Finanse, Rynki Finansowe, Ubezpieczenia

Previously: Zeszyty Naukowe Uniwersytetu Szczecińskiego. Finanse, Rynki Finansowe, Ubezpieczenia


ISSN: 2450-7741    OAI    DOI: 10.18276/frfu.2018.94/2-10
CC BY-SA   Open Access 

Issue archive / 4/2018
Analiza porównawcza kształtowania się indeksów akcji na świecie po kryzysie finansowym
(Comparative analysis of the shaping of share indexes in the world after the financial crisis)

Authors: Ryszard Węgrzyn
Uniwersytet Ekonomiczny w Krakowie
Keywords: stock market index volatility GARCH model
Data publikacji całości:2018
Page range:17 (125-141)
Klasyfikacja JEL: G15
Cited-by (Crossref) ?:

Abstract

Purpose – After the bear market associated with the recent financial crisis, it was possible to observe the reversal of trends and the beginning of the long-term bull market in the first months of 2009. The purpose of the article is to conduct and present a comparative analysis of the rates of return and risk (volatility of the rates of return) of the selected stock exchange indices in this post-crisis period, in particular to indicate differences in share indices from different geographic regions and countries. Design/methodology/approach – The stock indexes have been analyzed in terms of long-term changes, basic statistics of the rates of return and differences in their volatility using GARCH models. In the final part, the expected rates of return and the risk of indices were compared. A total of 15 indexes were analyzed in detail. Findings – On the basis of the conducted analysis, the assessment of selected stock exchange indices was made and the differences in their rates of return and risk were noted. Originality/value – The research results give the opportunity to look more broadly at the diverse situation of the capital market after the financial crisis and can be used by investors investing financial resources, for example, in index funds.
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Bibliography

1.Akgiray, V. (1989). Conditional Heteroscedasticity in Time Series of Stock Returns: Evidence and Forecasts. Journal of Business, 62, 55–80.
2.Bauwens, L., Hafner, Ch.M., Laurent, S. (2012). Handbook of Volatility Models and Their Applications. Hoboken: J. Wiley & Sons.
3.Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroscedasticity. Journal of Econometrics, 31, 307–327.
4.Cumby, R., Figlewski, S., Hasbrouck, J. (1993). Forecasting Volatilities and Correlations with EGARCH Models. Journal of Derivatives, 1, 51–63.
5.Doman, M., Doman, R. (2009). Modelowanie zmienności i ryzyka. Metody ekonometrii finansowej. Kraków: Wolters Kluwer.
6.Engle, R. (2001). GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics. The Journal of Economic Perspectives”, 4 (15), 157–168.
7.Engle, R.F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of Variance of UK Inflation. Econometrica, 50, 987–1007.
8.Erginbay, U., Eleftherios, T., Yusuf, M. (2014). Modeling Volatility in the Stock Markets using GARCH Models: European Emerging Economies and Turkey. International Journal in Economics and Business Administration, 2 (3), 72–87.
9.Farhan, A., Samia, M.U., Raza, A. (2017). Modelling Stock Indexes Volatility of Emerging Markets. Pobrano z: https://www.researchgate.net/publication/319954309.
10.Figlewski, S. (1997). Forecasting Volatility. Oxford, UK: Blackwell.
11.Fiszeder, P. (2009). Modele klasy GARCH w empirycznych badaniach finansowych. Toruń: Wydawnictwo Naukowe Uniwersytetu Mikołaja Kopernika.
12.Fiszeder, P., Orzeszko, W. (2012). Nonparametric Verification of GARCH-Class Models for Selected Polish Exchange Rates and Stock Indices. Czech Journal of Economics and Finance, 5 (62), 430–449.
13.Hassan, S.A. (2017). A Time Series Analysis of Major Indexes Using GARCH Model with Regime Shifts. International Journal of Financial Research, 4 (8), 127–133.
14.Heynen, R.C, Kat, H.M. (1994). Volatility Prediction: a Comparison of Stochastic Volatility, GARCH(1,1) and EGARCH(1,1) Models. Journal of Derivatives, 2 (2), 50–65.
15.Nawrot, W. (2009). Globalny kryzys finansowy XXI wieku. Przyczyny, przebieg, skutki, prognozy. Warszawa: CeDeWu.
16.Ng, H.G., McAleer, M.J. (2004). Recursive Modelling of Symmetric and Asymmetric Volatility in the Presence of Extreme Observations. International Journal of Forecasting, 1 (20), 115–129.
17.Osiewalski, J., Pajor, A., Pipień, M. (2004). Bayesowskie modelowanie i prognozowanie indeksu WIG z wykorzystaniem procesów GARCH i SV. W: A. Zeliaś (red.), XX Seminarium Ekonometryczne im. Prof. Zbigniewa Pawłowskiego (s. 17–39). Kraków: Akademia Ekonomiczna w Krakowie.
18.Osińska, M. (2006). Ekonometria finansowa. Warszawa: PWE.
19.Pagan, A.R., Schwert, G.W. (1990). Alternative Models for Conditional Stock Volatility. Journal of Econometrics, 45, 267–290.
20.Stawicki, J. (2004). Wykorzystanie łańcuchów Markowa w analizie rynku kapitałowego. Toruń: Uniwersytet Mikołaja Kopernika.
21.Taylor, S.J. (1986). Modelling Financial Time Series. Chichester: Wiley.
22.Węgrzyn, R. (2010). The Global Financial Crisis versus the Shaping of Stock Market Indices. W: R. Borowiecki, T. Rojek (red.), Challenges for the Contemporary Enterprise under Globalization (s. 23–32). Cracow: Cracow University of Economics.
23.Węgrzyn, R. (2013). Zastosowanie wybranych modeli zmienności w analizie ryzyka cen akcji. Zeszyty Naukowe Uniwersytetu Szczecińskiego, 760. Finanse, Rynki Finansowe, Ubezpieczenia, 60, 551–562.
24.Wojtyna, A. (red.) (2009). Dostosowania makroekonomiczne i mikroekonomiczne w krajach na średnim poziomie rozwoju po kryzysach finansowych. Warszawa: PWE.