Comparison of Stationarity on Ljung Box Test Statistics for Forecasting

Jayeola Dare,Aye O Patrick,David O Oyewola

doi:10.34198/ejms.8222.325336

Jayeola Dare, Aye O Patrick + Show 1 more

Open Access

https://doi.org/10.34198/ejms.8222.325336

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Abstract

The movements in Asset prices are very complex, therefore seem to be unpredictable. However, one of the main challenges of the econometric models is to get the best data for forecasting in order to present accurate results. This paper investigates the performance of stationary and non-stationary data on Ljung Box test statistics, to check the fitness of the data for forecasting. In the paper three assets (Groundnut, sorghum and soya bean) are used, tests are conducted for Ljung box statistics; Correlogram, Histogram Normality and Heteroscedasticity test. It is observed that stationary data are better for forecasting than non-stationary data in this research.

Highlights

Forecasting is fundamental to the risk management process in order to price assets derivatives, hedging strategies and estimating the financial risk of a firm in portfolio
The approach used for this research is based on quantitative approach and this is because it involves the collection and analysis of numerical data
The data used for this research are groundnut, sorghum and soya bean

Summary

Introduction

Forecasting is fundamental to the risk management process in order to price assets derivatives, hedging strategies and estimating the financial risk of a firm in portfolio. Autoregressive Conditional Heteroscedasticity (ARCH) type models have become popular as a means of capturing observed characteristics of financial returns like thick tails and volatility clustering. These models use time series data on returns to model conditional variance. An alternative way to estimate future volatility is to use options prices, which reflect the market expectations of volatility. Analytical option pricing models can be used to back out implied volatility over the remaining life of the option. Keywords and phrases: ARCH, asset, forecast, stationary.

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