A pinboard by

Research Assistant, University of Ibadan


To inform economist and financial analyst in choice of model in making decision in risk portfolio.

GARCH models have been developed to account for empirical regularities in financial data. Many financial time series have a number of characteristics in common; asset prices are generally non stationary while returns are usually stationary, some financial time series are fractionally integrated, return series usually show no or little autocorrelation, serial independence between the squared values of the series is often rejected pointing towards the existence of non-linear relationships between subsequent observations, volatility of the return series appears to be clustered, normality has to be rejected in favor of some thick-tailed distribution, some series exhibit so-called leverage effect that is changes in stock prices tend to be negatively correlated with changes in volatility.

An empirical analysis of the mean return and conditional variance of Nigeria Stock Exchange (NSE) index is performed using various error innovations in GARCH models. Conventional GARCH model which assumed normal error term failed to capture volatility clustering, leptokurtosis and leverage effect as a result of zero skewness and kurtosis respectively. We re-modify error distributions of GARCH (p,q) model inference using some thick-tailed distributions. Method of Quasi – Maximum Likelihood Estimation (MLE) was used in parameter estimation,m our result shows that GARCH(1,1) and APARCH(1,1) models with anomalous densities improves overall estimation for measuring conditional variance. The robust model that explained the NSE index is determined by loglikelihood and model selection Criteria. The prediction performance of these conditional changing variance models is compared using Root Mean Square Error(RMSE) and Mean Absolute Percentage Error (MAPE). Generalized Length Biased Scaled-t Innovation using APARCH(1,1) model is the most robust for forecasting Nigeria Stock Exchange Index.


A Framework for Inferring Causality from Multi-Relational Observational Data using Conditional Independence

Abstract: The study of causality or causal inference - how much a given treatment causally affects a given outcome in a population - goes way beyond correlation or association analysis of variables, and is critical in making sound data driven decisions and policies in a multitude of applications. The gold standard in causal inference is performing "controlled experiments", which often is not possible due to logistical or ethical reasons. As an alternative, inferring causality on "observational data" based on the "Neyman-Rubin potential outcome model" has been extensively used in statistics, economics, and social sciences over several decades. In this paper, we present a formal framework for sound causal analysis on observational datasets that are given as multiple relations and where the population under study is obtained by joining these base relations. We study a crucial condition for inferring causality from observational data, called the "strong ignorability assumption" (the treatment and outcome variables should be independent in the joined relation given the observed covariates), using known conditional independences that hold in the base relations. We also discuss how the structure of the conditional independences in base relations given as graphical models help infer new conditional independences in the joined relation. The proposed framework combines concepts from databases, statistics, and graphical models, and aims to initiate new research directions spanning these fields to facilitate powerful data-driven decisions in today's big data world.

Pub.: 08 Aug '17, Pinned: 20 Aug '17

Using phenomenological models for forecasting the 2015 Ebola challenge.

Abstract: The rising number of novel pathogens threatening the human population has motivated the application of mathematical modeling for forecasting the trajectory and size of epidemics.We summarize the real-time forecasting results of the logistic equation during the 2015 Ebola challenge focused on predicting synthetic data derived from a detailed individual-based model of Ebola transmission dynamics and control. We also carry out a post-challenge comparison of two simple phenomenological models. In particular, we systematically compare the logistic growth model and a recently introduced generalized Richards model (GRM) that captures a range of early epidemic growth profiles ranging from sub-exponential to exponential growth. Specifically, we assess the performance of each model for estimating the reproduction number, generate short-term forecasts of the epidemic trajectory, and predict the final epidemic size.During the challenge the logistic equation consistently underestimated the final epidemic size, peak timing and the number of cases at peak timing with an average mean absolute percentage error (MAPE) of 0.49, 0.36 and 0.40, respectively. Post-challenge, the GRM which has the flexibility to reproduce a range of epidemic growth profiles ranging from early sub-exponential to exponential growth dynamics outperformed the logistic growth model in ascertaining the final epidemic size as more incidence data was made available, while the logistic model underestimated the final epidemic even with an increasing amount of data of the evolving epidemic. Incidence forecasts provided by the generalized Richards model performed better across all scenarios and time points than the logistic growth model with mean RMS decreasing from 78.00 (logistic) to 60.80 (GRM). Both models provided reasonable predictions of the effective reproduction number, but the GRM slightly outperformed the logistic growth model with a MAPE of 0.08 compared to 0.10, averaged across all scenarios and time points.Our findings further support the consideration of transmission models that incorporate flexible early epidemic growth profiles in the forecasting toolkit. Such models are particularly useful for quickly evaluating a developing infectious disease outbreak using only case incidence time series of the early phase of an infectious disease outbreak.

Pub.: 04 Dec '16, Pinned: 20 Aug '17