Volatility model estimations of palm oil price returns via long-memory, asymmetric and heavy-tailed GARCH parameterization

This study attempts to model the volatility of palm oil price returns via a number of Generalized Autoregressive Conditional Heteroskedasticity class of models that capture the long-range memory, asymmetry, and heavy-tailedness phenomena. These models have been estimated in the presence of four alternative conditional distributions: Gaussian, Student t, generalized error distribution, and skewed Student t. The empirical results indicate that complex model specifications and distribution assumptions do not seem to outperform the simpler ones in terms of standard model selection criteria and numerical convergence. With regard to the conditional distributions, a symmetric fat-tailed distribution has been found to be preferred to Gaussian and asymmetric distribution in many cases

First-order fractionally integrated non-separable spatial autoregressive (FINSSAR(1,1)) model and some of its properties

Spatial modelling has its applications in many fields. There exist in the literature a class of models known as the fractionally integrated separable spatial autoregressive (FISSAR) model. In this paper the objective of our research is to develop a non-separable counterpart of the FISSAR model. We term this model as the fractionally integrated non-separable spatial autoregressive (FINSSAR) model. The FINSSAR model is a more general model as it encompasses the FISSAR and the standard separable autoregressive (SSAR) models. The theoretical autocovariance function and the spectral function of the model are obtained and some numerical results is presented. This model may be able to model many type of real phenomen

Approximate asymptotic variance-covariance matrix for the whittle estimators of GAR(1) parameters

Generalized Autoregressive (GAR) processes have been considered to model some features in time series. The Whittle's estimates have been investigated for the GAR(1) process by a simulation study by Shitan and Peiris (2008). This article derives approximate theoretical expressions for the enteries of the asymptotic variance-covariance matrix for those estimates of GAR(1) parameters. These results are supported by a simulation study

Modelling polio data using the first order non-negative integer-valued autoregressive INAR(1) model.

Time series data may consists of counts, such as the number of road accidents, the number of patients in a certain hospital, the number of customers waiting for service at a certain time and etc. When the value of the observations are large it is usual to use Gaussian Autoregressive Moving Average (ARMA) process to model the time series. However if the observed counts are small, it is not appropriate to use ARMA process to model the observed phenomenon. In such cases we need to model the time series data by using Non-Negative Integer valued Autoregressive (INAR) process. The modeling of counts data is based on the binomial thinning operator. In this paper we illustrate the modeling of counts data using the monthly number of Poliomyelitis data in United States between January 1970 until December 1983. We applied the AR(1), Poisson regression model and INAR(1) model and the suitability of these models were assessed by using the Index of Agreement(I.A.). We found that INAR(1) model is more appropriate in the sense it had a better I.A. and it is natural since the data are counts

Time series properties of the class of generalized first-order autoregressive processes with moving average errors

A new class of time series models known as Generalized Autoregressive of order one with first-order moving average errors has been introduced in order to reveal some hidden features of certain time series data. The variance and autocovariance of the process is derived in order to study the behaviour of the process. It is shown that in special cases these new results reduce to the standard ARMA results. Estimation of parameters based on the Whittle procedure is discussed. We illustrate the use of this class of model by using two examples

Autocovariance function of the Fractionally Integrated Separable Spatial ARMA (FISSARMA) models

Spatial modeling is important in many fields and there are various kinds of spatial models. One of such models is known as the fractionally integrated separable spatial ARMA (FISSARMA) model. In the area of time series analysis, Sowell (19927. Sowell, F. (1992). Maximum likelihood estimation of stationary univariate fractionally integrated time series models. J. Econ. 53:165–188.[CrossRef], [Web of Science ®]View all references) has established the autocovariance function of the long-memory models using hypergeometric function. In this paper we will extend Sowell’s work for FISSARMA models

Development of a web portal to forecast the monthly mean chlorophyll concentration of the waters off Peninsular Malaysia's west coast

The principal photosynthetic pigment of chlorophyll (chl-a) in the water is produced by phytoplankton and its concentration (CC) is the biomass and abundance indicator of phytoplankton. The small pelagic fishes which feed on phytoplankton is one of the dominant groups caught in Malaysia. The typical challenge in fisheries is to identify a fruitful fishing ground at any given time. Based on the relationship amongst CC, phytoplankton and food chain of pelagic fishes, it can be suggested that waters with high levels of CC may indicate a more favourable fishing ground. Thus, using CC may help to narrow down the fishing ground search and would be useful to fishermen. This paper described the development of a web portal to forecast the monthly mean CC of the waters off Peninsular Malaysia’s west coast. The portal’s functionality was illustrated with time series modelling and forecasting of 3 fisheries sites using Holt-Winters seasonal additive model. The portal consists of forecast outputs as well as related information and is primarily intended for use as a tool for fishermen with the advantage of easy accessibility

Some properties of the normalized periodogram of a fractionally integrated separable spatial ARMA (FISSARMA) model

In this article, we study the properties of the normalized periodogram of the Fractionally Integrated Separable Spatial ARMA (FISSARMA) models. In particular, we establish the asymptotic mean of the normalised periodogram and the asymptotic second-order moments of the normalised Fourier coefficients. We also establish the asymptotic distribution of the normalised periodogram. Some numerical results are also provided

Estimation of the memory parameters of the fractionally integrated separable spatial autoregressive (FISSAR(1,1)) model : a simulation study.

In this article, we implement the regression method for estimating (d1,d2) of the FISSAR(1,1) model. It is also possible to estimate d1 and d2 by Whittle’s method. We also compute the estimated bias, standard error, and root mean square error by a simulation study. A comparison was made between the regression method of estimating d1 and d2 to that of the Whittle’s method. It was found in this simulation study that the regression method of estimation was better when compare with the Whittle’s estimator, in the sense that it had smaller root mean square errors (RMSE) values

Cluster analysis of top 200 universities in Mathematics

University rankings are becoming a vital performance assessment for higher learning institutions worldwide. Besides the overall rankings, the universities are also ranked by subjects serving as comprehensive guide to discover the specialist strengths of universities worldwide by highlighting top 200 universities for a range of 30 individual popular subjects. Data for this ranking purpose consist four variables namely the academic reputation, employer reputation, citation per paper and H-index citations. In this ranking, universities are ranked according to their overall score calculated from linear combination of the aforementioned variables and their respective weightings. As the existing ranking technique based on overall score appears to be simple and since the rankings data are of multivariate nature, therefore it is possible to use multivariate statistical technique like cluster analysis. Agglomerative hierarchical cluster analysis of top 200 QS ranked universities by Mathematics subject area 2014 has been performed to obtain natural clustering of the universities in an objective manner. The agreement between cluster analysis and existing QS rankings is verified and it is suggested that the distance between universities can be used as an alternative measure to rank universities. Cluster analysis applied on the same variables would serve as an alternative way to rank universities and to look at the rankings in a different perspective