A Markov regime switching approach to estimating the volatility of Johannesburg Stock Exchange (JSE) returns

Oseifuah, Emmanuel K.; Korkpoe, Carl H.

doi:10.21511/imfi.16(1).2019.17

Cited by 2 publications

(4 citation statements)

References 50 publications

(27 reference statements)

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“…This means that the gain in the complexity of the two regime model was not enough to outdo the single regime. This result contradicts the findings of Oseifuah and Korkpoe (2019), who discovered that the two regime models outperformed the single regime models in the JSE. However, the differences may be attributed to the different sample periods.…”

Section: Resultscontrasting

confidence: 99%

“…However, this was not the case with the JSE stock data, where the single regime model outperformed the two regime models. This contradicts the findings of Oseifuah and Korkpoe (2019) and Makatjane and Molefe (2020) who found the single regime less effective. However, we are quick to note that we used a period that starts in 2017, whilst the studies mentioned above-considered sample periods that cover the global financial crisis of 2008.…”

Section: Discussioncontrasting

confidence: 84%

“…Both studies show that the two-regime models improved in-sample forecasts. Oseifuah and Korkpoe (2019) and Shiferaw (2018) apply the MSGARCH models to the JSE stock return data. In both studies, they used the Bayesian approach.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Resultscontrasting

confidence: 99%

Section: Discussioncontrasting

confidence: 84%

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

Kaseke

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“…Ardia et al [26], Oseifuah et al [27], and Xiaofei Wu et al [28] empirically showed the supremacy of the Markov-switching GARCH against non-switching GARCH models in estimating risk measures like Value-at-Risk (VaR), Expected Shortfall (ES), & left-tail distribution forecasts using financial indices. Since risk measures are better estimated by a statistical distribution that best describes the returns [11] their findings suggested that the presence of regimes in the volatility dynamics of the financial returns is prevalent.…”

Section: Literature Reviewmentioning

confidence: 99%

Estimating Extreme Value at Risk Using Bayesian Markov Regime Switching GARCH-EVT Family Models

Ndlovu,

Chikobvu

2024

Cryptocurrencies - Financial Technologies of the Future [Working Title]

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In this study, the performance of the Bayesian Markov regime-switching GARCH-EVT in the estimation of extreme value at risk in the BitCoin/dollar (BTC/USD) and the South African Rand/dollar (ZAR/USD) exchange rates is investigated. The goal is to capture regime switches and extreme returns to exchange rates, all to explain and compare the riskiness of BitCoin and the Rand. The Markov chain Monte Carlo method is used to estimate parameters for the GARCH family models. Using the deviance information criterion, the two regime-switching GARCH models perform better than the single-regime GARCH model when modelling volatility of the two currencies’ returns. Based on the estimated value at risk figures, BitCoin is riskier than the Rand. At both 95% and 99% levels of significance, the results suggest that the MS(2)-gjrGARCH(1,1)-GEVD7 and MS(2)-sGARCH(1,1)-GPD7 are the best fitting models for both BTC/USD and ZAR/USD respectively, at both significance levels. The backtest confirms model adequacy. This information is useful to local and foreign currency traders and investors who need to fully appreciate the risk exposure when they convert their savings or investments to BitCoin instead of the South African currency, the Rand.

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A Markov regime switching approach to estimating the volatility of Johannesburg Stock Exchange (JSE) returns

Cited by 2 publications

References 50 publications

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

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

Estimating Extreme Value at Risk Using Bayesian Markov Regime Switching GARCH-EVT Family Models

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