MODELING ASSETS PRICING USING PURE JUMP INVERSE-GAUSSIAN PROCESS

Abstract

In general, stock price changes are rare occurrences, and a Poisson process can be used to describe those price changes. This study focuses on asset price modeling utilizing just jump Inverse-Gaussian processes. The goals are to analyze the price-movement patterns of the stock and gauge its relative volatility using the Inverse-Gaussian jump model. The Inverse-Gaussian jump model was employed to analyze the data. The outcome from Table 2 shows that there are more jumps than zero in the estimation of the inverse Gaussian process. This implies that the stock has been continuously rising. For Nestle Nigeria Limited (628.15), BCC (131.27), NB (110.6), 7UP (79.944), and Guinness (79.944), the average percentage increase in stock price is exceptionally high. The estimated model demonstrates that jump risk can be diversified. It is significantly different for all variables, according to the computed goodness of fit (Chi Square) test. This demonstrates unequivocally that there are daily increases in stock prices. As a result, we draw the conclusion that stock prices have increased recently. The analysis suggests that investors recognize that Nigerian stocks are producing strong returns regardless of stock market trends. To attract more stockholders, more efforts should be made to boost their production.