Predicting BRICS stock returns using ARFIMA models

Aye, Goodness C.; Balcılar, Mehmet; Gupta, Rangan; Kilimani, Nicholas; Nakumuryango, Amandine; Redford, Siobhan

doi:10.1080/09603107.2014.924297

Cited by 27 publications

(12 citation statements)

References 72 publications

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“…The findings revealed that there exists positive correlation between all the four series. Aye et al (2012) examined the existence of long memory in daily stock market returns from BRICS countries using Autoregressive Fractionally Integrated Moving Average (ARFIMA) models in predicting stock returns. The outcome suggested that ARFIMA models produced better forecasting results than the non-ARFIMA (AR, MA, ARMA and GARCH) models.…”

Section: Us-brics Marketsmentioning

confidence: 99%

Intensity of shock transmission amid US-BRICS markets

Lakshmi

Visalakshmi

Shanmugam³

2015

International Journal of Emerging Markets

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Purpose -The purpose of this paper is to analyze the intensity of transmission of shocks from USA to BRICS countries in the long-run and short-run deviations and swiftness of recovery during US subprime mortgage crisis. This analysis enables the authors to explore the evolving patterns of relationships between these markets and examine whether their co-movements altered either in response to international shocks that originated in advanced markets like USA or due to their domestic fluctuations. Design/methodology/approach -Employing data of daily stock market indices (open and close) of BRICS countries for the period January 2, 2001 to May 31, 2012, this paper examines the interactions and characteristics of price movements of BRICS with US market by applying co-integration tests, vector error correction model and Granger causality relationship. The daily stock market indices data are derived from respective stock exchange web sites. Findings -The results exhibit that both long-run co-integration relationships and short-run Granger causality relationships exist between the stock markets of US-BRICS. Furthermore, this nexus is amplified in the short-run during 2007-2009, when the subprime mortgage financial crisis in the USA cropped up. This finding lends support to the prominence of developed (US) market links in the proliferation of persistent co-movements of BRICS stock markets.Research limitations/implications -The findings imply an increasing degree of global market integration due to quick dissemination of global shocks originating from developed market like USA, and swift recovery which can be attributed to the increased resilience, consistent with the moderated level of domestically driven risk in the BRICS markets. In spite of their similarities, long-run and short-run interdependences with the US stock market exhibit differences among the BRICS. This can be attributed to the regional heterogeneity in long-run risk and return co-movements with the USA. Practical implications -Changes from the US index easily affect these stock markets in the shortrun, which implies that the US index may act as a leading indicator for investing funds in BRICS markets. Originality/value -This study would enable the authors to understand whether BRICS economies actually remain resilient to adverse developments in USA and could serve as alternative investment destinations for global portfolio diversification.

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Section: Us-brics Marketsmentioning

confidence: 99%

Intensity of shock transmission amid US-BRICS markets

Lakshmi

Visalakshmi

Shanmugam³

2015

International Journal of Emerging Markets

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“…As far as the econometric framework is concerned, unlike the existing studies on modeling volatility of the BRICS stock markets based on univariate models from the generalized autoregressive conditional heteroskedasticity (GARCH)-family (see for example, Babikir et al, (2012), Aye et al, (2014), Kishor & Singh (2014), Adu et al, (2015), Bouri et al, (2018) for detailed reviews), we use the GARCH variant of the mixed data sampling (MIDAS), i.e., the GARCH-MIDAS model. The reason behind this is that, while stock market data is at a daily frequency, the oil shocks used as predictors are available only at the monthly frequency, and hence the modeling of volatility requires a MIDAS-based approach.…”

Section: Introductionmentioning

confidence: 99%

Oil shocks and stock market volatility of the BRICS: A GARCH-MIDAS approach

Salisu

Gupta

2021

Global Finance Journal

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In this study, we employ the GARCH-MIDAS model to investigate the response of stock market volatility of the BRICS to oil shocks. We utilize the recent datasets of Baumeister & Hamilton (2019) where oil shocks are decomposed into four variants -oil supply shocks, economic activity shocks, oil consumption shocks, and oil inventory shocks. We further decomposed each of these shocks into positive and negative shocks, and our findings show heterogeneous response of stock market volatility of the BRICS countries to the alternative oil shocks including the positive and negative shocks. The differing responses across the BRICS countries could be attributed to the difference in the economic size, oil production and consumption profile, market share distribution across firms, as well as financial system and regulation efficiency.

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“…From several previous studies, the ARFIMA model build predictive values with better accuracy than other models because it has flexibility, robustness and able to accommodate long memory effects, including (Kartikasari, 2020) show that the ARFIMA model show better result than the LSTAR and FILSTAR models. Aye et al, (2014) examines the effect of long memory daily stock returns from Brazil, Russia, India, China, and South Africa, also explain the efficacy of the ARFIMA model. (Baillie & Morana, 2012) examines simple adaptive modification of the basic ARFIMA model using the flexible Fourier form which is enable for a variety of interceptions In the long memory case the stationary process causes the autocorrelation function to slow down slowly.…”

Section: Arfima Modelmentioning

confidence: 99%

Autoregressive Fractional Integrated Moving Average (Arfima) Model to Predict Covid-19 Pandemic Cases in Indonesia

Kartikasari

Yasin

Maruddani

2021

Medstat

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Currently the emergence of the novel coronavirus (Sars-Cov-2), which causes the COVID-19 pandemic and has become a serious health problem because of the high risk causes of death. Therefore, fast and appropriate action is needed to reduce the spread of the COVID-19 pandemic. One of the way is to build a prediction model so that it can be a reference in taking steps to overcome them. Because of the nature of transmission of this disease which is so fast and massive cause extreme data fluctuations and between objects whose observational distances are far enough correlated with each other (long memory). The result of this determination is the best ARFIMA model obtained to predict additional of recovering cases of COVID-19 is (1,0,489.0) with an SMAPE value of 12,44%, while the case of death is (1.0.429.0) with SMAPE value of 13,52%. This shows that the ARFIMA model can accommodate well the long memory effect, resulting in a small bias. Also in estimating model parameters, it is also simpler. For cases of recovery and death, the number is increasing even though the case of death is still very high compared to cases of recovery.

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Predicting BRICS stock returns using ARFIMA models

Abstract: This paper examines the existence of long memory in daily stock market returns from Brazil, Russia, India, China, and South Africa (BRICS)

Cited by 27 publications

References 72 publications

Intensity of shock transmission amid US-BRICS markets

Intensity of shock transmission amid US-BRICS markets

Oil shocks and stock market volatility of the BRICS: A GARCH-MIDAS approach

Autoregressive Fractional Integrated Moving Average (Arfima) Model to Predict Covid-19 Pandemic Cases in Indonesia

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