The Superiority of the EGARCH-Odd Exponentiated Skew-t Model in Predicting Financial Returns Volatility

Articles in Press

Authors

1 Department of Mathematics and Statistics, Federal University Wukari, Wukari, Nigeria; Department of Mathematical Sciences, Abubakar Tafawa Balewa University, Bauchi, Nigeria.

2 Department of Mathematical Sciences, Abubakar Tafawa Balewa University, Bauchi, Nigeria.

3 Department of Physics, University of Ilorin, Ilorin, Nigeria.

Abstract

The key point for volatility forecast is to utilize appropriate innovation conditional density. The specification of flexible innovation density is very essential given that it directly affects the accuracy of volatility prediction. In this work, a new odd exponentiated skew-t (OEST) innovation density is introduced for exponentiated generalized autoregressive conditional heteroscedasticity (EGARCH) models for modeling daily volatility of financial return series. The simulation via Monte Carlo experiment indicates that the estimators compared are asymptotically unbiased and consistent given that their biases converge to zero as the sample size increases. The maximum likelihood and maximum product of spacing procedures dominate the other procedures. The real dataset application based on the First bank Nigeria shock price index is given to show the performance of EGARCH model specified under OEST innovation density relative to normal, student-t, generalized error, skew normal, skew student-t, skew generalized error, generalized hyperbolic and Johnson reparametrized densities in terms of volatility accuracy. Overall, the empirical results show that EGARCH model with OEST innovation density generates better in-and out-of-samples performance than all the other models.
 

Keywords

Iranian Economic Review

Articles in Press, Accepted Manuscript
Available Online from 17 January 2023

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