Forecast Error Variance Decomposition Analysis Research Articles

The Causal Relationship Between Stocks, Gold, Crude Oil, and Bond Returns in Poland

Theoretical background: Capital investments involve taking risks to achieve a favourable rate of return. Investors are offered various options, including stocks, bonds, and commodities. The aforementioned investment opportunities are considered alternatives due to their varying price volatility and risk levels. Bonds and gold demonstrate a low or negative correlation with equities. On the other hand, crude oil and gold are positively correlated. An essential issue in analysing financial markets is to capture the dynamic behaviour of the financial time-series data, i.e. variables such as prices and returns on investments in assets mentioned above. In decision-making, investors should consider the causal relationship between asset classes, as some variables may contain important information about the future dynamics of other variables. The ability to predict future outcomes is crucial to reduce uncertainty. Vector autoregression (VAR) models are regarded as a useful modelling tool in investigating interrelations between financial instrument prices and returns. Purpose of the article: The paper aims to evaluate the linkages between stock, gold, crude oil, and bond returns in Poland. We focus on analysing both the long term equilibrium between logarithmic financial asset prices and the Granger causality between logarithmic returns in the short term. Research methods: The causal relationships between the stocks, gold, crude oil, and bond logarithmic returns are investigated based on the VAR/VECM estimates. The empirical data cover the period from December 2006 to January 2023. We use the following market data to measure the interrelations between the considered assets: WIG Index, gold and crude oil spot prices, and bond index reflecting the price movements on the Polish financial market. We carry out the test for stationarity employing the ADF and Phillips–Perron tests. The pre-estimation process also involves identifying the number of lags and conducting the Johansen cointegration test since variables are integrated of order one to examine the existence of the long-term equilibrium between the logarithmic prices of assets mentioned above. We use the Wald test for the models’ parameters to indicate the type of Granger causality between logarithmic returns. The forecast error variance decomposition (FEVD) and impulse response functions (IRF) analysis is also applied. Moreover, the post-estimation procedure includes a test for parameters stability and white noise of residuals. All calculations are performed using Stata’s standard software package. Main findings: The results suggest that the markets we examined in Poland are cointegrated, meaning a long-term relationship exists between the prices of financial assets. Additionally, we prove that gold logarithmic prices have a negative impact on the stock index, indicating an inverse relationship between their price developments. We also discover that crude oil and bonds’ effects on the stock market are positive. Although all β coefficients of the error correction equation are statistically significant, the short-term adjustment to equilibrium did not occur based on the α coefficients of the short-term equations. According to the Granger causality analysis related to the VAR in first differences, gold price changes have a short-term impact on stock returns. In contrast, stock returns cause crude oil returns. However, we find no causal linkages in the remaining cases, suggesting that the variables are independent. Changes in asset prices are mainly attributed to their shocks, while observed impacts described by IFR function patterns die out quite quickly. Overall, results confirm that investors can combine gold and stocks in their portfolios, as these interrelated assets have alternative investment properties. Additionally, it is worth noting that the correlation between stock returns and crude oil or bonds is undesirable when constructing portfolios.

Determinants of the Developed Country Index and Indonesian Macroeconomic on the IDX Growth 30 and IDX Value 30

This study analyzes the determinants of the Developed Country Index (Hang Seng Index, Dow Jones Index) and Indonesian macroeconomic variables (10-Year Government Bond Yield, Foreign Ownership in Government Bonds, and the BI-7 Day Reverse Repo Rate, BI7DRR) on the movement of the IDX Growth 30 Index (IDXG30) and IDX Value 30 Index (IDXV30) from 2018–2022. Monthly time series data is used with a saturated sampling technique, processed via Eviews 12 using VECM analysis. The study conducts Stationarity, Optimal Lag, VAR Stability, and Cointegration Tests, along with Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD) analyses Results show that the Hang Seng Index and Foreign Ownership in Government Bonds negatively affect IDXG30 and IDXV30, while BI7DRR positively influences both. The Dow Jones Index positively affects IDXG30 but negatively impacts IDXV30. Meanwhile, the 10-Year Government Bond Yield negatively affects IDXG30 and positively influences IDXV30. Additionally, the performance of IDXG30 and IDXV30 themselves and BI7DRR significantly contribute to both indices' movements.

