Linear Vector Error Correction Model Versus Markov Switching Vector Error Correction Model To Investigate Stock Market Behaviour

The stock market can reflect the economy of a country. The movement of the stock market index may imply the economic condition in general. The 1997 Asian Financial Crisis and the 2008 Global Economic Crisis are examples of share depressions that impacted countries’ inflation, unemployment rates and gross national product (GNP). This study investigates how oil and gold prices impact the stock exchange using a linear vector error correction model (VECM) and a Markov switching vector error correction model (MS-VECM). The results show that oil and gold prices affect the stock market returns for the four selected countries, namely Malaysia, Singapore, Thailand and Indonesia. The MS-VECM is able to capture every change in the transition probabilities of the financial time series data and is more reliable than the linear VECM for examining the effect of oil and gold prices on the stock market

Do gold prices respond to real interest rates? Evidence from the Bayesian Markov Switching VECM model

The goal of this paper is to examine the transmission dynamics between the real interest rate and gold prices in the G7. The methodology follows the Bayesian Markov-Switching Vector Error-Correction (MS-VECM) model, along with regime-dependent impulse response functions, spanning the period 1975–2016. The findings suggest a positive association between gold prices and real interest rates, with the estimates remaining consistently positive and statistically significant across all G7 countries. The results indicate that gold prices can provide hedging services against real interest rate movements mainly during recessionary times. Our results continue to be robust when we extend the bivariate version of our modeling approach to include more drivers for gold prices

A Markov Switching Vector Error Correction Model on Oil Price and Gold Price Effect on Stock Market Returns

Stock market index represent a country growth and always as an interest for economist and statisticians. In this paper, the effect of oil price and gold price on stock market index on Malaysia, Singapore, Thailand and Indonesia are investigated and a two-regime Markov Switching Vector Error Correction model is used to examine the nonlinear properties model. Moreover, a two regime mean adjusted Markov Switching Vector Error Correction model is used in the study to capture the filtered and smoothed probabilities of the time series sequence in the economic model. Results found that the oil price and gold price affect the movement of the Malaysia, Singapore, Thailand and Indonesia stock market index and there is an asymmetric cycle since 97% of the total sample size is recorded in the growth state

Testing for two-regime threshold cointegration in the parallel and official markets for foreign currency in Greece

This paper models the short-run as well as the long-run relationship between the parallel and official markets for US dollars in Greece in a threshold VECM framework. Modeling exchange rates within this context can be motivated by the fact that the transition mechanism is controlled by the parallel market premium. The results show that linearity is rejected in favour of a TVECM specification, which forms statistically an adequate representation of the data. Two regimes are implied by the model; the “typical” regime, which applies most of the time and the “unusual” one associated with economic and political events t hat took place in Greece during the 1980s. Another implication is that in the parallel exchange rate there are strong asymmetries between the two regimes in the speed of adjustment to the long-run equilibrium. Finally, Granger causality runs from the official to the parallel market in both regimes but not vice versa.Parallel market premium, nonlinearity, threshold cointegration, regime switching,

Non-Linearities in the relation between oil price, gold price and stock market returns in Iran: a multivariate regime-switching approach

Iran Stock Exchange is the most important component of Iran capital market and more attention has been paid to it in recent years. Many factors affect the Iran stock exchange. In this paper, the effects of oil price and gold price on stock market index are investigated and a three regime Markov Switching Vector Error Correction model is used to examine the nonlinear properties model during the period January 2003 to December 2014. The results of the study shows that the relationships between variables can be analyzed in three different status, so that the three regimes, respectively, represents the “great depression”, “mild depression” and “expansion” period. The results of the model show that the impact of oil price on stock returns is negative and significant in all three regimes; this means that with rising oil price, stock market returns are reduced. But the relationship between gold price and stock market returns varies during the period, according to market conditions. It means that positive shock inflicted on the price of gold in the short-run (10 months) leads to reduce the stock returns and in the medium-term and long-run, it leads to increase the stock returns

Non-Linearities in the relation between oil price, gold price and stock market returns in Iran: a multivariate regime-switching approach

Iran Stock Exchange is the most important component of Iran capital market and more attention has been paid to it in recent years. Many factors affect the Iran stock exchange. In this paper, the effects of oil price and gold price on stock market index are investigated and a three regime Markov Switching Vector Error Correction model is used to examine the nonlinear properties model during the period January 2003 to December 2014. The results of the study shows that the relationships between variables can be analyzed in three different status, so that the three regimes, respectively, represents the “great depression”, “mild depression” and “expansion” period. The results of the model show that the impact of oil price on stock returns is negative and significant in all three regimes; this means that with rising oil price, stock market returns are reduced. But the relationship between gold price and stock market returns varies during the period, according to market conditions. It means that positive shock inflicted on the price of gold in the short-run (10 months) leads to reduce the stock returns and in the medium-term and long-run, it leads to increase the stock returns

Subsample stability, change detection and dynamics of oil and metal markets: A recursive approach

