Asymmetric Mean Reversion in Low Liquid Markets: Evidence from BRVM

Gbenro, Nathaniel; Moussa, Richard K.

doi:10.3390/jrfm12010038

Cited by 10 publications

(3 citation statements)

References 27 publications

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“…This might be since the stock market efficiency is time varying [37]. In this case, using a rolling regression approach can help in analyzing the efficient markets hypothesis [38]. Furthermore, there is a long memory property in some CEE stock markets [39] that is not consistent with efficiency.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Multifractal Detrended Fluctuation Analysis (MF-DFA) of Stock Market Indexes. Empirical Evidence from Seven Central and Eastern European Markets

et al. 2020

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In this paper, we present a comparative investigation of the multifractal properties of seven Central and Eastern European (CEE) stock markets using recent financial data up to August 2018 by employing seasonal and trend decompositions before applying multifractal detrended fluctuation analysis. We find that stock indices returns exhibit long-range correlations, supporting the idea that the stock markets in question are not efficient markets and have not reached a mature stage of market development. The results of the paper are of interest to investors looking for opportunities in these stock exchanges and also to policy makers in their endeavour of realizing institutional reforms in order to increase stock market efficiency and to support the sustainable growth of the financial markets.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Multifractal Detrended Fluctuation Analysis (MF-DFA) of Stock Market Indexes. Empirical Evidence from Seven Central and Eastern European Markets

et al. 2020

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“…Another debated issue in the analysis of emerging markets is related to market efficiency and its link with market liquidity conditions. Gbenro and Moussa (2019) provide an empirical investigation of market efficiency in the West African Regional Stock Market (BRVM), using non-linear autoregressive conditionally heteroskedastic models to estimate the speed of mean reversion for two stock market indices. Their findings provide some evidence of the presence of market inefficiencies in the West African Regional Stock Market, in which intensity is, however slightly, reducing over time.…”

Section: Outline Of the Volumementioning

confidence: 99%

Financial Time Series: Methods and Models

Caporin

Storti

2020

JRFM

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The statistical analysis of financial time series is a rich and diversified research field whose inherent complexity requires an interdisciplinary approach, gathering together several disciplines, such as statistics, economics, and computational sciences. This special issue of the Journal of Risk and Financial Management on “Financial Time Series: Methods & Models” contributes to the evolution of research on the analysis of financial time series by presenting a diversified collection of scientific contributions exploring different lines of research within this field.

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“…If the persistence parameter is less than 1, the return series will exhibit mean reversion. However, if the persistence parameter is equal to 1, then the series follows the random walk (Gbenro and Moussa 2019).…”

Section: Statisticmentioning

confidence: 99%

Financial modelling of cryptocurrency: a case study of Bitcoin, Ethereum, and Dogecoin in comparison with JSE stock returns.

Kaseke

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The emergency of cryptocurrency has caused a shift in the financial markets. Although it was created as a currency for exchange, cryptocurrency has been shown to be an asset, with investors seeking to profit from it rather than using it as a medium of exchange. Despite being a financial asset, cryptocurrency has distinct, stylised facts like any other asset. Studying these stylised facts allows the creation of better-suited models to assist investors in making better data-driven decisions. The data used in this thesis was of three leading cryptocurrencies: Bitcoin, Ethereum, and Dogecoin and the Johannesburg Stock Exchange (JSE) data as a guide for comparison. The sample period was from 18 September 2017 to 27 May 2021. The goal was to research the stylised facts of cryptocurrencies and then create models that capture these stylised facts. The study developed risk-quantifying models for cryptocurrencies. The main findings were that cryptocurrency exhibits stylised facts that are well-known in financial data. However, the magnitude and frequency of these stylised facts tend to differ. For example, cryptocurrency is more volatile than stock returns. The volatility also tends to be more persistent than in stocks. The study also finds that cryptocurrency has a reverse leverage effect as opposed to the normal one, where past negative returns increase volatility more than past positive returns. The study also developed a hybrid GARCH model using the extreme value theorem for quantifying cryptocurrency risk. The results showed that the GJR-GARCH with GDP innovations could be used as an alternative model to calculate the VaR. The volatile nature of cryptocurrency was also compared with that of the JSE while accounting for structural breaks and while not accounting for them. The results showed that the cryptocurrencies’ volatility patterns are similar but differ from those of the JSE. The cryptocurrency was also found to be an inefficient market. This finding means that some investors can take advantage of this inefficiency. The study also revealed that structural breaks affect volatility persistence. However, this persistence measure differs depending on the model used. Markov switching GARCH models were used to strengthen the structural break findings. The results showed that two-regime models outperform single-regime models. The VAR and DCC-GARCH models were also used to test the spillovers amongst the assets used. The results showed short-run spillovers from Bitcoin to Ethereum and long-run spillovers based on the DCC-GARCH. Lastly, factors affecting cryptocurrency adoption were discussed. The main reasons affecting mass adoption are the complexity that comes with the use of cryptocurrency and its high volatility. This study was critical as it gives investors an understanding of the nature and behaviour of cryptocurrency so that they know when and how to invest. It also helps policymakers and financial institutions decide how to treat or use cryptocurrency within the economy.

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Asymmetric Mean Reversion in Low Liquid Markets: Evidence from BRVM

Cited by 10 publications

References 27 publications

Multifractal Detrended Fluctuation Analysis (MF-DFA) of Stock Market Indexes. Empirical Evidence from Seven Central and Eastern European Markets

Multifractal Detrended Fluctuation Analysis (MF-DFA) of Stock Market Indexes. Empirical Evidence from Seven Central and Eastern European Markets

Financial Time Series: Methods and Models

Financial modelling of cryptocurrency: a case study of Bitcoin, Ethereum, and Dogecoin in comparison with JSE stock returns.

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