How Important are Commodity Terms of Trade Shocks in Explaining Government Tax Revenue Fluctuations? Evidence from Ethiopia

Using a new database of commodity terms of trade and identifying country-specific commodity export and import price shocks separately, this article mainly aims to examine the importance of commodity export and import price shocks in explaining government tax revenue fluctuations using Ethiopia as an example over the sample period 1980–2019. This article employs a structural vector autoregressive model and forecast error variance decomposition analysis to address this objective. The results show that commodity export and import price shocks account for around 48% of government tax revenue fluctuations. While commodity export price shocks contribute to around 37% of fluctuations in government tax revenue, commodity import price shocks account for only 11% of government tax revenue fluctuations. These findings are discussed in terms of their policy implications. JEL Classification C32, F41, F44

거시경제변수와 국유재산 공매 낙찰가율 간의 영향관계 실증 분석

This study analyzes the dynamic effects of macroeconomic variables on successful bid prices for the appraised value (PTV) of national property. The analysis uses the following macroeconomic variables: interest rates (COFIX), money supply (M2), stock index (KOSPI), consumer price index (CPI), and economic sentiment index (ESI). Monthly data of 151 months from January 2011 to July 2023 are used. The VEC model is fitted to capture the dynamic effects of macroeconomic variables on PTV. The analysis yielded the following results. First, when estimating the long-term relationship between variables and successful bid prices, we find that M2 and ESI have positive effects on PTV, whereas COFIX, KOSPI, and CPI have negative effects. Second, in the impulse response analysis, PTV exhibits positive responses to shocks in M2 and ESI but negative responses to shocks in COFIX, KOSPI, and CPI. Third, the forecast error variance decomposition analysis reveals that 24 months after the exogenous shocks affect the variables, the forecast error variance of PTV can be explained by shocks to KOSPI, M2, CPI, and COFIX, accounting for 17.1%, 8.4%, 7.2%, and 6%, respectively. ESI has minimal explanation power.

Asymmetric Effects of Oil Price Volatility on Government Expenditure in Oil-Importing African Countries: Evidence from Zambia

This paper examines the impact of disaggregated oil price shocks the government expenditure in oil-importing African countries using the case of Zambia. The analysis utilizes a“Structural Vector Autoregressive (SVAR) Model” to evaluate the short-run effects, while a “Vector Error Correction Model (VECM) is employed to analyze the relationship over the long-run. Results of the short-run analysis reveal a notable positive influence of oil price shocks”on government expenditure that emanates from global aggregate and precautionary demand factors. The aforementioned results are clarified by the effects that increased global aggregate demand has on copper priсes, which in turn influences government revenue derived from taxes. Additionally, the transmission of “financial contagion and volatility spillovers” from the οil to the copper market clarifies the influence of precautionary demand on government expenditure. The findings obtained from the VECM indicate that global aggregate demand has positive effects on public sector spending in the long run. Additionally, the analysis demonstrates that the rate at which public sector spending recovers to its equilibrium level after a temporary deviation is both monotonic and statistically significant. Moreover, among the three categories of οil priсe hikes, Forecast Error Variance Decomposition analysis reveals that shocks related to precautionary demand have the most significant impact, accounting for 11.7% of the observed variation in public sector spending.

