.g. USA, UK, Japan, Canada and Germany) and five Eurozone peripheral GIPSI countries (Greece, Ireland, Portuguese, Spain and Ireland) stock markets. We use a novel quantile-on-quantile (QQ) approach to construct the dependence estimates of the quantiles of gold and bond with the quantiles of stock markets. The QQ approach, recently developed by Sim and Zhou (2015), captures the dependence between the entire distributions of financial assets and uncovers some nuance features of the relationship. The empirical findings primarily show that gold is strong hedge and diversifier for the stock portfolio except when both the markets are under stress. Further, the flight to safety phenomenon is shortlived because national benchmark bonds exhibit a positive dependence with their respective countries stock indices at various quantiles. Unlike the existing literature, the QQ approach suggest that bonds act as safe havens for the stock portfolio but gold does not. Our findings also suggest that dependence between stock-gold and stock-bond pairs is not uniform and this relationship is market state (e.g. bearish, mild bearish, optimistic or bullish) and country specific.

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Section: Estimates Of the Qq Approachsupporting

confidence: 85%

Dependence of stock markets with gold and bonds under bullish and bearish market states

et al. 2017

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“…For example, Morales, Di Matteo, and Aste (2014) used a correlation-based clustering algorithm to show the structure of dependency between stocks in dif- Jammazi, Tiwari, Ferrer, and Moya (2015) reported that the dependence structure between stock and 10-year government bond returns varies significantly over time for most countries. For example, Morales, Di Matteo, and Aste (2014) used a correlation-based clustering algorithm to show the structure of dependency between stocks in dif- Jammazi, Tiwari, Ferrer, and Moya (2015) reported that the dependence structure between stock and 10-year government bond returns varies significantly over time for most countries.…”

Section: Empirical Studymentioning

confidence: 99%

“…Many studies are devoted to investigating the change of structures among stock markets in the literature, since the return and risk of securities are affected by macroeconomic information. For example, Morales, Di Matteo, and Aste (2014) used a correlation-based clustering algorithm to show the structure of dependency between stocks in dif- Jammazi, Tiwari, Ferrer, and Moya (2015) reported that the dependence structure between stock and 10-year government bond returns varies significantly over time for most countries. Cerrato, Crosby, Kim, and Zhao (2017) provided empirical results that two extreme equity portfolios with higher-order components are closely correlated and that their dependence structure is strongly time varying and nonlinear.…”

Section: Empirical Studymentioning

confidence: 99%

Bayesian structure selection for vector autoregression model

Chu

Huang

et al. 2019

Journal of Forecasting

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A vector autoregression (VAR) model is powerful for analyzing economic data as it can be used to simultaneously handle multiple time series from different sources. However, in the VAR model, we need to address the problem of substantial coefficient dimensionality, which would cause some computational problems for coefficient inference. To reduce the dimensionality, one could take model structures into account based on prior knowledge. In this paper, group structures of the coefficient matrices are considered. Because of the different types of VAR structures, corresponding Markov chain Monte Carlo algorithms are proposed to generate posterior samples for performing inference of the structure selection. Simulation studies and a real example are used to demonstrate the performances of the proposed Bayesian approaches.

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“…For instance, if government wishes to finance budget deficits, it could borrow from the markets through issuing bonds with favorable rates of return, this could shift focus of investors from equities to stocks. Several studies that been carried out to check correlation between Stocks and bonds include: Jammazi, et al [1] and Andersson et al [2]; Li and Zou [3]; Ahmed and Joher [4]; Rankin and Idil [5]. Fan and Mitchell [6] state that, Equity-bond correlation is not a static number and can either be positive or negative.…”

Section: Stocks and Bondsmentioning

confidence: 99%

“…Jammazi et al [1] examined the dynamic interactions between stock and long-term government bond markets for several countries. The authors used time-varying DCC-GARCH-copula approach.…”

Section: Empirical Reviewmentioning

confidence: 99%

Stocks, Bonds and Foreign Exchange Inter-linkages: A Market Micro-structure Perspective in Kenya

Lidiema¹,

Macharia²

2017

JFE

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This paper examined Market Micro-structure interrelations between Stocks, Bonds and Foreign Exchange Markets. The Study analyzed both unconditional correlation, dynamic relationship and volatility spillovers effects between the markets. The analysis used the asymmetric dynamic conditional correlation (aDCC) using Exponential Generalized Autoregressive Conditional Heteroskedastic model (EGARCH). Using monthly data from Kenyan market during the period January 2004 -June 2017, results indicate that there is significant market interactions and interlinkages and existence time varying variance correlation between any bivariate set in the three markets in Kenya. While, the conditional correlations are positive, the unconditional correlation reveal, a negative correlation between Foreign exchange markets and the Bonds as well as stocks. The study proposes policy makers like the government through treasury, capital markets authority (CMA) and Nairobi securities exchange (NSE) to encourage more Kenyan investors to invest in bond market by marketing the bond market through educational forums, conferences. For the Proposal to take effect, appropriate authorities should stabilize the Kenyan currency through monetary interventions.

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

Cited by 55 publications

References 42 publications

Dependence of stock markets with gold and bonds under bullish and bearish market states

Dependence of stock markets with gold and bonds under bullish and bearish market states

Bayesian structure selection for vector autoregression model

Stocks, Bonds and Foreign Exchange Inter-linkages: A Market Micro-structure Perspective in Kenya

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