A Comparative Study Of Stock Price Forecasting Using Nonlinear Models

Xaba, Diteboho; Moroke, Ntebogang Dinah; Arkaah, Johnson; Pooe, Charlemagne

doi:10.22495/rgcv7i2art1

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…This nature of studies is very scarce in literature as far as South Africa is concerned. Other researchers such as Xaba et al (2015) and Alvarez et al (2018) employ the regime switching models but the focus was not on predicting the regime shifts in the JSE-ALSI. For more reading about nonlinear and regime-shifts, see Hamilton, (2010); Chkili and Nguyen (2014); Ardia et al (2018); and Wolf et al (2019).…”

Section: Introductionmentioning

confidence: 99%

Predicting Regime Shifts in Johannesburg Stock Exchange Allshare Index (Jse-Alsi): A Markov-Switching Approach

Makatjane¹,

Molefe²

2020

EJEF

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It has been proven several times that linear models are unable to encapsulate nonlinear dynamics of macroeconomic and financial data such as inflation rates, exchange rates and stock prices to mention fewer. As a result, to overcome this problem, this current study adopted the nonlinear models due to the fact that they have required qualities to apprehend nonlinearity in a dataset. In order to predict a regime shifts, a five-day Johannesburg stock exchange allshare index (JSE-ALSI) spanning period from 02 January 2003 to 28 June 2019 was used as an experimental unit. This current study firstly employed Teräsvirta neural network test to detect the presence of nonlinearity and proceeded to estimate a two regime Markov-Switching autoregressive (MS-AR). The results of Teräsvirta neural network test revealed a highly significant nonlinearity with permanent seasonality as demonstrated by Kruskal-Wallis test. The predicted regime shifts by a latent dynamic allowed the autoregressive and variance parameters to promptly react to vital systemic shocks. As a result, this current study allowed volatility to oscillate between high and low volatility regimes that produced an expected duration of high volatility of two year and two months. This was a clear indication that there is a regime shifts in JSE-ALI which are modeled using Markov-Chain (MC) stochastic process. These findings may be used to inform robust policy making with the aim of safeguarding both the JSE and other global stock markets from the episodes of stock market crash. Moreover, other researchers can utilize the results of this study to calculate the risk associated with structural breaks and high volatility periods.

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Section: Introductionmentioning

confidence: 99%

Predicting Regime Shifts in Johannesburg Stock Exchange Allshare Index (Jse-Alsi): A Markov-Switching Approach

Makatjane¹,

Molefe²

2020

EJEF

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“…With these results, the null hypothesis is rejected, and we conclude that the behaviour of the exchange rates in SA is better explained by a proposed supervised ML model (akin to the Markov-switching generalized autoregressive conditional heteroscedasticity model). Previous empirical studies, including Xaba et al (2017) and Chkili and Nguyen (2014), found parallel results when utilizing the likelihood ratio test. From a theoretical perspective, this behaviour is normal and can be clarified by the changing economic structure in the exchange rate of SA, attributable to structural economic reform policies (financial liberalization, tax system adjustments, competition policy), as well as to progressive economical and financial crises at both local and worldwide levels.…”

Section: Regime Shifts In the Real Exchange Ratementioning

confidence: 77%

“…According to Tsay (2014), the final optimal lag length should be selected by the SBC. Xaba et al (2017) acknowledged that with a large sample size, say ≥ 30, both the SBC and Hannan-Quinn perform much better in an optimal lag length selection. With this evidence, in this study the selected optimal lag length is 1 (Table 4).…”

Section: Markov-switching Garch Frameworkmentioning

confidence: 99%

Identifying structural changes in the exchange rates of South Africa as a regime-switching process

Makatjane¹,

Wyk²

2020

WIDER Working Paper

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Exchange rate volatility is said to exemplify the economic health of a country. Exchange rate break points (known as structural breaks) have a momentous impact on the macroeconomy of a country. Nonetheless, this country study makes use of both unsupervised and supervised machine learning algorithms to classify structural changes as regime shifts in real exchange rates in South Africa. Weekly data for the period January 2003–June 2020 are used. To these data we apply both non-linear principal component analysis and Markov-switching generalized autoregressive conditional heteroscedasticity. The former approach is used to reduce the dimensionality of the data using an orthogonal linear transformation by preserving the statistical variance of the data, with the proviso that a new trait is non-linearly independent, and it identifies the number of regime switches that are to be used in the Markov-switching model. The latter is used to partition the variance in each regime by allowing an estimation of multiple break transitions. The transition breakpoints estimates derived from this machine learning approach produce results that are comparable to other methods on similar system sizes. Application of these methods shows that the machine learning approach can also be employed to identify structural changes as a regime-switching process. During times of financial crisis, the growing concern over exchange rate volatility, including its adverse effects on employment and growth, broadens the debates on exchange rate policies. Our results should help the South African monetary policy committee to anticipate when exchange rates will pick up and be prepared for the effects of periods of high exchange rates.

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“…According to Timmermann (2000) the probability law governing the parameters fully follows the Gaussian innovation , the coefficient of an autoregressive , the two intercepts and finally the twostate transition probabilities that follow homogenous Markov Chain (MC). For further readings on MSM, consult Yang (2000); Moolman (2005); Xaba et al (2016) and Xaba et al (2017).…”

Section: Literature Reviewmentioning

confidence: 99%

On the Prediction of the Inflation Crises of South Africa Using Markov-Switching Bayesian Vector Autoregressive and Logistic Regression Models

Makatjane

Moroke

Xaba

2018

Journal of Social Economics Research

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Article HistoryThe aim of this study is to build an early warning system (EWS) model for inflation rates of South Africa (SA). A logistic regression model (LRM) is used in collaboration with a Markov-switching Bayesian vector autoregressive (MS-BVAR) to produce the estimates. Monte Carlo experimental methods are used to simulate both the inflation rate and repo rate of the SA economy. The procedure simulated 228 observations for the period of January 1999 to December 2017. Preliminary results confirmed the applicability of both models for further analyses. MS (2)-BVAR(1) proved to be the most appropriate model for detecting regime shifts in inflation rates. The results indicate that SA inflation might be in a low inflation regime for the period of 11 years and 4 months. Surprisingly, we discovered that the repo rate is not a good tool to combat the inflation rate in SA i.e a 1% increase in the repo in a month significantly increased inflation rate by about 81%. The findings also confirmed 51% and 53% of insample and out-of-sample SA inflation crises forecasts to be correctly classified. This is in accordance with the reported results of Cruz and Mapa (2013) and Makatjane and Xaba (2016). The verdicts of the study are relevant for policy purposes and literature.Contribution/Originality: This study uses new estimation methodology to quantify the likelihood of future inflation crises in South Africa.

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A Comparative Study Of Stock Price Forecasting Using Nonlinear Models

Cited by 4 publications

References 12 publications

Predicting Regime Shifts in Johannesburg Stock Exchange Allshare Index (Jse-Alsi): A Markov-Switching Approach

Predicting Regime Shifts in Johannesburg Stock Exchange Allshare Index (Jse-Alsi): A Markov-Switching Approach

Identifying structural changes in the exchange rates of South Africa as a regime-switching process

On the Prediction of the Inflation Crises of South Africa Using Markov-Switching Bayesian Vector Autoregressive and Logistic Regression Models

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