Analisis Kebijakan Moneter melalui Jalur Harga Aset di Negara Muslim Berkembang

This research aims to analyze the contribution of variables from the interaction of monetary policy transmission variables (SBI, STOCK, INVESTMENT, GDP, INFLATION). This research uses secondary data or time series, namely from the first quarter of 2011 to the first quarter of 2016. The data analysis model in this research is Vector Autoregression (VAR) model and sharpened with Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD) analysis. The results of the VAR analysis show that the past variable (t-1) contributes to the current variable both the variable itself and other variables and from the estimation results it turns out that there is a reciprocal relationship between the variables where all the variables are monetary policy transmission variables (SBI, STOCK, INVESTMENT, GDP, INFLATION) contribute to each other. The results of the IRF analysis show that the stability of the response of all variables is formed in period 5 or the medium and long term, where the response of other variables to changes in one variable shows different variations from positive to negative responses or vice versa, and there are variables whose responses remain positive or remains negative from the short term to the long term. The results of the FEVD analysis show that there are variables that have the largest contribution to the variable itself in the short, medium and long term, such as GDP and SB. Meanwhile, other variables that have the greatest influence on the variable itself in the short, medium and long term are GDP which is most influenced by INVESTMENT, Inflation is most influenced by GDP, INVESTMENT is most influenced by Inflation, SB is most influenced by INVESTMENT while it is most influenced by SB. The results of the analysis of the interaction of each monetary policy transmission variable in the path of asset prices in developing Muslim countries in the short, medium and long term show that monetary policy transmission can increase economic growth and achieve the inflation targets of developing Muslim countries.

Navigating Economic Uncertainty: Exploring The Impact Of Reduced Bank Credit On The Construction Industry

Purpose: This paper aims to analyze credit in the Indonesian banking construction sector empirically. Specifically, we examine whether economic conditions affect construction sector credit and how the response and diversity of construction sector credit in the face of economic shocks. Methodology/approach: This article use monthly data on Indonesian banking credit from 2004 to 2022 using the Vector Error Correction Model (VECM). Findings: The long-term effects of the economy on credit are shown by the results of the VECM research. The Impulse Response Function (IRF) results indicate that credit in the construction industry has both positive and negative effects. Forecast Error Variance Decomposition (FEVD) analysis shows that gross domestic product, crises, industrial price index, interest rates, exchange rates, and inflation affect credit diversity. Practical implications: Banking credit is an essential component in meeting company needs. Banks need to consider several things in the distribution of credit to the construction sector, especially those that impact the long term. Originality/value: This research provides a better understanding of how the construction sector credit can be affected by changing economic conditions, and how diverse credit responses and policies are in the face of such shocks.

The Impact of Uncertainty Shocks to Consumption under Different Confidence Regimes Based on a Stochastic Uncertainty-in-Mean TVAR Model

Exogenous uncertainty shocks may have different effects on domestic and foreign consumption under different consumer confidence regimes. In this paper, we specify a threshold vector autoregressive (TVAR) model with different consumer confidence regimes to study the response of endogenous macroeconomic variables to exogenous shocks. The evidence shows that in China, compared to high consumer confidence, low consumer confidence dampens consumption both at home and abroad. However, low consumer confidence benefits exports, the source of foreign consumption. A forecast error variance decomposition analysis further confirms the difference in the effects under different consumer confidence regimes. A comparative analysis shows that consumer confidence is much more influential in the US than in China. Our findings differ from those of earlier works, as we introduce stochastic uncertainty to both the mean and heteroscedasticity and apply counterfactual analysis to show the hazard of ignoring stochastic uncertainty in the traditional threshold vector autoregression. Finally, from the ex ante and ex post perspectives, we provide managerial implications for the authorities to tackle economic issues based on different consumer confidence regimes.

The Impact of Fed’s Monetary Policy on the Capital Market in the USA

The focus of this paper is the impact of the Federal Reserve (Fed) System’s monetary policy on the United States capital market. To identify the capital market, we have segmented it into the government bond market, the corporate bond market, and the stock market. We have utilized a structural vector autoregressive model methodology in order to assess the interrelations between six US variables. We have performed impulse response functions and forecast error variance decomposition analyses for the model interpretation. The empirical findings based on monthly data suggest that the results, in general, are consistent with the expected effects, despite some deviations. The responses of the government, corporate bond markets and the stock market in the United States to the Fed’s monetary policy interest rate shocks have developed mainly in the short run and gradually faded away in the long run. There are significant interdependencies between the observed variables, though each of them is affected to its own specific extent.

Corporate governance, capital structure, and firm performance: a panel VAR approach.

