The effect of financial, macroeconomic and sentimental factors on stock market volatility

Anna Czapkiewicz; Agnieszka Choczyńska

doi:10.5604/01.3001.0014.8492

Anna Czapkiewicz AGH University of Science and Technology, Faculty of Management, ul. Antoniego Gramatyka 10, 30-067 Kraków, Poland ORCID:https://orcid.org/0000-0002-6144-8381 , Agnieszka Choczyńska Student of AGH University of Science and Technology, Faculty of Management, ul. Antoniego Gramatyka 10, 30-067 Kraków, Poland Przegląd Statystyczny. Statistical Review, vol. 67, 2020, 4, pages: 274-293 Published online: 31 May 2021 DOI 10.5604/01.3001.0014.8492

962 Views 64 Downloads

ARTICLE

(English) PDF

ABSTRACT

The aim of this paper is to find economic factors that could be helpful in explaining the market’s shifts between periods of prosperity and crisis. The study took into account the main stock indices from developed markets of the USA, Germany and Great Britain, and from two emerging markets, i.e. Poland and Turkey. The analysis confirms the existence of two different states of volatility in these markets, namely the state with a positive returns’ mean and low volatility, and the state with a negative or insignificant mean and high volatility. The Markov-switching model with a dynamic probability matrix was applied in the study. The subject of the analysis was the impact of domestic and global factors, such as VIX and TED spread, oil prices, sentiment indices (ZEW), and macroeconomic indices (unemployment, longterm interest rate, CPI), on the probability of switching between the states. The authors concluded that in all the examined countries, changes in long-term interest rates have an influence on market returns. However, the direction of this impact is different for developed and emerging markets. As regards developed markets, high prices of oil, 10-year bonds, and the ZEW index can suggest a high probability of the countries remaining in the first state, whereas an increase in the VIX index and the TED spread significantly reduces the probability of staying in this state. The other studied factors proved to be rather local in nature.

KEYWORDS

regime shift, equity volatility, macroeconomic factors, sentimental factors, financial markets, TVPMS model

JEL

C52, G11, G15, G32

REFERENCES

Algaba, A., Ardia, D., Bluteau, K., Borms, S., & Boudt, K. (2020). Econometrics meets sentiment: an overview of methodology and applications. Journal of Economic Surveys, 34(3), 512–547. https://doi.org/10.1111/joes.12370.

Aloy, M., De Truchis, G., Dufréenot, G., & Keddad, B. (2014). Shift-Volatility Transmission in East Asian Equity Markets: New Indicators. In G. Dufrénot, F. Jawadi & W. Louhichi (Eds.), Market Microstructure and Nonlinear Dynamics (pp. 273–291). Cham: Springer. https://doi.org /10.1007/978-3-319-05212-0.

Ang, A., & Bekaert, G. (2002). International Asset Allocation With Regime Shifts. The Review of Financial Studies, 15(4), 1137–1187. https://doi.org/10.1093/rfs/15.4.1137.

Apergis, N., & Miller, S. M. (2009). Do structural oil-market shocks affect stock prices?. Energy Economics, 31(4), 569–575. https://doi.org/10.1016/j.eneco.2009.03.001.

Boudt, K., Danielsson, J., Koopman, S. J., & Lucas, A. (2012). Regime switches in volatility and correlation of financial institutions (NBB Working Paper No. 227). http://dx.doi.org/10.2139 /ssrn.2160835.

Celebi, K., & Hönig, M. (2019). The Impact of Macroeconomic Factors on the German Stock Market: Evidence for the Crisis. Pre- and Post-Crisis Periods. International Journal of Financial Studies, 7(2), 1–13. https://doi.org/10.3390/ijfs7020018.

Chang, K. L. (2009). Do macroeconomic variables have regime-dependent effects on stock return dynamics? Evidence from the Markov regime switching model. Economic Modelling, 26(6), 1283–1299. https://doi.org/10.1016/j.econmod.2009.06.003.

Chen, S.-S. (2009). Predicting the bear stock market: Macroeconomic variables as leading indicators. Journal of Banking and Finance, 33(2), 211–223. https://doi.org/10.1016 /j.jbankfin.2008.07.013.

