Modelling and Forecasting volatility in International financial markets

Samuel Tabot Enow

doi:10.20525/ijrbs.v12i2.2338

Authors

Samuel Tabot Enow Research associate, The IIE Vega school, 444 Jan Smuts Ave, Bordeaux, Randburg, 2194, South Africa https://orcid.org/0000-0001-8060-3108

DOI:

https://doi.org/10.20525/ijrbs.v12i2.2338

Keywords:

Volatility, Stock Markets, forecasting, GARCH model, ARCH model

Abstract

Modelling volatility using asset price returns has always been at the forefront of financial economics and option pricing. Observing the conditional variance properties in these asset returns, can be very useful for trend analysis and volatility predictions which are ever needed for trading, portfolio management and financial decision making. The aim of the study was to model and forecast volatility in stock markets. Six financial markets namely the Nasdaq, JSE, the DAX, the CAC 40 and the Nikkei 225 were used as samples with a sampling frame from January 29, 2018 to January 29, 2023. The findings of this study revealed that the variance for all the financial markets under consideration changes significantly with the passage of time. Also, volatility in the JSE, DAX & CAC 40 display fat tail distributions and it is expected to move by three standard deviations. Accordingly, volatility will persist in the Nasdaq, DAX and CAC 40 at an increasing rate but will persist at a decreasing rate in the JSE and Nikkei 225. Considering the peril involved in stock market investing, this study makes a notable contribution to estimating market volatility which is an integral component of asset pricing. With this knowledge, analyst and market traders will have a better understanding of the error distribution in stock markets which will assist in specifying predictive asset prices.

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