This study aims to examine the interrelationships and interdependencies between corporate governance (CG), capital structure (CS), and firm performance (FP) of companies listed on the Stock Exchange of Mauritius from 2009 to 2019 along with a comparison between financial and non-financial firms. A panel vector autoregression (PVAR) approach is used in this study to determine the relationship dynamics between CG, CS and FP. The findings reveal a positive and significant bidirectional association between CS and FP, supporting the trade-off theory. The results also show that CG and FP jointly help to increase CS while CG and CS jointly boost the profitability of firms. A strong bidirectional relationship with varied signs between CG and CS is found only for financial firms. The results of the forecast error variance decomposition analysis support the selection of FP as the most endogenous variable. Robustness tests also support the findings. This study is the first to examine the dynamic and interdependent relationships using a PVAR model between CG, CS and FP that presents new contributions to the existing CG and CS literature with insights from an emerging economy.

The Nexus Between Remittance Inflows and Financial Development in India: Substitutes or Complements?

This article investigates the relationship between remittance inflows and financial development in India from 1980 to 2018. The study employed Autoregressive Distributed Lag (ARDL) and Vector Error Correction (VEC) models to capture the short and long-run dynamics. In addition, the impulse response function (IRF) and forecast error variance decomposition (FEVD) analysis were utilised to understand the dynamic reaction of financial development to a given shock to remittance inflows and other variables. The results of the ARDL model reveal that remittances negatively influence financial development in the short run, while they positively influence it in the long run. The IRF analysis shows that financial development responds positively to one standard positive shock to remittance inflows. The FEVD analysis further reveals that shocks to remittance inflows explain around 30% to 32% of the total variation in financial development. From a policy standpoint, the findings suggest that well-framed policies should be formulated and implemented to encourage more remittance flows through formal channels. It will boost financial development, economic growth and also increase the other developmental effects of remittances on the economy. JEL Codes: C32, F22, F37

The changing economic relationship between some of the major COVID-19 impacted countries with prominent wealth: a comparative study from the view point of stock markets.

In the present work, a study has been made over the prime stock indices of some fiscally prominent countries impacted by COVID-19. The countries are separated in two ways: (1) considering gross total number of infected cases—here seven mostly impacted countries with certain global economic influence are selected; (2) considering the concentration of the infected cases—here six major impacted countries with considerable influence are selected. This sort of categorization is itself a novel strategy which is capable of including some less populated, but severely impacted countries of economic importance. The objective of the present analysis is to comprehend the impact of COVID-19 on these markets and to recognize the effect of COVID-19 on mutual association and dependence between these markets. To add more flavour of reliability, we have taken a new and fresh strategy of fixing the time frames under consideration before and during COVID-19 pandemic as uniform. We have used both linear and nonlinear Granger causality analysis and employed generalized forecast error variance decomposition analysis to review the exogeneity and endogeneity of the individual markets. The present study shows that this pandemic has changed the underlying relationship: some exogenous stock markets have become endogenous and vice versa in the pandemic. Linear relationship has been reduced radically, whereas nonlinear relationship has been improved during the COVID-affected period. TASE, the highest returned and significantly uncorrelated index, emerged as the most exogenous market in the pre-COVID period, though it is nonlinearly endogenous in the long term, in the COVD-affected period. CAC 40 is the most endogenous market for the short term in both pre-COVID and COVID-affected period. B3 and NYSE, exogenous in the pre-COVID period, turned out to be linearly endogenous in the COVID-affected duration, whereas BIST 100 and BSE SENSEX are found to be exogenous markets in the COVID-affected period according to both linear and nonlinear causal analysis. They were also exogenous in the pre-COVID era for the short-term period, with BSE SENSEX exhibiting exogeneity anti-persistently for the COVID-affected period too. Association among the markets is more in long term rather than short term. A possible conclusion is also that the markets may regain long-term association once the effect of COVID would fade away.

Charting Future Resilient Sukuk Market in Malaysia: What can be Learned from Macreconomic Determinants of Sukuk Issuance?