Chen, N.-F., Roll, R., & Ross, S. A. (1986). Economic Forces and the Stock Market. The Journal of Business, 59(3), 383–403. https://doi.org/10.1086/296344.

Chollete, L., Heinen, A., & Valdesogo, A. (2009). Modeling international financial returns with a multi-variate regime switching copula. Journal of Financial Econometrics, 7(4), 437–480. https://doi.org/10.1093/jjfinec/nbp014.

Czapkiewicz, A. (2018). Determinanty zmian współzależności wybranych giełd papierów wartościowych. Łódź: Wydawnictwo Uniwersytetu Łódzkiego.

Diebold, F. X., Gunther, T. A., & Tay, A. S. (1998). Evaluating density forecasts with applications to financial risk management. International Economic Review, 39(4), 863–883. https://doi.org /10.2307/2527342.

Doman, R. (2011). Zastosowania kopuli w modelowaniu dynamiki zależności na rynkach finansowych. Poznań: Wydawnictwo Uniwersytetu Ekonomicznego.

Doman, M., & Doman, R. (2014). Dynamika zależności na globalnym rynku finansowym. Warszawa: Difin.

Dufrénot, G., Damette, O., & Frouté, P. (2014). Anticipated macroeconomic fundamentals, sovereign spreads and regime-switching: the case of the Euro area. In G. Dufrénot, F. Jawadi & W. Louhichi (Eds.), Market Microstructure and Nonlinear Dynamics (pp. 205–234). Cham: Springer. https://doi.org/10.1007/978-3-319-05212-0_8.

Ferson, W. E., & Harvey, C. R. (1994). Sources of risk and expected returns in global equity markets. Journal of Banking and Finance, 18(4), 775–803. https://doi.org/10.1016/0378-4266(93) 00020-P.

Filardo, A. J. (1994). Business-Cycle Phases and Their Transitional Dynamics. Journal of Business and Economic Statistics, 12(3), 299–308.

Forbes, J. K., & Chinn, M. D. (2004). A decomposition of global linkages in financial markets over time. The Review of Economics and Statistics, 86(3), 705–722. https://doi.org/10.1162 /0034653041811743.

García, D. (2013). Sentiment during Recessions. Journal of Finance, 68(3), 1267–1300. https://doi.org/10.1111/jofi.12027.

Hamilton, J. D. (1990). Analysis of time series subject to changes in regime. Journal of Econometrics, 45(1-2), 39–70. https://doi.org/10.1016/0304-4076(90)90093-9.

Haq, S., & Larson, R. (2016). The dynamics of stock market returns and macroeconomic indicators: An ARDL approach with cointegration [Master of Science Thesis, Royal Insitute of Technology]. Stockholm. http://kth.diva-portal.org/smash/get/diva2:950080/FULLTEXT01.pdf.

Homolka, L., & Pavelková, D. (2018). Predictive Power of the ZEW Sentiment Indicator: Case of the German Automotive Industry. Acta Polytechnica Hungarica, 15(4), 161–178. http://doi.org /10.12700/APH.15.4.2018.4.9.

Huang, R. D., Masulis, R. W., & Stoll, H. R. (1996). Energy Shocks and Financial Markets. Journal of Futures Markets, 16(1), 1–27. https://doi.org/10.1002/(SICI)1096-9934(199602)16:1<1::AID -FUT1>3.0.CO;2-Q.

Huang, W., Nakamori, Y., & Wang, S.-Y. (2005). Forecasting stock market movement direction with support vector machine. Computers and Operations Research, 32(10), 2513–2522. https://doi.org/10.1016/j.cor.2004.03.016.

Hüfner, F. P., & Schröder, M. (2002). Forecasting Economic Activity in Germany – How Useful are Sentiment Indicators? (ZEW Discussion Paper No. 02-56). https://ftp.zew.de/pub/zew-docs /dp/dp0256.pdf.

Humpe, A., & Macmillan, P. (2007). Can macroeconomic variables explain long term stock market movements? A comparison of the US and Japan (CDMA Working Paper No. 07/20). http://dx.doi.org/10.2139/ssrn.1026219.

Jondeau, E., & Rockinger, M. (2006). The copula-GARCH model of conditional dependencies: an international stock market application. Journal of International Money and Finance, 25(5), 827– –853. https://doi.org/10.1016/j.jimonfin.2006.04.007.