The analysis of historical price data for patterns and using such patterns for predictions and policy recommendations has become ubiquitous in the existing economics literature. These predictions and recommendations are premised on the stability of the statistical properties and inter-variable dynamics for which a single regime or few number of regimes can capture. This, however, is a strong assumption with serious repercussions if violated. In this study, the appropriateness of the stability assumption is questioned using various recursive regressions to test stability, consistency of stationarity and stability in inter-variable dynamics between crude oil, gold, silver, and platinum prices. Using monthly data sourced from the World Bank Commodity Price Data (Pink Sheet) from January 1, 9960 to March 2022, our empirical analysis found level prices of oil, gold, and platinum to be consistently non-stationary with rare exceptions. The level price of silver however is found to be inconsistent with multiple regime switches while the logged series of all variables yielded non-stationarity. The default is stationarity for all the variables when price series are logged differenced and/or differenced for oil, silver, and platinum. Differenced gold prices resulted in inconsistent stationarity with multiple regime changes. Even if rare, the stationarity of all the variables is dependent on time and sample size due to the inconsistence in the stationarity verdict. On the bi-variate relationship in the long run, only level silver prices are found to be cointegrated with oil while logged silver prices are inconsistently cointegrated with logged oil prices. Also, in the short-run, only log of oil prices is found to Granger cause log of silver prices. It is thus recommended that researchers and policy makers be tempered in extrapolating statistical findings in general and the price and interprice dynamics of oil, gold, silver and platinum into the future

A Comparison of Threshold Cointegration and Markov-Switching Vector Error Correction Models in Price Transmission Analysis

We compare two regime-dependent econometric models for price transmission analysis, namely the threshold vector error correction model and Markov-switching vector error correction model. We first provide a detailed characterization of each of the models which is followed by a comprehensive comparison. We find that the assumptions regarding the nature of their regime-switching mechanisms are fundamentally different so that each model is suitable for a certain type of nonlinear price transmission. Furthermore, we conduct a Monte Carlo experiment in order to study the performance of the estimation techniques of both models for simulated data. We find that both models are adequate for studying price transmission since their characteristics match the underlying economic theory and allow hence for an easy interpretation. Nevertheless, the results of the corresponding estimation techniques do not reproduce the true parameters and are not robust against nuisance parameters. The comparison is supplemented by a review of empirical studies in price transmission analysis in which mostly the threshold vector error correction model is applied.price transmission, market integration, threshold vector error correction model, Markov-switching vector error correction model, comparison, nonlinear time series analysis, Agricultural Finance,

Commodity price volatility, stock market performance and economic growth: evidence from BRICS countries

Abstracts in English, Afrikaans and ZuluThe study investigated the nexus between commodity price volatility, stock market performance, and economic growth in the emerging economies of Brazil, Russia, India, China, and South Africa (the BRICS) predicated on two hypotheses. First, the study hypothesised that in modern integrated financial systems, commodity price volatility predisposes stock market performance to be non-linearly related to economic growth. The second hypothesis was that financial crises are an inescapable feature of modern financial systems. The study used daily data on stock indices and selected commodity prices as well as monthly data on national output proxies and stock indices. The study analysed data for non-linearities, fractality, and entropy behaviour using the spectral causality approach, univariate GARCH, EGARCH, FIGARCH, DCC-GARCH, and Markov Regime Switching (MRS) – GARCH. The four main findings were: first, spectral causality tests signalled dynamic non-linearities in the relationship between the three commodity futures prices and the BRICS stock indices. Second, the predominantly non-linear relationship between commodity prices and stock prices was reflected in the nexus between the national output proxies and the indices of the five main commodity classes. Third, spectral causality analysis revealed that the causal structures between commodity prices and national output proxies were non-linear and dynamic. Fourth, the Nyblom parameter stability tests revealed evidence of structural breaks in the data that was analysed. The DCC-GARCH model uncovered strong evidence of contagion, spillovers, and interdependence. The study added to the body of knowledge in three ways. First, micro and macro levels of commodity price changes were linked with corresponding stock market performance indicator changes. Second, unlike earlier studies on the commodity price – stock market performance – economic growth nexus, the study employed spectral causality analysis, single - regime GARCH analysis, Dynamic Conditional Correlation (DCC) – GARCH and a two-step Markov – Regime – Switching – GARCH as a unified analytical approach. Third, spectral causality graphs depicting relationships between stock indices and national output proxies revealed benign business cycle effects, thus, contributing to broadening the scope of business cycle theoryBusiness ManagementPhD. (Management Studies

Economic Turmoil in Islamic Banking Investment

Investment financing is one of the operational activities of Islamic banking to encourage the real sector. This study aims to analyze the effect of economic turmoil on investment financing, analyze the response to investment financing, and analyze each variable's contribution in explaining the diversity of investment financing. This study uses monthly time series data from 2009 to 2020 using the Vector Error Correction Model (VECM) analysis. The results show that the exchange rate, inflation, and interest rates significantly affect Islamic banking investment financing in the long term. The response to investment financing is the fastest to achieve stability when it responds to shocks to the composite stock price index. Inflation is the most significant contribution in explaining diversity in investment financing. Islamic banking should increase the proportion of funding for investment. Customers can have a larger business scale to encourage economic growth, with investment financing increasing.JEL Classification: E22, G11, G24How to Cite:Budiandru., & Yuniarti, S. (2020). Economic Turmoil in Islamic Banking Investment. Etikonomi: Jurnal Ekonomi, 19(2), 271-286. https://doi.org/10.15408/etk.v19i2.17206