Financial End Economic Time Series

This study examines the determinants of manufacturing sector growth in Nigeria within the time frame of 1981-2016. The main objective of this study is to identify the determinants of the manufacturing sector growth in Nigeria. A model was constructed to incorporate manufacturing output (MO) as the dependent variable, federal government allocation on power, inflation rate, trade openness, gross capital formation and primary school enrolment as the independent variables and tested using the ordinary least-square (OLS) techniques. The empirical result shows that federal government allocation on power, inflation rate and primary school enrolment have negative relationship on the manufacturing output whereas trade openness and gross capital formation have positive relationship on manufacturing output in Nigeria. From the result, federal government allocation on power, trade openness and gross capital formation are significant variables to determine manufacturing output in Nigeria whereas inflation rate and primary school enrolment are not significant variables to determine manufacturing output in Nigeria. Based on the result, the researcher recommends that Nigeria should based on reckoning diversify the income stream of the economy (rebasing the economy) so as to lower the production cost which will based on multiplier effect decrease the selling price of the domestic product.

Keywords: Domestic product, Manufacturing output, Inflation trade, Trade Openness,

We backtest 59 instruments and investigate the predictability of daily returns using
Bayesian variable selection methods. Through these models we show the importance
of variable selection and reduction of overtting. We also visualize how the driving
factors of daily returns from dierent classes vary over time. Predicting daily returns'
magnitude is again conrmed to be a hard task but we show that for some instruments
it is possible to achieve above average hit-rates that could lead to protable strategies.
Simulation results show that predictability levels of daily returns also vary over time.

On the job training has always been viewed as an avenue to improve the production and productivity of companies. In the dynamic world that we live in on the job training and skill upgradation has become very important for all the companies to stay relevant in their domain. This holds true independent of the size of companies in question. It is more so needed in India, wherein what academia offers to our graduates is not in line with Industrial requirements. In our study we have analyzed the impact of on-the-job training and skill upgradation on company's ability to overcome the demand shock caused by pandemic. To understand the same, we have used GARCH model to analyze the rate at which shock caused by COVID-19 subsided among large, mid, and small cap companies. Through our empirical analysis we were able to conclude that, large cap companies who were spending substantial amount on skill upgradation of their employees were able to revert to normalcy at a higher rate as compared to mid and small cap companies.

Archetypal analysis approximates data by means of mixtures of actual extreme cases (archetypoids) or archetypes, which are a convex combination of cases in the data set. Archetypes lie on the boundary of the convex hull. This makes the analysis very sensitive to outliers. A robust methodology by means of M-estimators for classical multivariate and functional data is proposed. This unsupervised methodology allows complex data to be understood even by non-experts. The performance of the new procedure is assessed in a simulation study, where a comparison with a previous methodology for the multivariate case is also carried out, and our proposal obtains favorable results. Finally, robust bivariate functional archetypoid analysis is applied to a set of companies in the S&P 500 described by two time series of stock quotes. A new graphic representation is also proposed to visualize the results. The analysis shows how the information can be easily interpreted and how even non-experts can gain a qualitative understanding of the data.

In this article we describe a new approach for detecting changes in rapidly evolving large-scale graphs. The key notion involved is local alertness: nodes monitor change within their neighborhoods at each time step. Here we propose a financial local alertness application for cointegrated stock pairs

This study investigates the existence of chaos on the Johannesburg Stock Exchange (JSE) and studies three indices namely the FTSE/JSE All Share, FTSE/JSE Top 40 and FTSE/JSE Small Cap. Building upon the Fractal Market Hypothesis to provide evidence on the behavior of returns time series of the above mentioned indices, the BDS test is applied to test for non-random chaotic dynamics and further applies the rescaled range analysis to ascertain randomness, persistence or mean reversion on the JSE. The BDS test shows that all the indices examined in this study do not exhibit randomness. The FTSE/JSE All Share Index and the FTSE/JSE Top 40 exhibit slight reversion to the mean whereas the FTSE/JSE Small Cap exhibits significant persistence and appears to be less risky relative to the FTSE/JSE All Share and FTSE/JSE Top 40contrary to the assertion that small cap indices are riskier than large cap indices.