Kilian, L., & Park, C. (2009). The impact of oil price shocks on the U.S. stock market. International Economic Review, 50(4), 1267–1287. https://doi.org/10.1111/j.1468-2354.2009.00568.x.

Kim, K. (2003). Dollar exchange rate and stock price: evidence from multivariate cointegration and error correction model. Review of Financial Economics, 12(3), 301–313. https://doi.org/10.1016 /S1058-3300(03)00026-0.

Kim, C.-J., Piger, J., & Startz, R. (2008). Estimation of Markov regime-switching regression models with endogenous switching. Journal of Econometrics, 143(2), 263–273. https://doi.org/10.1016 /j.jeconom.2007.10.002.

Kvietkauskiene, A., & Plakys, M. (2017). Impact indicators for stock markets return. Poslovna izvrsnosta promicanje kulturekvalitete i poslovne izvrsnosti, 11(2), 59–83. https://doi.org /10.22598/pi-be/2017.11.2.59.

Lischka, J. (2015). What follows what? Relations between economic indicators, economic expectations of the public, and news on the general economy and unemployment in Germany, 2002- 2011. Journalism and Mass Communication Quarterly, 92(2), 374–398. https://doi.org/10.1177 /1077699015574098.

Longin, F., & Solnik, B. (1995). Is the correlation in international equity returns constant: 1960– –1990?. Journal of International Money and Finance, 14(1), 3–26. https://doi.org/10.1016/0261 -5606(94)00001-H.

Mahmood, W. M., & Dinniah, N. M. (2009). Stock returns and macroeconomic variables: evidence from the six Asian-Pacific countries. International Research Journal of Finance and Economics, (30), 154–164.

Narayan, P. K., & Narayan, S. (2010). Modelling the impact of oil prices on Vietnama’s Stock Prices. Applied Energy, 87(1), 356–361. https://doi.org/10.1016/j.apenergy.2009.05.037.

Nasir, M. A., Shahbaz, M., Mai, T. T, & Shubita, M. (2020). Development of Vietnamese stock market: Influence of domestic macroeconomic environment and regional markets. International Journal of Finance and Economics, 26(1), 1435–1458. https://doi.org/10.1002 /ijfe.1857.

Nasseh, A., & Strauss, J. (2000). Stock prices and domestic and international macroeconomic activity: a cointegration approach. The Quarterly Review of Economics and Finance, 40(2), 229– –245. https://doi.org/10.1016/S1062-9769(99)00054-X.

Pilinkus, D. (2010). Macroeconomic indicators and their impact on stock market performance in the short and long run: the case of the Baltic States. Technological and Economic Development of Economy, 16(2), 291–304. https://doi.org/10.3846/tede.2010.19.

Ramchand, L., & Susmel, R. (1998). Volatility and cross correlation across major stock markets. Journal of Empirical Finance, 5(4), 397–416. https://doi.org/10.1016/S0927-5398(98)00003-6.

Rapach, D. E., Wohar, M. E., & Rangvid, J. (2005). Macro variables and international stock return predictability. International Journal of Forecasting, 21(1), 137–166. https://doi.org/10.1016 /j.ijforecast.2004.05.004.

Rodriguez, J. C. (2007). Measuring financial contagion: A Copula approach. Journal of Empirical Finance, 14(3), 401–423. https://doi.org/10.1016/j.jempfin.2006.07.002.

Siganos, A., Vagenas-Nanos, E., & Verwijmeren, P. (2014). Facebook’s daily sentiment and international stock markets. Journal of Economic Behavior and Organization, 107(B), 730–743. https://doi.org/10.1016/j.jebo.2014.06.004.

Toparli, E. A., Çatik, A. N., & Balcilar, M. (2019). The impact of oil prices on the stock returns in Turkey: A TVP-VAR approach. Physica A: Statistical Mechanics and its Applications, 535. https://doi.org/10.1016/j.physa.2019.122392.

White, H., & Domiwitz, I. (1984). Nonlinear regression with dependent observations. Econometrica, 52(1), 143–162. https://doi.org/10.2307/1911465.

Vuong, Q. H. (1989). Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica, 57(2), 307–333. https://doi.org/10.2307/1912557.