The Causal Relationship Between Stocks, Gold, Crude Oil, and Bond Returns in Poland
Abstract
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.
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Al-Ameer, M., Hammad, W., Ismail, A., & Hamdan, A. (2018). The relationship of the gold price with the stock market: The case of Frankfurt Stock Exchange. International Journal of Energy Economics and Policy, 8(5), 357–371.
Baek, C. (2019). How are gold returns related to stock or bond returns in the U.S. market? Evidence from the past 10-year gold market. Applied Economics, 50(51), 5490–5497. https://doi.org/10.1080/00036846.2019.1616062
Becketti, S. (2013). Introduction to Time Series Using Stata. Stata Press Publication.
Beckmann, J., Berger, T., & Czudaj, R. (2015). Does gold act as a hedge or a safe haven for stocks? A smooth transition approach. Economic Modelling, 48, 16–24. https://doi.org/10.1016/j.econmod.2014.10.044
Chevallier, J., & Ielpo, F. (2013). Cross-market linkages between commodities, stocks and bonds. Applied Economics Letters, 20(10), 1008–1018. http://dx.doi.org/10.1080/13504851.2013.772286
Chkili, W. (2022). The links between gold, oil prices, and Islamic stock markets in a regime-switching environment. Eurasian Economic Review, 12, 169–186. https://doi.org/10.1007/s40822-022-00202-y
Deng, C., Su, X., Wang, G., & Peng, C. (2022). The existence of flight-to-quality under extreme conditions: Evidence from a nonlinear perspective in Chinese stocks and bonds’ sectors. Economic Modelling, 105895. https://doi.org/10.1016/j.econmod.2022.105895
Diebold, F.X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57–66. https://doi.org/10.1016/j.ijforecast.2011.02.006
Diebold, F.X., & Yilmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1), 119–134. https://doi.org/10.1016/j.jeconom.2014.04.012
EIA. (2023). Petroleum & other liquids, spot prices. http://www.eia.gov/dnav/pet/pet_pri_spt_s1_d.htm
Enders, W. (2010). Applied Econometric Time Series. Wiley & Sons.
Enwereuzoh, P.A., Odei-Mensah, J., & Junior, P.O. (2021). Crude oil shocks and African stock markets. Research in International Business and Finance, 55, 101346. https://doi.org/10.1016/j.ribaf.2020.101346
Golitsis, P., Gkasis, P., & Bel, S.K. (2022). Dynamic spillovers and linkages between gold, crude oil, S&P 500, and other economic and financial variables. Evidence from the USA. The North American Journal of Economics and Finance, 63, 101785. https://doi.org/10.1016/j.najef.2022.101785
Granger, C. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424–438. https://doi.org/10.2307/1912791
Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration with applications to the demand for money. Oxford Bulletin of Economics & Statistics, 52(2), 169–210. https://doi.org/10.1111/j.1468-0084.1990.mp52002003.x
Juselius, K. (2006). The Cointegrated VAR Model: Methodology and Applications. Oxford University Press.
Kusideł, E. (2000). Modele wektorowo-autoregresyjne VAR: metodologia i zastosowania. Absolwent.
Mamcarz, K. (2018). Podstawowe klasy aktywów jako determinanty ceny złota w okresie długim. Analiza zależności. Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, 533, 160–170. https://doi.org/10.15611/pn.2018.533.16
Mamcarz, K. (2021). Granger causality between stock and gold returns – evidence from Poland, Hungary and the Czech Republic. Annales Universitatis Mariae Curie-Skłodowska, sectio H – Oeconomia, 55(3), 67–80. https://doi.org/10.17951/h.2021.55.3.67-80
Mensi, W., Reboredo, J., & Ugolin, A. (2021). Price-switching spillovers between gold, oil, and stock markets: Evidence from the USA and China during the COVID-19 pandemic. Resources Policy, 73, 102217. https://doi.org/10.1016/j.resourpol.2021.102217
NBP. (2023). Historic average exchange rates – table A. https://nbp.pl/en/statistic-and-financial-reporting/rates/archive-table-a-csv-xls/
Sekuła, P. (2018). The causal relationships between WIG20 and PLN. Annales Universitatis Mariae Curie-Skłodowska, sectio H – Oeconomia, 52(4), 73–81. https://doi.org/10.17951/h.2018.52.4.73-81
Sims, C. (1980). Macroeconomics and reality. Econometrica, 48(1). https://doi.org/10.2307/1912017
Singh, D. (2014). The dynamics of gold prices, crude oil prices and stock index comovements: Cointegration evidence of India. Finance India, 28(4), 1265–1274.
Stooq.pl. (2023). Historical data Treasury BondSpot Poland Index (^TBSP). https://stooq.pl/q/d/?s=%5Etbsp&c=0&i=m
Tiwari, A. K., Adewuyi, A., & Roubaud, D. (2019). Dependence between the global gold market and emerging stock markets (E7+1): Evidence from Granger causality using quantile and quantile-on-quantile regression methods. The World Economy, 42(7), 2172–2214. https://doi.org/10.1111/twec.12775
Tursoy, T., & Faisal, F. (2018). The impact of gold and crude oil prices on stock market in Turkey: Empirical evidences from ARDL bounds test and combined cointegration. Resources Policy, 55, 49–54. https://doi.org/10.1016/j.resourpol.2017.10.014
WGC. (2023). Gold spot prices. https://www.gold.org/goldhub/data/gold-prices
Wu, Y., Zhou, X. (2015). VAR Models: Estimation, inferences, and applications. In C.F. Lee & J. Lee (Eds.), Handbook of Financial Econometrics and Statistics (pp. 2077–2091). Springer. https://doi.org/10.1007/978-1-4614-7750-1_76
Zakamulin, V., & Hunnes, J.A. (2021). Stock earnings and bond yields in the US 1871–2017: The story of a changing relationship. The Quarterly Review of Economics and Finance, 79, 182–197. https://doi.org/10.1016/j.qref.2020.05.013
DOI: http://dx.doi.org/10.17951/h.2024.58.4.127-147
Date of publication: 2024-10-26 13:52:08
Date of submission: 2023-11-20 21:13:53
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