Skip to main content
SpringerLink
Log in

Dynamic long-range dependences in the Swiss stock market

  • Published:
Empirical Economics Aims and scope Submit manuscript

Abstract

Although the analysis of dependence in financial markets started a century ago, there is still room for new work, both because statistical methods continue to de developed, allowing stronger and more robust analysis, and because more and more data is available. In this context, we propose to make a deep analysis of the Swiss stock market, one of the most important financial centres in the world, studying the main index and also 19 of its 20 components. We use detrended fluctuation analysis, which allows us to analyse the existence of long-term dependence in a given variable. As our objective is to analyse the evolution of that dependence over time, we use a sliding windows approach. The results show that several of the analysed stocks have a behaviour which is not consistent with the absence of dependence, which could be informative for actual and potential investors.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Notes

  1. https://www.six-swiss-exchange.com/issuers/marketplace/financialcenteren.html.

  2. It is usual, in this kind of analysis, to show the behaviour of the variation of stocks and returns. However, because this study has many indices, and due to space constraints, they are not shown, but are available upon request.

  3. See the news in https://www.theguardian.com/business/2002/oct/23/12.

References

  • Anagnostidis P, Varsakelis C, Emmanouilides C (2016) Has the 2008 financial crisis affected stock market efficiency? The case of Eurozone. Phys A 447:116–128

    Article  Google Scholar 

  • Ausloos M (2000) Statistical physics in foreign exchange currency and stock markets. Phys A 285:48–65

    Article  Google Scholar 

  • Ausloos M, Vandewalle N, Boveroux P, Minguet A, Ivanova K (1999) Applications of statistical physics to economic and financial topics. Phys A 274:229–240

    Article  Google Scholar 

  • Ausloos M, Jovanovic F, Schinckus C (2016) On the “usual” misunderstandings between econophysics and finance: some clarifications on modelling approaches and efficient market hypothesis. Int Rev Financial Anal 47:7–14

    Article  Google Scholar 

  • Bachelier L (1900) Théorie de la spéculation. Annales Scientifiques de l’École Normale Supérieure 3(17):21–86

    Article  Google Scholar 

  • Barkoulas J, Baum C (1996) Long-term dependence in stock returns. Econ Lett 53:253–259

    Article  Google Scholar 

  • Bollerslev T, Jubinski D (1999) Equity trading volume and volatility: latent information arrivals and common long-run dependencies. J Bus Econ Stat 17:9–21

    Google Scholar 

  • Breidt F, Crato N, Lima P (1993) Modeling long memory stochastic volatility. Working Paper of John Hopkins University, Baltimore

  • Cajueiro D, Tabak B (2004a) The Hurst exponent over time: testing the assertion that emerging markets are becoming more efficient. Phys A 336(3–4):521–537

    Article  Google Scholar 

  • Cajueiro D, Tabak B (2004b) Evidence of long range dependence in Asian equity markets: the role of liquidity and market restrictions. Phys A 342(3–4):656–664

    Article  Google Scholar 

  • Cajueiro D, Tabak B (2005) Ranking efficiency for emerging markets II. Chaos Solitons Fractals 23:671–675

    Article  Google Scholar 

  • Cajueiro D, Tabak B (2006) Testing for predictability in equity returns for European transition markets. Econ Syst 30(1):56–78

    Article  Google Scholar 

  • Cajueiro D, Tabak B (2007) Are the crude oil markets becoming weakly efficient over time? A test for time-varying long-range dependence in prices and volatility. Energy Econ 29:28–36

    Article  Google Scholar 

  • Cajueiro D, Tabak B (2008a) Testing for long-range dependence in world stock markets. Chaos Solitons Fractals 37(3):918–927

    Article  Google Scholar 

  • Cajueiro D, Tabak B (2008b) Testing for time-varying long-range dependence in real estate equity returns. Chaos Solitons Fractals 38(1):293–307

    Article  Google Scholar 

  • Campbell J, Grossman S, Wang J (1993) Trading volume and serial correlation in stock returns. Q J Econ 108:905–939

    Article  Google Scholar 

  • Cao G, Zhang M (2015) Extreme values in the Chinese and American stock markets based on detrended fluctuation analysis. Phys A 436(15):25–35

    Article  Google Scholar 

  • Carbone A, Castelli G, Stanley H (2004) Time dependent Hurst exponent in financial time series. Phys A 344:267–271

    Article  Google Scholar 

  • Christodoulou-Volosa C, Siokis F (2006) Long range dependence in stock market returns. Appl Financial Econ 16:1331–1338

    Article  Google Scholar 

  • Clark P (1973) A subordinated stochastic process model with finite variance for speculative prices. Econometrica 41:135–156

    Article  Google Scholar 

  • Cont R (2001) Empirical properties of asset returns: stylized facts and statistical issues. Quant Finance I:223–236

