Tasiu Musa
Ahmadu Bello University, Zaria, Nigeria, Statistics, Faculty Member
- My name is Tasi’u Musa, a Nigerian of 33-years old. I have a National Diploma in Statistics, first degree in Statistics (B. Sc. Statistics), Masters Degree in Statistics (M. Sc. Statistics) and now going my PhD in Statistics with the dep... moreMy name is Tasi’u Musa, a Nigerian of 33-years old. I have a National Diploma in Statistics, first degree in Statistics (B. Sc. Statistics), Masters Degree in Statistics (M. Sc. Statistics) and now going my PhD in Statistics with the department of Statistics, Ahmadu Bello University Zaria, Nigeria. My areas of research interests are financial Time Series and Multivariate Analysis.edit
Research Interests:
Research Interests:
ABSTRACTThis articles introduces a new lifetime model called the generalized transmuted Kumaraswamy distribution which extends the Kumaraswamy distribution from the family proposed by Nofal et al., (2017). We provide hazard and survival... more
ABSTRACTThis articles introduces a new lifetime model called the generalized transmuted Kumaraswamy distribution which extends the Kumaraswamy distribution from the family proposed by Nofal et al., (2017). We provide hazard and survival functions of the proposed distribution. The statistical properties of the proposed model are provided and the method of Maximum Likelihood Estimation (MLE) was proposed in estimating its parameters.
Research Interests:
ABSTRACTThis articles introduces a new lifetime model called the generalized transmuted Kumaraswamy distribution which extends the Kumaraswamy distribution from the family proposed by Nofal et al., (2017). We provide hazard and survival... more
ABSTRACTThis articles introduces a new lifetime model called the generalized transmuted Kumaraswamy distribution which extends the Kumaraswamy distribution from the family proposed by Nofal et al., (2017). We provide hazard and survival functions of the proposed distribution. The statistical properties of the proposed model are provided and the method of Maximum Likelihood Estimation (MLE) was proposed in estimating its parameters.
A volatility model must be able to forecast volatility; this is the central requirement in almost all financial applications. In this paper, we outline some stylized facts about volatility that should be incorporated in a model;... more
A volatility model must be able to forecast volatility; this is the central requirement in almost all financial applications. In this paper, we outline some stylized facts about volatility that should be incorporated in a model; pronounced persistence, mean reversion and asymmetry such that the sign of an innovation also affects volatility. We use daily data on the Naira per Dollar exchange rate to illustrate these stylized facts, and the ability of GARCH-type models to capture these features. The conditional volatility of the exchange rate return was found to be quite persistent, yet test for non-stationarity indicated that it is mean reverting. A negative lagged return innovation was found to have an impact on conditional variance equal as that of a positive return innovation, indicating the absence of leverage effect. Finally, measuring the fit of the models namely, GARCH (1,1) and EGARCH (1,1), the statistically preferred model is the EGARCH (1,1) model.