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The Index of the Financial Safety (IFS) of South Africa and Bayesian Estimates for IFS Vector-Autoregressive Model

Roman Matkovskyy

Open publisher page 5 citations

Abstract

This paper proposes an approach to explore the strength of the financial system of a country against the possibility of financial perturbations appearing based on the construction of the Index of Financial Safety (IFS) of a country. The Markov Chain Monte Carlo (MCMC) and Gibbs sampler technique is used to estimate a Bayesian Vector Autoregressive Model of the IFS of South Africa for the period 1990Q1-2011Q1 and to forecast its value over the period 2011Q2-2017Q1. It is shown that the IFS could capture the disturbances in the financial system and the BVAR models with the non-informative and Minnesota priors could predict the future dynamics of IFS with sufficient accuracy.

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What this paper is about

This paper proposes an approach to explore the strength of the financial system of a country against the possibility of financial perturbations appearing based on the construction of the Index of Financial Safety (IFS) of a country. The Markov Chain Monte Carlo (MCMC) and Gibbs sampler technique is used to estimate a Bayesian Vector Autoregressive Model of the IFS of South Africa for the period 1990Q1-2011Q1 and to forecast its value over the period 2011Q2-2017Q1. It is shown that the IFS could capture the disturbances in the financial system and the BVAR models with the non-informative and Minnesota priors could predict the future dynamics of IFS with sufficient accuracy.

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OpenAlex reports 5 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

This paper proposes an approach to explore the strength of the financial system of a country against the possibility of financial perturbations appearing based on the construction of the Index of Financial Safety (IFS) of a country. The Markov Chain Monte Carlo (MCMC) and Gibbs sampler technique is used to estimate a Bayesian Vector Autoregressive Model of the IFS of South Africa for the period 1990Q1-2011Q1 and to forecast its value over the period 2011Q2-2017Q1. It is shown that the IFS could capture the disturbances in the financial system and the BVAR models with the non-informative and Minnesota priors could predict the future dynamics of IFS with sufficient accuracy.

Key concepts: Markov chain Monte Carlo, Autoregressive model, Gibbs sampling, Bayesian probability, Index (typography), Econometrics, Prior probability, Markov chain

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