This article examines the effects of macroeconomic variables on sukuk issuance in Malaysia from the first quarter of 2005 to the fourth quarter of 2019. It seeks to analyse the relationship between Sukuk issuance and selected macroeconomic variables such as Bursa Malaysia composite index (BMCI), exchange rate (EXR), GDP, Inflation (INF) and interest rate (INT). This study employs time series analysis techniques such as Johansen Co-Integration Analysis, Vector Error Correction Model (VECM), and Forecast Error Variance Decomposition (FEVD) analysis to unravel the short run and the long run dynamics among the selected variables. Our findings suggest that BMCI, EXR, INT are significant in affecting sukuk issuance in Malaysia in the long run while for the short run, the BMCI, EXR, and GDP, are significant. EXR is the most important contributor in short-term for sukuk issuance, followed by GDP and BMCI. These findings further suggest that the government can closely monitor exchange rate (EXR) movement to promote fundings of business projects through sukuk issuance. The findings on the significance of BMCI in both the short run and the long run suggest that investors can also consider investing in sukuk securities within their portfolio investment to manage the risk and return profile. It is also recommended that policy makers can design policies to ensure the performance of BMCI and GDP growth to achieve a resilient sukuk market especially during and post the Covid-19 pandemic.

Stock Market Synchronization and Stock Volatility: The Case of an Emerging Market

The purpose of this paper is to study the effect of stock market synchronization on the volatility of its component assets. For this objective, we calculate the stock market's synchronization using the Minimum Spanning Tree Length (MSTL) network analysis method. Then, we implement forecasting tests in and out the sample to assess the forecasting power on the stock market's synchronization to predict the individual stock realized volatility. Additionally, we test a VAR and a forecast error variance decomposition analysis to study Granger causality's presence on volatility. Our results show that synchronization within a market exists and changes over time. Our main results show that an increase in synchronization causes an increase in financial assets' realized volatility in the following month. Our results made it possible to study financial markets' synchronization and take a systemic risk approach to improve investment management. Our main idea was that the stock markets' synchronization positively correlates with financial assets' volatility. The greater the synchronization, the greater the volatility in the following period. This study offers a new approach to study the stock market volatility.

On the macroeconomic effects of news about innovations of information technology

Existing literature shows a strong relationship between innovations of information technology (IT) and their falling prices, which result in increased use of computer and information processing equipment, and related demand for investment and labor at the firm level. This paper investigates the macroeconomic effects of news about future IT innovations. Utilizing the maximum forecast error variance approach, we identify IT news shock from the real investment price on computers and related information processing equipment. We provide robust evidence that IT news shocks induce a co-movement in key macroeconomic variables, including output, labor hours, consumption, and investment in IT and non-IT sectors. The forecast error variance and historical decomposition analysis show that news shocks about future IT significantly contribute to business cycle fluctuations. Strong connections between news about future IT innovations and the macroeconomy suggest that news could affect firms’ investment and labor demand decisions when they undergo an organizational redesign to adopt new technology.

Do information and communication technology and renewable energy use matter for carbon dioxide emissions reduction? Evidence from the Middle East and North Africa region

This study aims to investigate whether information and communication technologies (ICT) and renewable energy consumption can help improve environmental quality for a selected group of the Middle East and North Africa (MENA) region. By using the Panel Vector Autoregressive model over the period 1980–2019, the results show evidence for the first-order effects of ICTs on CO2 emissions, indicating that the use of ICT in the current economic development context of the MENA region lead to a deterioration of the environmental quality.The results also show that renewable energy consumption improves environmental quality whatever the sample and the proxy for ICT used. Overall, the results of the impulse responses functions (IRFs) show that the impact of shocks on ICT and renewable energy last between 1 and 7 years. The results of the IRFs are confirmed by the forecast error variance decomposition analysis, which shows that the contributions of ICT and renewable energy to the variability of CO2 emissions is not zero. Finally, in tests for causality, the results reveal evidence for bidirectional causality in most cases between CO2 emissions and ICT and renewable energy consumption. To benefit from the potential positive impact of ICT and renewable energy consumption on the quality of the environment, several ICT and renewable energy policies have been developed and discussed.

