Time-varying efficiency of developed and emerging bond markets: Evidence from long-spans of historical data

Charfeddine, Lanouar; Khediri, Karim Ben; Aye, Goodness C.; Gupta, Rangan

doi:10.1016/j.physa.2018.04.004

Cited by 27 publications

(17 citation statements)

References 49 publications

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“…Although weekly or daily data could be used as they have the advantage of providing large number of observations, however, monthly data have the advantage of limiting noise in the data relative to daily or weekly data (Dedi and Yavas, 2016;Charfeddine et al (2018). Data on the JSE All Share Index are taken from the International Financial The study period is motivated by three factors.…”

Section: Methodsmentioning

confidence: 99%

Asymmetric volatility transmission between the real exchange rate and stock returns in South Africa

Sikhosana

Aye

2018

Economic Analysis and Policy

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This paper analyses the asymmetric volatility spillovers between the real exchange rate and stock returns in South Africa. A Multivariate Exponential Generalized Autoregressive Conditionally Heteroskedastic (EGARCH) model alongside other asymmetric GARCH models (GJR GARCH and APARCH) were estimated using monthly data from 1996 to 2016 to examine the relationship. The results show that there is a bi-directional volatility spillover effect between the two markets in the short-run. Also these effects are asymmetric. These findings suggest that while information in one market can be used to forecast changes in the other, these financial assets should not be included in the same portfolio when diversifying risk.

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Section: Methodsmentioning

confidence: 99%

Asymmetric volatility transmission between the real exchange rate and stock returns in South Africa

Sikhosana

Aye

2018

Economic Analysis and Policy

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“…For the U.S. market, this hypothesis is, for example, supported by research by Ito and Sugiyama [43] and Kim et al [44]. Further, more compelling evidence for the adaptive market hypothesis is subsequently presented by Urquhart and McGroarty [45,46], using long-term datasets for the Dow Jones Industrial Average and various global stock exchanges, respectively, and through investigations of bond markets [47].…”

Section: Resultsmentioning

confidence: 84%

Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning

Moews

Ibikunle

2020

Physica A: Statistical Mechanics and its Applications

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Standard methods and theories in finance can be ill-equipped to capture highly non-linear interactions in financial prediction problems based on large-scale datasets, with deep learning offering a way to gain insights into correlations in markets as complex systems. In this paper, we apply deep learning to econometrically constructed gradients to learn and exploit lagged correlations among S&P 500 stocks to compare model behaviour in stable and volatile market environments, and under the exclusion of target stock information for predictions. In order to measure the effect of time horizons, we predict intraday and daily stock price movements in varying interval lengths and gauge the complexity of the problem at hand with a modification of our model architecture. Our findings show that accuracies, while remaining significant and demonstrating the exploitability of lagged correlations in stock markets, decrease with shorter prediction horizons. We discuss implications for modern finance theory and our work's applicability as an investigative tool for portfolio managers. Lastly, we show that our model's performance is consistent in volatile markets by exposing it to the environment of the recent financial crisis of 2007/2008.

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“…It is noteworthy that the study of AMH has been introduced to markets other than stock markets. For instance, Charfeddine, et al [136] employed state-space GARCH-M model, which revealed time-varying efficiency in the developed US and UK and emerging South Africa and India bond markets with the US market being the most efficient. Similarly, Kumar [137] validated the AMH in the Indian FOREX market using data from 1999 to 2017.…”

Section: Time Varying Efficiency Studiesmentioning

confidence: 99%

Are African Stock Markets Inefficient or Adaptive? Empirical Literature

Obalade¹,

Muzindutsi²

2021

Emerging Markets

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This chapter reviews empirical studies on weak form of efficiency with the aim of establishing whether the African market is inefficient or adaptive. The reviewed studies are categorised based on their methodological approaches to compare the power of linear and non-linear models in testing for weak-form efficiency. The studies on calendar anomalies, an indication of weak-form inefficiency, are reviewed to assess whether these anomalies are adaptive as portrayed by the relatively recent theory of adaptive market hypothesis (AMH). The scope of reviewed studies is also extended to developed and emerging markets to gain a broad comparison of the findings. This review revealed that non-linear dependence has been revealed in stock returns suggesting that non-linear models are best fit to test for the stock market efficiency. Reviewed studies produced contradictory findings with some supporting and others rejecting weak-form efficiency. Thus, most studies support the AMH, which suggests that market efficiencies and anomalies are time changing. This chapter concludes that most of the existing studies on AMH have been carried out in markets other than Africa, and hence, further empirical studies on the evolving and changing nature of efficiency in African stock markets are recommended.

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Time-varying efficiency of developed and emerging bond markets: Evidence from long-spans of historical data

Cited by 27 publications

References 49 publications

Asymmetric volatility transmission between the real exchange rate and stock returns in South Africa

Asymmetric volatility transmission between the real exchange rate and stock returns in South Africa

Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning

Are African Stock Markets Inefficient or Adaptive? Empirical Literature

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