In this article we describe a new approach for detecting changes in rapidly evolving large-scale graphs. The key notion involved is local alertness: nodes monitor change within their neighborhoods at each time step. Here we propose a financial local alertness application for cointegrated stock pairs

This study investigates the existence of chaos on the Johannesburg Stock Exchange (JSE) and studies three indices namely the FTSE/JSE All Share, FTSE/JSE Top 40 and FTSE/JSE Small Cap. Building upon the Fractal Market Hypothesis to provide evidence on the behavior of returns time series of the above mentioned indices, the BDS test is applied to test for non-random chaotic dynamics and further applies the rescaled range analysis to ascertain randomness, persistence or mean reversion on the JSE. The BDS test shows that all the indices examined in this study do not exhibit randomness. The FTSE/JSE All Share Index and the FTSE/JSE Top 40 exhibit slight reversion to the mean whereas the FTSE/JSE Small Cap exhibits significant persistence and appears to be less risky relative to the FTSE/JSE All Share and FTSE/JSE Top 40contrary to the assertion that small cap indices are riskier than large cap indices.

This paper describe the empirical study based on financial time series modelling with special application to modelling inflation data for Kenya. Specifically the theory of time series is modelled and applied to the inflation data spanning from January 1985 to April 2016 obtained from the Kenya National Bureau of Statistics. Three Autoregressive Conditional Heteroscedastic (ARCH) family type models (traditional ARCH, Generalized ARCH (GARCH), GJR GARCH and the Exponential GARCH (EGARCH)) models were fitted and forecast to the data. This was principally because the data were characterized by changing mean and variance. The outcome of the study revealed that the ARCH –family type models, particularly, the EGARCH (1, 1) with generalized error distribution (GED) was the best in modelling and forecasting Kenya's monthly rates of inflation. The study recommends that governments, policy makers interested in modelling and forecasting monthly rates of inflation should take into consideration Heteroscedastic models since it captures the volatilities in the monthly rates of inflation. 1. Introduction The performance of every financial sector in an economy is affected by a foreseeable rise in the inflation rate which interferes with the process of allocating and distributing resources effectively. The stability of the price and unwavering inflation is a major objectives of every government because it is an significant financial indicator that the economists, governments, political leaders, shareholders, stakeholders and investors use as their foundation of argument when discussing on the state of the economy. According to Webster (2000), a continuous increase of the prices of commodities in an economy is referred to us as inflation. Whenever an economy experiences an increase in the rate of inflation the purchasing power of the local currency decreases and therefore an individual is not able to buy as much commodities compared to when the inflation rate is stable. Financial analysts conclude therefore that there exist direct and indirect aftershocks of inflation in every sector of the economy ranging from inflation rates, investment, unemployment, interest rates, and stock markets among others. An increase in the inflation rate has been found in most of the recent studies to affect financial institutions and investments leading to negative effects .Rising inflation has resulted into a major economic challenges facing most countries in the world especially developing countries like Kenya. For the success of every economic policy inflation rate should be considered as a major focus for growth and development. A research study performed by Ngailo and Massawe (2014) on the modelling inflation rate in Tanzania using ARCH family models. The data taken was from 1997 to 2010. The goodness of fit was assessed through the Akaike information criteria (AIC), Bayesian information criteria (BIC) and standard error (SE) Based on minimum AIC and BIC values, the best fit GARCH models tend to be GARCH (1; 1) and GARCH (1; 2) After estimation of the parameters of selected models, a series of diagnostic and forecast accuracy test were performed. GARCH (1,1) model was found to be the best fitting model and was used for forecasting. it was concluded that the actual series were close to the forecasted series. Numerous evaluation criteria were established to measure the performance of these models. One criterion that is popularly used is by estimating the maximum likelihood of the models and observing which of them has the highest log-likelihood value (Shephard, 1996). In situations when the models do not have the same number of parameters, the principle of parsimony is applied and a suitable model selection criterion such as the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and the Hannan–Quinn (HQ) is used to choose the best model. Another model criterion is to submit estimated models to misspecification tests and observe each model perform under each scenario. The estimated models were checked to verify if it adequately represents the series. Diagnostic checks were performed on the residuals to see the validity of the distribution assumption. The (Q-Q) plot, skewness and kurtosis of the residuals were used to check for the validity of the distribution assumption. R programming software version 3.3.1 was used for data analysis.