Detecting Financial Crisis in Nigerian Capital Markets
Using Markov Switching Generalized Autoregressive
Conditional Heteroscedasticity

Omeruruike Gideon Wobo; Deebom Zorle Dum

doi:https://doi.org/10.56201/ijbfr.v8.no2.2022.pg1.18

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Detecting Financial Crisis in Nigerian Capital Markets Using Markov Switching Generalized Autoregressive Conditional Heteroscedasticity

Omeruruike Gideon Wobo , Deebom Zorle Dum

Abstract

Financial crisis is often a thing of major concern in many countries. It has led so many experts to be interested in identifying the causes and the general statistical pattern associated with them. Based on the causes, pattern and characteristics exhibited by the financial crisis, it is expected that experts should be able to develop a model to detect the financial crisis. The financial crisis in Nigerian stock markets can be observed as a macro-economic indicator. Therefore, the aim of this study is to develop a model using a macro-economic indicator (All share index) that can explain the combined nature of volatility and switching characteristics associated with Nigerian capital markets. The data used for the study is the All Share Index (ASI) of Nigerian stock markets spanned from the month of January, 1985 – January 2021. The series is fitted to conditionally compound monthly return and the result obtained was used in fitting volatility and Markov switching GARCH models. Based on the AIC and SIC, the EGARCH model was found to be the appropriate model for modeling Nigerian stock markets. However, structural changes in the model were tested using the Chow breakpoint test. The results obtained based on the test of structural changes, found that there was a structural change in the real exchange rate in the period between February 1998 and July 2010. For MSGARCH (3,1.1), the results show that the conditional probability of surviving in the high volatility state in the next period is 0.984. Also, the conditional probability of surviving in a medium volatility state in the next period is 0.617 and the conditional probability of surviving in a low volatility state in the next period is 0.410. The results showed that the MSGARCH (3,1,1) model could explain that the period from February 1997 until July 2020 had probabilitvalues of more than 0.6. This reveals high volatility confirming the occurrence of a crisis.

Keywords

VolatilityNigeriacapitalMarketsGARCHTGARCHEGARCHMSGARCH

References

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Detecting Financial Crisis in Nigerian Capital Markets Using Markov Switching Generalized Autoregressive Conditional Heteroscedasticity

Abstract

Keywords

References

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