    Article  Google Scholar 

  • Cont R, Wagalath L (2013) Institutional investors and the dependence structure of asset returns. Int J Theor Appl Finance 19(2):1–37

    Google Scholar 

  • Dajcman S (2012) Time-varying long-range dependence in stock market returns and financial market disruptions—a case of eight European countries. Appl Econ Lett 19:953–957

    Article  Google Scholar 

  • Di Matteo T, Aste T, Dacorogna M (2005) Long-term memories of developed and emerging markets: using the scaling analysis to characterize their stage of development. J Bank Finance 29(4):827–851

    Article  Google Scholar 

  • Ding Z, Granger C, Engle R (1993) A long memory property of stock market returns and a new model. J Empir Finance 1:83–106

    Article  Google Scholar 

  • Fama E (1963) Mandelbrot and the stable paretian hypothesis. J Bus 36(4):420–429

    Article  Google Scholar 

  • Fama E (1970) Efficient capital markets: a review of theory and empirical work. J Finance 25:383–417

    Article  Google Scholar 

  • Ferreira P (2016) Apple, alphabet or microsoft: which is the most efficient share? Econ Res Finance 1(2):67–79

    Google Scholar 

  • Ferreira P, Dionísio A (2014) Revisiting serial dependence in the stock markets of the G7 countries, Portugal, Spain and Greece. Appl Financial Econ 24(5):319–331

    Article  Google Scholar 

  • Ferreira P, Dionísio A (2016a) G7 Stock markets: who is the first to defeat The DCCA correlation? Rev Soc Econ Perspect 1(1):107–120

    Google Scholar 

  • Ferreira P, Dionísio A (2016b) How long is the memory of the US stock market? Phys A 451:502–506

    Article  Google Scholar 

  • Ferreira P, Loures L, Nunes J, Dionísio A (2017) The behaviour of share returns of football clubs: an econophysics approach. Phys A 472:136–144

    Article  Google Scholar 

  • Ferreira P, Dionísio A, Guedes E, Zebende G (2018) A sliding windows approach to analyse the evolution of bank shares in the European Union. Physica A 490:1355–1367

    Article  Google Scholar 

  • Gençay R, Gradojevic N (2010) Crash of ‘87—was it expected? Aggregate market fears and long-range dependence. J Empir Finance 17:270–282

    Article  Google Scholar 

  • Granger C, Morgenstern O (1964) Spectral analysis of New York Stock Market Prices. In: Cootner P (ed) The random character of stock Prices. MIT Press, Cambridge, originally published in 1963

  • Gvozdanovic I, Podobnik B, Wang D, Stanley E (2012) 1/f behavior in cross-correlations between absolute returns in a US market. Phys A 391:2860–2866

    Article  Google Scholar 

  • Herberger T, Kohlert D, Oehler A (2011) Momentum and industry-dependence: an analysis of the Swiss stock market. J Asset Manag 11(6):391–400

    Article  Google Scholar 

  • Hess M (2003a) What drives Markov regime-switching behavior of stock markets? The Swiss case. Int Rev Financial Anal 12:527–543

    Article  Google Scholar 

  • Hess M (2003b) Sector specific impacts of macroeconomic fundamentals on the Swiss stock market. Fin Mark Portf Manag 17(2):234–245

    Article  Google Scholar 

  • Isakov D (1999) Is beta still alive? Conclusive evidence from the Swiss stock market. Eur J Finance 5:202–212

    Article  Google Scholar 

  • Jochum C (1999) Volatility spillovers and the price of risk: evidence from the Swiss stock market. Empir Econ 24:303–322

    Article  Google Scholar 

  • Karanasos M, Kartsaklas A (2009) Dual long-memory, structural breaks and the link between turnover and the range-based volatility. J Empir Finance 16:838–851

    Article  Google Scholar 

  • Karpoff J (1987) The relation between price changes and trading volume: a survey. J Financial Quant Anal 22(1):109–126

    Article  Google Scholar 

  • Kendall M (1953) The analysis of economic time-series. J R Stat Soc 116:11–25

    Google Scholar 

  • Kristoufek L (2014) Measuring cross-correlation between non-stationary series with DCCA coefficient. Phys A 402:291–298

    Article  Google Scholar 

  • Kristoufek L (2015) Detrended fluctuation analysis as a regression framework: estimating dependence at different scales. Phys Rev E 91:022802

    Article  Google Scholar 

  • Kumar M, Thenmozhi M (2012) Causal effect of volume on stock returns and conditional volatility in developed and emerging market. Am J Finance Account 2(4):346–362

    Article  Google Scholar 

  • Liu Y, Cizeau P, Meyer M, Peng C, Stanley H (1997) Correlations in economic time series. Phys A 245(3–4):437–440

