During the past decades, seasonal autoregressive integrated moving average (SARIMA) had become one of a prevalent linear models in time series and forecasting. Empirical research advocated that forecasting with non-linear models can be an encouraging alternative to traditional linear models. Linear models are often compared to non-linear models with mixed conclusions in terms of superiority in forecasting performance. Therefore, the aim of this study is to build an early warning system (EWS) model for extreme daily losses for financial stock markets. A logistic model tree (LMT) is used in collaboration with a seasonal autoregressive integrated moving average-Markov-Switching exponential generalised autoregressive conditional heteroscedasticity-generalised extreme value distribution (SARIMA-MS-EGARCH-GEVD) estimates. A time series of the study is a five-day financial time series exchange/Johannesburg stock exchange-all share index (FTSE/JSE-ALSI) for the period of 4 January 2010 to 31 July 2020. The study is set into a two-stage framework. Firstly, SARIMA model is fitted to stock returns in order to obtain independently and identically distributed (i.i.d) residuals and fit the MS(k)-EGARCH(p,q)-GEVD to i.i.d residuals; while, in the second stage, we set-up an EWS model. The results of the estimated MS(2)-EGARCH(1,1) -GEVD revealed that the conditional distribution of returns is highly volatile giving the expected duration to approximately 36 months and 4 days in regime one and 58 months and 2 days in regime two. We further found that any degree losses above 25% implies that there will be no further losses. Using the seven statistical loss functions, the estimated

S A R I M A (2, 1, 0) \times {(2, 1, 0)}_{240} - M S (2) - E G A R C H (1, 1) - G E V D

proved to be the most appropriate model for predicting extreme regimes losses as it was ranked at 71%. Finally, the results of EWS model exhibit reasonably an overall performance of 98%, sensitivity of 79.89% and specificity of 98.40% respectively. The model further indicated a success classification rate of 89% and a prediction rate of 95%. This is a promising technique for EWS. The findings also confirmed 63% and 51% of extreme losses for both training sample and validation sample to be correctly classified. The findings of this study are useful for decision makers and financial sector for future use and planning. Furthermore, a base for future researchers for conducting studies on emerging markets, have been contributed. These results are also important to risk managers and and investors.

International Journal of Financial Studies, Volume 9, Issue 2

Articles (16)

Relativistic Option Pricing

Innovation and Competitive Intelligence in Business. A Bibliometric Analysis

Modeling System Risk in the South African Insurance Sector: A Dynamic Mixture Copula Approach

Assessing Market Risk in BRICS and Oil Markets: An Application of Markov Switching and Vine Copula

#Bitcoin, #COVID-19: Twitter-Based Uncertainty and Bitcoin Before and during the Pandemic

Accounting Comparability and Cash Holdings in Vietnam

The Interactions between COVID-19 Cases in the USA, the VIX Index and Major Stock Markets

Improving Audit Reports: A Consensus between Auditors and Users

Does Trading Volume Drive Systemic Banks’ Stock Return Volatility? Lessons from the Greek Banking System

Common Factors in the Term Structure of Credit Spreads and Predicting the Macroeconomy in Japan

Short-Selling and Financial Performance of SMEs in China: The Mediating Role of CSR Performance

Revisiting Banking Stability Using a New Panel Cointegration Test

Exploring Investment Behavior of Women Entrepreneur: Some Future Directions

Earnings Management of Insolvent Firms and the Prediction of Corporate Defaults via Discretionary Accruals

Tick Size and Price Reversal after Order Imbalance

Predicting Extreme Daily Regime Shifts in Financial Time Series Exchange/Johannesburg Stock Exchange—All Share Index