Effect of Monetary Policy on the Nigerian Stock Market: A Smooth Transition Autoregressive Approach

This paper examines the nonlinear effect of monetary policy decisions on the performance of the Nigerian Stock Exchange market, by employing the Smooth Transition Autoregressive (STAR) model on monthly data from 2013 M4 to 2019 M12 for All Share Index and monetary policy instrument. This study considers the two regimes characterizing the stock market, which are the lower regime (the bear market) and the upper regime (the bull market). The results show evidence of nonlinear effect of monetary policy on the stock exchange market. Monetary policy rate, money supply, lagged monetary policy rate and lagged treasury bill rate are found to have significant positive effects on the stock exchange market in the lower regime while current treasury bill rate shows a negative effect. In the upper regime, money supply and lagged treasury bill rate have significant negative effect on the stock market. The current treasury bill rate is found to have a positive effect on the stock exchange market. It is recommended that the Central Bank of Nigeria should maintain a stable money supply growth that is consistent with increased activities in the Nigerian stock market.

Policy and Law Assessment of COVID-19 Based on Smooth Transition Autoregressive Model

As of the end of October 2020, the cumulative number of confirmed cases of COVID-19 has exceeded 45 million and the cumulative number of deaths has exceeded 1.1 million all over the world. Faced with the fatal pandemic, countries around the world have taken various prevention and control measures. One of the important issues in epidemic prevention and control is the assessment of the prevention and control effectiveness. Changes in the time series of daily new confirmed cases can reflect the impact of policies in certain regions. In this paper, a smooth transition autoregressive (STAR) model is applied to investigate the intrinsic changes during the epidemic in certain countries and regions. In order to quantitatively evaluate the influence of the epidemic control measures, the sequence is fitted to the STAR model; then, comparisons between the dates of transition points and those of releasing certain policies are applied. Our model well fits the data. Moreover, the nonlinear smooth function within the STAR model reveals that the implementation of prevention and control policies is effective in some regions with different speeds. However, the ineffectiveness is also revealed and the threat of a second wave had already emerged.

Bayesian inference for unit root in smooth transition autoregressive models and its application to OECD countries

This paper proposes a Bayesian unit root test for testing a non-stationary random walk of nonlinear exponential smooth transition autoregressive process. It investigates the performance of Bayes estimators and Bayesian unit root test due to its superiority in estimation and power properties than reported in existing literature. The proposed approach is applied to the real effective exchange rates of 10 selected countries of the organization of economic co-operation and development (OECD) and the paper observe some interesting findings which demonstrate the usefulness of the model.

Autoregressive modeling and diagnostics for qPCR amplification

Motivation Current methods used to analyze real-time quantitative polymerase chain reaction (qPCR) data exhibit systematic deviations from the assumed model over the progression of the reaction. Slight variations in the amount of the initial target molecule or in early amplifications are likely responsible for these deviations. Commonly used 4- and 5-parameter sigmoidal models appear to be particularly susceptible to this issue, often displaying patterns of autocorrelation in the residuals. The presence of this phenomenon, even for technical replicates, suggests that these parametric models may be misspecified. Specifically, they do not account for the sequential dependent nature of the amplification process that underlies qPCR fluorescence measurements. Results We demonstrate that a Smooth Transition Autoregressive (STAR) model addresses this limitation by explicitly modeling the dependence between cycles and the gradual transition between amplification regimes. In summary, application of a STAR model to qPCR amplification data improves model fit and reduces autocorrelation in the residuals. Availability and implementation R scripts to reproduce all the analyses and results described in this manuscript can be found at: https://github.com/bhsu4/GAPDH.SO. Supplementary information Supplementary data are available at Bioinformatics online.

PERAMALAN HARGA SAHAM DENGAN METODE LOGISTIC SMOOTH TRANSITION AUTOREGRESSIVE (LSTAR) (Studi Kasus pada Harga Saham Mingguan PT. Bank Mandiri Tbk Periode 03 Januari 2011 sampai 24 Desember 2018)

Series such as financial and economic data do not always form a linear model, so a nonlinear model is needed. One of the popular nonlinear models is the Smooth Transition Autoregressive (STAR). STAR has two possible suitable transition function such as logistic and exponential that need to be test to find the appropriate transition function. The purpose of writing this thesis is to determine the LSTAR model, then use the model to predict the stock price of PT Bank Mandiri. This study uses the data of the weekly stock price of PT Bank Mandiri from the period of January 3, 2011 to December 24, 2018 as insample data and the period of January 1, 2019 to December 30, 2019 as outsample data. The research procedure begins with modeling the data with the Autoregressive (AR) process, testing the linearity of the data, modeling with LSTAR, forecasting, and finally evaluating the results of forecasting. Evaluating the results of the forecasting of the weekly share price of PT Bank Mandiri with the STAR model results in the best nonlinear model LSTAR (1,1). This model produces an highly accurate forecasting result with a value of symmetric Mean Square Error (sMAPE) to be 5.12%.Keywords: Nonlinear, Time Series, STAR, LSTAR.

A contribution to the empirics of food price behavior: the case of rice price dynamics in Italy

PurposeThe purpose is to detect the nonlinearity wholesale rice price formation process in Italy in the 1995–2017 period.Design/methodology/approachA nonlinear smooth transition autoregressive (STAR)-type dynamics model is used.FindingsWholesale rice prices are significantly affected by variations in the international price of rice as well as variations in Arborio price.Research limitations/implicationsThe limitations include policy recommendations for the production and commercialization of rice in Italy.Practical implicationsUnderstanding rice pricing dynamics and nonlinearity behavior is pivotal for the survival of the entire European and Italian rice supply chain.Originality/valueIn the extant literature, no evidence exists on non-linearity of rice prices in Italy.