Purpose: The research investigated variation of cattle prices in Nigeria. Specifically, the research: determined presence volatility prices, degree and estimated level persistence prices.
 Methodology: Multi-stage simple random (balotting) sampling techniques were used to select two states each from five out six geo-political zones Nigeria, except South-East zone which was not represented ...

The volatility dynamics of foreign exchanges have been the focus of research since Bollerslev’s (1986) seminal work on the generalized autoregressive conditional heteroscedasticity (GARCH) modelling. Several well-established empirical regularities may be highlighted as follows: [a] evidence of volatility clustering is detected in the exchange rates returns; [b] asymmetric effects in exchange ra...

In this article, we investigated the volatility of Chinese open-end funds market by using Zhongxin open-end funds index. According to the characteristics of different GARCH models, we empirically studied GARCH, EGARCH and GARCH_M model. The result indicated that GARCH (1, 1) model and GARCH_M (1, 1) model could better fit the characteristics of the index return rate. At the same time, the resul...

Forecasting stock exchange rates is an important financial problem that is receiving increasing attention. During the last few years, a number of neural network models and hybrid models have been proposed for obtaining accurate prediction results, in an attempt to outperform the traditional linear and nonlinear approaches. This paper evaluates the effectiveness of neural network models; recurre...

In this paper, we introduce supergaussian generalized autoregressive conditional heteroscedasticity (GARCH) models for speech signals in the short-time Fourier transform (STFT) domain. We address the problem of speech enhancement, and show that estimating the variances of the STFT expansion coefficients based on GARCH models yields higher speech quality than by using the decision-directed metho...

Most high-frequency asset returns exhibit seasonal volatility patterns. This article proposes a new class of models featuring periodicity in conditional heteroscedasticity explicitly designed to capture the repetitive seasonal time variation in the second-order moments. This new class of periodic autoregressive conditional heteroscedasticity, or P-ARCH, models is directly related to the class o...

There is vast empirical evidence that for many economic variables conditional variances and covariances change over time. Given the importance of heteroscedasticity in finance and macroeconomics1 it is not surprising that estimation of the time-varying volatility has attracted substantial attention in the literature. As any time-varying parameter, volatility can be modelled both with observatio...

We study portfolio optimization of four major cryptocurrencies. Our time series model is a generalized autoregressive conditional heteroscedasticity (GARCH) with multivariate normal tempered stable (MNTS) distributed residuals used to capture the non-Gaussian cryptocurrency return dynamics. Based on model, we optimize in terms Foster-Hart risk. Those sophisticated techniques are not yet documen...

I investigate how a model that assumes learning might interact with a rational expectations data generating process. Milani (2007b) asserts that if agents are learning and there is no conditional heteroscedasticity then an econometrician may be fooled into estimating ARCH/GARCH models. In addition, I evaluate the contribution of a new endogenous gain, which I have proposed in previous paper, ma...