    Article  Google Scholar 

  • Lobato I, Velasco C (2000) Long memory in stock-market trading volume. J Bus Econ Stat 18:410–427

    Google Scholar 

  • López J, Contreras J (2013) Performance of multifractal detrended fluctuation analysis on short time series. Phys Rev E 87:022918

    Article  Google Scholar 

  • Mandelbrot B (1977) The fractal geometry of nature. Freeman and Company, New York

    Google Scholar 

  • Osborne M (1964) Brownian motion in the stock prices. In: Cootner P (ed) The random character of stock prices. MIT Press, Cambridge, originally published in 1959

  • Parisi D, Sornett D, Helbing D (2013) Financial price dynamics and pedestrian counterflows: a comparison of statistical stylized facts. Phys Rev E 87:012804

    Article  Google Scholar 

  • Peng C, Buldyrev S, Havlin S, Simons M, Stanley E, Goldberger A (1994) Mosaic organization of DNA nucleotides. Phys Rev E 49:1685–1689

    Article  Google Scholar 

  • Pereira E, Silva M, Pereira H (2017) Econophysics: past and present. Phys A 473:251–261

    Article  Google Scholar 

  • Podobnik B, Stanley H (2008) Detrended cross-correlation analysis: a new method for analyzing two nonstationary time series. Phys Rev Lett 100:084102

    Article  Google Scholar 

  • Podobnik B, Horvatic D, Petersen A, Stanley E (2009) Cross-correlations between volume change and price change. Proc Natl Acad Sci USA 106:22079–22084

    Article  Google Scholar 

  • Podobnik B, Jiang Z, Zhou W, Stanley H (2011) Statistical tests for power-law cross-correlated processes. Phys Rev E 84:066118

    Article  Google Scholar 

  • Qian X, Liu Y, Jiang Z, Podobnik B, Zhou W, Stanley E (2015) Detrended partial cross-correlation analysis of two time series influenced by common external forces. Phys Rev E 91:062816

    Article  Google Scholar 

  • Ranaldo A (2001) Intraday market liquidity on the Swiss stock exchange. Financial Mark Portf Manag 15(3):309–327

    Article  Google Scholar 

  • Ray B, Tsay R (2000) Long-range dependence in daily stock volatilities. J Bus Econ Stat 18(2):254–262

    Google Scholar 

  • Rey D, Schmid M (2007) Feasible momentum strategies: evidence from the Swiss stock market. Financial Mark Portf Manag 21:325–352

    Article  Google Scholar 

  • Sadique S, Silvapulle P (2001) Long-term memory in stock market returns: international evidence. Int J Financial Econ 6:59–67

    Article  Google Scholar 

  • Samuelson P (1965) Proof that properly anticipated prices fluctuate randomly. Ind Manag Rev 6:41–49

    Google Scholar 

  • Sensoy A, Tabak B (2015) Time-varying long term memory in the European Union stock markets. Phys A 436(15):147–158

    Article  Google Scholar 

  • Sewell M (2011) History of the efficient market hypothesis. Research Note No. RN/11/04. UCL Department of Computer Science, London

  • Sibbersten P (2004) Long memory in volatilities of German stock returns. Empir Econ 29:477–488

    Google Scholar 

  • Silva M, Pereira E, Filho A, Castro A, Miranda J, Zebende G (2015) Quantifying cross-correlation between Ibovespa and Brazilian blue-chips: the DCCA approach. Phys A 424:124–129

    Article  Google Scholar 

  • Ureche-Rangau L, Collado F, Galiay U (2011) The dynamics of the volatility—trading volume relationship: new evidence from developed and emerging markets. Econ Bull 31(3):2569–2583

    Google Scholar 

  • Vandewalle N, Ausloos M (1997) Coherent and random sequences in financial fluctuations. Phys A 246(3–4):454–459

    Article  Google Scholar 

  • Wang Y, Wei Y, Wu C (2011a) Analysis of the efficiency and multifractality of gold markets based on multifractal detrended fluctuation analysis. Phys A 390(5):817–827

    Article  Google Scholar 

  • Wang Y, Wei Y, Wu C (2011b) Detrended fluctuation analysis on spot and futures markets of West Texas Intermediate crude oil. Phys A 390(5):864–875

    Article  Google Scholar 

  • Zebende G (2011) DCCA cross-correlation coefficient: quantifying level of cross-correlation. Phys A 390(4):614–618

    Article  Google Scholar 

Download references

Acknowledgements

This work was funded by Fundação para a Ciência e a Tecnologia (Grant UID/ECO/04007/2013) and FEDER/COMPETE (POCI-01-0145-FEDER-007659).