Determinants of homelessness in the U.S.: new hypotheses and evidence

ABSTRACT This study seeks to provide new insights into factors that influence homelessness in the U.S. by empirically investigating two heretofore effectively unexplored hypotheses as they relate to homelessness. The first hypothesis is that the greater the overall degree of entrepreneurial activity in a given environment, the lower the degree of homelessness. The second hypothesis is that homelessness is a decreasing function of the overall degree of labour market freedom. Panel VAR, Granger causality, and Cholesky forecast-error variance decomposition analyses are undertaken. Overall, strong, empirical support for both hypotheses is obtained. Accordingly, the homelessness rate is found to be a decreasing function of both the overall degree of entrepreneurial activity in a given state and the overall degree of labour market freedom in that state. Hence, it is argued that policies promoting entrepreneurial activity and labour freedom potentially can be useful tools in helping to diminish the degree of homelessness in the U.S.

Indian Money and Equity Market: Are They Entwined?

After almost three decades of the New Economic Policy of the Government of India, now it is high time to assess the policy prescriptions so far followed to converge the financial markets, especially the stock and money market, objectively with the robust statistical and econometric tools. This paper attempts to measure the integration amongst stock and some select segments of Indian money market. The results of Johansen and Juselius (1990) cointegration test suggest strong integration amongst the markets and statistically significant presence of all the markets in the cointegration space. Granger causality expectedly runs from money to stock market and Forecast Error Variance Decomposition Analysis indicates the rigidity of the stock and treasury bills market only, hence, warrants more attention of the Indian policy planners for adequate steps to make these markets more flexible.

Understanding Business Cycle Fluctuations in Pakistan

Notwithstanding the level of improvement in understanding the complexities of an economy, it is now well accepted that the ultimate incidence of various policy interventions leads to varied outcomes in terms of magnitude and persistence depending upon the structure of the economy. The objective of the present study is to disentangle the relative contributions of various exogenous and domestic shocks that contribute to business cycle fluctuations in Pakistan. The study is based on the New-Keynesian Open economy model, which is an extended version of (Gali & Monacili 2005). Keating’s two-step approach (1990, 2000) is employed to capture the dynamic behaviour of the variables of interest. Impulse response functions, along with forecast error variance decomposition analyses, are used to gain useful insights into the understanding of the transmission mechanism of policy and non-policy shocks. It is observed that fiscal policy does matter, at least in the short-run. The interest rate shock leads to the exchange rate appreciation thereby confirming the exchange rate puzzle. In response to adverse supply shocks, the Monetary Authority responds with a monetary contraction that prolongs the recessionary periods. Furthermore, it has a limited power to control inflation as inflation in Pakistan stems from supply-side factors as well as fiscal dominance. JEL Classification: C32, E52, E62, F41 Keywords: Open Economy, New Keynesian Model, Rational Expectations, Exchange Rate Puzzle

Dynamic Linkages of Stock Market Sectors: Evidence from the Kenyan Stock Market

This paper undertakes an empirical investigation into the dynamic linkages of the Nairobi Securities Exchange’s sectors. Using weekly indices for the 9th June 2008 to 14th February 2019 period, it examines the dynamic linkages of the Investment, Manufacturing and Allied, and Banking sectors. The study finds out the presence of one cointegrating relationship in the long run. A Vector Error Correction Model is fitted to estimate the temporal relationships of the three indexes. Results of Granger Causality testing, which are based on the VECM output, indicate the presence of bidirectional granger causality between the Manufacturing and Allied & Banking sectors as well as the Banking & Investment sectors. However, there is no granger causality between the Investment & Manufacturing and Allied sectors. Impulse response analysis show that shocks to the Manufacturing and Allied Sector are least significant in terms of their ability to invoke responses from other sectors, while shocks to the Banking sector are most influential as far are the tendency of elevating the volatility of other sectors’ indexes is concerned. Forecast Error Variance Decomposition (FEVD) analysis further indicate that shocks to the banking sector are most influential in terms of their explanatory power of other sectors’ forecast variances. The study therefore concludes that the banking sector has the highest tendency to influence other sectors’ volatility, while the Manufacturing and Allied Sector is least significant in terms of its ability to influence other sectors’ volatility. Therefore, it is recommended that that stocks that are listed under the Manufacturing and Allied Sector should be considered for diversification purposes due to the low scale of linkages that this sector has with other sectors. But, better still; investors should seek inter-market diversification opportunities in order to tap fully into the benefits of portfolio diversification.