Original Research
A Hidden Markov Model inference approach to testing the Random Walk Hypothesis: Empirical evidence from the Nigerian Stock Market
Journal of Economic and Financial Sciences | Vol 9, No 3 | a66 |
DOI: https://doi.org/10.4102/jef.v9i3.66
| © 2016 Edesiri Nkemnole
| This work is licensed under CC Attribution 4.0
Submitted: 18 December 2017 | Published: 03 December 2016
Submitted: 18 December 2017 | Published: 03 December 2016
About the author(s)
Edesiri Nkemnole, Department of Mathematics, University of Lagos, NigeriaFull Text:
PDF (403KB)Abstract
The movement of stock prices, in capital markets across the world, has been found to be both random and non-random. Basically, for a stock price to follow a random walk, its future price changes randomly based on all currently available information in the stock market, its price history inclusive. Some research findings have shown that the existing traditional unit root tests have low statistical power and hence cannot capture gradual changes over successive observations. Consequently, there is a need to revisit the random walk theory in stock prices using other tests. This study employs a Hidden Markov Model (HMM) with time-varying parameters to assess whether the stock price movements of the Nigerian Stock Exchange (NSE) follow a random walk process, or otherwise. Via hidden states, the HMM allows for periods with different volatility levels characterised by the hidden states. By simply accounting for the non-constant variance of the data with a two-state Hidden Markov Model and taking estimation into account via the Sequential Monte Carlo Expectation Maximisation (SMCEM) technique, this study finds no support of randomness. In conclusion, the stock price movements of the NSE do not follow the random walk process.
Keywords
Hidden Markov Model; random walk theory; stochastic volatility; stock exchange
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