Volatility Model Choice for Sub-Saharan Frontier Equity Markets - A Markov Regime Switching Bayesian Approach

Korkpoe, Carl H.; Howard, Nathaniel

doi:10.5195/emaj.2019.172

Cited by 7 publications

(3 citation statements)

References 54 publications

(48 reference statements)

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“…The MS(3)-AR(2) provides the empirical results of Nigeria's stock returns in three distinct phases; accumulation/distribution, big-move and excess/panic regimes. This finding is unique as compared to related studies such as Aliyu and Wambai (2018), Korkpoe and Howard (2019) and Yahaya and Adeoye (2020) whose studies provided evidence of Nigeria's stock market in two eras (appreciation and depreciation). Also, evidence from the three-regimes [MS(3)-AR(2)] estimation, established a high probability that the returns' system remains in the same state, it implied that only unconventional or severe events can switch the series from regime 1(accumulation/distribution phase) Markov Regime-Switching Autoregressive Model of Stock Market Returns in Nigeria Adejumo et al…”

Section: Conclusion and Policy Recommendationscontrasting

confidence: 43%

Markov Regime-Switching Autoregressive Model of Stock Market Returns in Nigeria

Adejumo

Albert

Asemota³

2021

CBN-JAS

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This study is designed to model and forecast Nigeria’s stock market using the All-Share Index (ASI) as a proxy. By employing the Markov regime-switching autoregressive (MS-AR) model with data from April 2005 to September 2019, the study analyzes the stock market volatility in three distinct regimes (accumulation or distribution – regime 1; big-move – regime 2; and excess or panic phases – regime 3) of the bull and bear periods. Six MS-AR candidate models are estimated and based on the minimum AIC value, MS(3)-AR(2) is returned as the optimal model among the six candidate models. The MS(3)-AR(2) analysis provides evidence of regime-switching behaviour in the stock market. The study also shows that only extreme events can switch the ASI returns from regime 1 to regime 2 and to regime 3, or vice versa. It further specifies an average duration period of 9, 3 and 4 weeks for the accumulation/distribution, big-move and excess/panic regimes respectively which is an evidence of favorable market for investors to trade. Based on Root Mean Square Error and Mean Absolute Error, the fitted MS-AR model is adjudged the most appropriate ASI returns forecasting model. The study recommends investments in stock across the regimes that are switching between accumulation/distribution and big-move phases for promising returns.

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Section: Conclusion and Policy Recommendationscontrasting

confidence: 43%

Markov Regime-Switching Autoregressive Model of Stock Market Returns in Nigeria

Adejumo

Albert

Asemota³

2021

CBN-JAS

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“…Hu and Shin (2008) applied MS-GARCH modeling by using weekly stock market index data of developing countries in East Asia. Marcucci (2005), Wang and Theobald (2008), Visković, Arnerić and Rozga (2014), Abounoori, Elmi and Nademi (2016), Lolea and Vilcu (2018) and Korkpoe and Howard (2019) applied MS-GARCH models on various stock market indexes. Ardia, Bluteau and Rüede (2019) found that the volatility structure of the bitcoin market shows regime changes.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting of Volatility in Stock Exchange Markets by MS-Garch Approach: An Application of Borsa Istanbul

Kaya¹,

Yarbaşi²

2021

Ekonomi, Politika &Amp; Finans Araştırmaları Dergisi

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The volatility observed in securities markets has an important influence on the decision making processes of stock market stakeholders. In this study, the volatilities in BIST100 index which represents Borsa Istanbul was analyzed. For this purpose, BIST100 index closing data for the period of 03.01.1988-20.04.2018 was used in the study. The BIST100 index was analyzed by Markov regime switching GARCH (MS-GARCH) with three regimes, standard, high and low volatility regimes. As a result of the triple regime MS-GARCH intensive analysis, the existence of endogenous regimens was determined, in which the regime coefficients considered for the index were statistically significant. When the possibilities of regime transitions are analyzed, it is determined that the probability of continuing the standard volatility regime is 0.62, the probability of transition to low volatility regime is 0.23 and the probability of transition to high volatility regime is 0.145. Moreover, it was determined that the possibilities of regime passage in 5 and 20 days are very close to each other.

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“…This model has become very prevalent, particularly in applied research. The regime-switching model has gained the attention of many scholars like Calvet and Fisher (2004), Masoud et al (2012), Beckmann and Czudaj (2013), Lux et al (2014), Nguyen and Walid (2014), Aliyu and Wambai (2018), Korkpoe and Howard (2019), Yahaya and Adeoye (2020) to mention but few. They have documented the distinctiveness and forecasting capabilities of Markov regime-switching against the commonly used GARCH models.…”

Section: Introductionmentioning

confidence: 99%

The Regime Examination of Nigeria Exchange Rate Volatility: Evidence from Markov Regime Switching Autoregressive Approach

S.D.,

O.A.

2023

African Journal of Accounting and Financial Research

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Inarguably, the escalation in dollar rates and the price instability in the Nigerian economy underwent significant structural and institutional changes. In assessing the importance of understanding exchange rates, it becomes imperative to build reliable models for predicting the volatility of exchange rates of home currency. Hence, this study aims to model the Nigerian exchange rate volatility using the Markov regime-switching model. The study analyses the Nigerian exchange rate returns in two and three distinct regimes by employing the Markov regime-switching autoregressive (MS-AR) model with data from 2nd January 2018 to 7th September 2020. Four MS-AR candidate models were estimated for the exchange rate series. Based on the least AIC value, MS(3)-AR(2) was returned as the most parsimonious model among the four candidate models. The MS(3)-AR(2) analysis established a high probability that the returns system remains in the liquidation and awareness states. It implied that only unconventional or severe events could switch the series from regime 2 (liquidation phase) and regime 3 (awareness). While there is a low probability that the system will stay in an imbalanced regime implies high switching of regime 1. Furthermore, an average duration period of 2 days, six days and five days were estimated for the imbalance, liquidation and awareness regimes, respectively. Thus, the findings, i.e. imbalance and liquidation regimes’ identification and their average durations, show that the Naira in the foreign exchange market is not favourable for investors to trade. The study recommends that the Nigerian government should direct more efforts towards improving the performance of the Naira in the foreign exchange market to make the market more favourable for investors. Specifically, the CBN should develop new strategies towards tackling the behaviour of the Nigerian exchange rate when in a liquidation state.

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Volatility Model Choice for Sub-Saharan Frontier Equity Markets - A Markov Regime Switching Bayesian Approach

Cited by 7 publications

References 54 publications

Markov Regime-Switching Autoregressive Model of Stock Market Returns in Nigeria

Markov Regime-Switching Autoregressive Model of Stock Market Returns in Nigeria

Forecasting of Volatility in Stock Exchange Markets by MS-Garch Approach: An Application of Borsa Istanbul

The Regime Examination of Nigeria Exchange Rate Volatility: Evidence from Markov Regime Switching Autoregressive Approach

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