Author information

Authors and Affiliations

  1. VALORIZA - Research Center for Endogenous Resource Valorization, Portalegre, Portugal

    Paulo Ferreira

  2. Instituto Politécnico de Portalegre, Portalegre, Portugal

    Paulo Ferreira

  3. CEFAGE-UE, IIFA, Universidade de Évora, Largo Dos Colegiais 2, 7000, Évora, Portugal

    Paulo Ferreira

Authors
  1. Paulo Ferreira
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paulo Ferreira.

Ethics declarations

Conflict of interest

The author declares that he has no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Appendix

Appendix

See Figs. 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 and 20.

Fig. 9
figure 9

Evolution of DFA exponents of volatility for SMI, ABB LTD, ADECCO, CREDIT SUISSE GROUP, GEBERIT, GIVAUDAN and LAFARGEHOLCIM, considering a window length w = 1000. In red, the error bar of the DFA exponents (coloured online)

Full size image
Fig. 10
figure 10

Evolution of DFA exponents of volatility for LONZA GROUP, NESTLE, NOVARTIS, RICHEMONT, ROCHE HOLDING, SGS and SIKA, considering a window length w = 1000. In red, the error bar of the DFA exponents (coloured online)

Full size image
Fig. 11
figure 11

Evolution of DFA exponents of volatility for THE SWATCH GROUP, SWISS LIFE HOLDING, SWISS RE, SWISSCOM, UBS GROUP and ZURICH INSURANCE GROUP, considering a window length w = 1000. In red, the error bar of the DFA exponents (coloured online)

Full size image
Fig. 12
figure 12

Test statistic for hypothesis H0: α = 0.5 versus H1: α ≠ 0.5, for the exponents of volatility for SMI, ABB LTD, ADECCO, CREDIT SUISSE GROUP, GEBERIT, GIVAUDAN and LAFARGEHOLCIM. Dashed lines represent critical values for a 5% level of significance

Full size image
Fig. 13
figure 13

Test statistic for hypothesis H0: α = 0.5 versus H1: α ≠ 0.5, for the exponents of volatility for LONZA GROUP, NESTLE, NOVARTIS, RICHEMONT, ROCHE HOLDING, SGS and SIKA. Dashed lines represent critical values for a 5% level of significance

Full size image
Fig. 14
figure 14

Test statistic for hypothesis H0: α = 0.5 versus H1: α ≠ 0.5, for the exponents of volatility for THE SWATCH GROUP, SWISS LIFE HOLDING, SWISS RE, SWISSCOM, UBS GROUP and ZURICH INSURANCE GROUP. Dashed lines represent critical values for a 5% level of significance

Full size image
Fig. 15
figure 15

Evolution of DFA exponents of trading volume for ABB LTD, ADECCO, CREDIT SUISSE GROUP, GEBERIT, GIVAUDAN and LAFARGEHOLCIM, considering a window length w = 1000. In red, the error bar of the DFA exponents (coloured on line)

Full size image
Fig. 16
figure 16

Evolution of DFA exponents of trading volume for LONZA GROUP, NESTLE, NOVARTIS, RICHEMONT, ROCHE HOLDING, SGS and SIKA, considering a window length w = 1000. In red, the error bar of the DFA exponents (coloured online)

Full size image
Fig. 17
figure 17

Evolution of DFA exponents of trading volume for THE SWATCH GROUP, SWISS LIFE HOLDING, SWISS RE, SWISSCOM, UBS GROUP and ZURICH INSURANCE GROUP, considering a window length w = 1000. In red, the error bar of the DFA exponents (coloured online)

Full size image
Fig. 18
figure 18

Test statistic for hypothesis H0: α = 0.5 versus H1: α ≠ 0.5, for the exponents of trading volume for ABB LTD, ADECCO, CREDIT SUISSE GROUP, GEBERIT, GIVAUDAN and LAFARGEHOLCIM. Dashed lines represent critical values for a 5% level of significance

Full size image
Fig. 19
figure 19

Test statistic for hypothesis H0: α = 0.5 versus H1: α ≠ 0.5, for the exponents of trading volume for LONZA GROUP, NESTLE, NOVARTIS, RICHEMONT, ROCHE HOLDING, SGS and SIKA. Dashed lines represent critical values for a 5% level of significance

Full size image
Fig. 20
figure 20

Test statistic for hypothesis H0: α = 0.5 versus H1: α ≠ 0.5, for the exponents of trading volume for THE SWATCH GROUP, SWISS LIFE HOLDING, SWISS RE, SWISSCOM, UBS GROUP and ZURICH INSURANCE GROUP. Dashed lines represent critical values for a 5% level of significance

Full size image

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ferreira, P. Dynamic long-range dependences in the Swiss stock market. Empir Econ 58, 1541–1573 (2020). https://doi.org/10.1007/s00181-018-1549-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00181-018-1549-x

Keywords

JEL Classification

Search

Navigation