Swiss Journal of Economics and Statistics

, Volume 150, Issue 3, pp 161–190 | Cite as

Stock Market’s Reactions to Revelation of Tax Evasion: An Empirical Assessment

Open Access


Additionally to the financial crisis causing a world recession, Liechtenstein’s financial sector has been challenged by the so-called “Zumwinkel-Affair”, when a whistle-blower sold data of hundreds of tax evaders to international tax authorities. This paper investigates the impact of this affair on the daily stock prices of banks from Liechtenstein. An unconventional augmented GARCH-model (labelled as “augmented amalGARCH”), which outperforms conventional models, is introduced and dynamically analyses various influences on risk and returns. Also, an event study framework is applied. The main finding beyond further conclusions is that the Zumwinkel-Affair had an (accumulating) effect on risk, but surprisingly no impact on average stock return could be detected.


Tax Evasion Liechtenstein Zumwinkel Stock Price Volatility Augmented GARCH Event Study 


C01 C22 G01 G14 G21 


  1. Akaike, Hirotugu (1974), “A New Look at the Statistical Model Identification”, IEEE Transactions on Automatic Control, 19(6), pp. 716–723.CrossRefGoogle Scholar
  2. Asteriou, Dimitrios, and Simon Price (2001), “Political Instability and Economic Growth: UK Time Series Evidence”, Scottish Journal of Political Economy, 48(4), pp. 383–399.CrossRefGoogle Scholar
  3. Bollerslev, Tim (1986), “Generalized Autoregressive Conditional Heteroskedasticity”, Journal of Econometrics, 31(3), pp. 307–327.CrossRefGoogle Scholar
  4. Bollerslev, Tim (1987), “A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return,” Review of Economics and Statistics, 69, pp. 542–547.CrossRefGoogle Scholar
  5. Brunhart, Andreas (2012), “Stock Market’s Reactions to Revelation of Tax Evasion: An Empirical Assessment”, KOFL Working Papers No. 9, Konjunkturforschungsstelle Liechtenstein.Google Scholar
  6. Burren, Daniel, and Klaus Neusser (2010) “The Decline in Volatility of US GDP Growth”, Applied Economics Letters, 17, pp. 1625–1631.CrossRefGoogle Scholar
  7. Burnham, Kenneth P., and David R. Anderson (2004), “Multimodel Inference: Understanding AIC and BIC in Model Selection”, Sociological Methods & Research, 33(2), pp. 261–304.CrossRefGoogle Scholar
  8. Dickey, David, and Wayne A. Fuller (1979), “Distribution of the Estimators for Autoregressive Time Series with a Unit Root”, Journal of the American Statistical Association, 74, pp. 427–431.Google Scholar
  9. Elyasiani, Elyas, Iqbal Mansur, and Babatunde Odusami (2011), “Oil Price Shocks and Industry Stock Returns”, Energy Economics, (33), pp. 966–974.CrossRefGoogle Scholar
  10. Engle, Robert F. (1982), “Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of U. K. Inflation”, Econometrica, 50(4), pp. 987–1008.CrossRefGoogle Scholar
  11. Engle, Robert F., David M. Lilien, and Russel P. Robins (1987), “Estimating Time Varying Risk Premia in the Term Structure: The ARCH-M Model”, Econometrica, 55(2), pp. 391–407.CrossRefGoogle Scholar
  12. Fama, Eugene F., Lawrence Fisher, michael C. Jensen, and richard Roll (1969), “The Adjustment of Stock Prices to New Information”, International Economic Review, (10), pp. 1–21.CrossRefGoogle Scholar
  13. Glosten, Lawrence R., Ravi Jagannathan, and David E. Runkle (1993), “On the Relation between the Expected Value and Volatility of the Nominal Excess Return on Stocks”, The Journal of Finance, 48(5), pp. 1779–1801.CrossRefGoogle Scholar
  14. Hammoudeh, Shawkat, and Yuan Yuan (2008), “Metal Volatility in Presence of Oil and Interest Rate Shocks”, Energy Economics, (30), pp. 606–620.CrossRefGoogle Scholar
  15. Hauser, Michael and Robert M. Kunst (1993), “Fractionally Integrated Models With ARCH Errors”, Forschungsbericht No. 322, Institute for Advanced Studies.Google Scholar
  16. Hauser, Michael, and Robert M. Kunst (1998), “Fractionally Integrated Models With ARCH Errors: With an Application to the Swiss 1-Month Euromarket Interest Rate”, Review of Quantitative Finance and Accounting, 10, pp. 95–113.CrossRefGoogle Scholar
  17. Kollias, Christos, Stephanos Papadamou, and Costas Siriopoulos (2012), “Terrorism Induced Cross-Market Transmission of Shocks: A Case Study Using Intraday Data”, Economics of Security Working Paper No. 66, EUSECON.Google Scholar
  18. Kothari, S. P., and Jerold B. Warner (2007), “Econometrics of Event Studies”, in Handbook of Corporate Finance, Espen Eckbo, ed., Vol. 1, pp. 3–36, North Holland: Elsevier.CrossRefGoogle Scholar
  19. Kunst, Robert M. (1997), “Augmented ARCH Models for Financial Time Series: Stability Conditions and Empirical Evidence”, Applied Financial Economics, 7(6), pp. 575–586.CrossRefGoogle Scholar
  20. Kunst, Robert M. (2003), “Testing for Relative Predictive Accuracy: A Critical Viewpoint”, Working Paper, University of Vienna.Google Scholar
  21. Kwiatkowski, Denis, Peter C. B. Phillips, Peter Schmidt, and Yongcheol Shin (1992), “Testing the Null Hypothesis of Stationarity Against the Alternative of a Unit Root: How Sure Are We that Economic Time Series Have a Unit Root?”, Journal of Econometrics, 54, pp. 159–178.CrossRefGoogle Scholar
  22. Ljung, Greta M., and George E. P. Box (1978), “On a Measure of Lack of Fit in Time Series Models”, Biometrika, 66(2), pp. 265–270.CrossRefGoogle Scholar
  23. MacKinlay, A. Craig (1997), “Event Studies in Economics and Finance”, Journal of Economic Literature, 35, pp. 13–39.Google Scholar
  24. Phillips, Peter C. B., and Pierre Perron (1988), “Testing for a Unit Root in Time Series Regression”, Biometrika, 75(2), pp. 335–346.CrossRefGoogle Scholar
  25. Pynnönen, Seppo (2005), “On Regression Based Event Study”, in Contributions to Accounting, Finance, and Management Science. Essays in Honor of Professor Timo Salmi, Erkkie K. Laitinen and Teija Latinen, eds., No. 143 of Acta Wasensia, pp. 327–354.Google Scholar
  26. Schwarz, Gideon (1978), “Estimating the Dimension of a Model”, Annals of Statistics, 6(2), pp. 461–464.CrossRefGoogle Scholar
  27. Stoica, Petre, Yngve Selén, and Jian Li (2004), “On Information Criteria and the Generalized Likelihood Ratio Test of Model Order Selection”, IEEE Signal Processing Letters, 11(10), pp. 794–797.CrossRefGoogle Scholar
  28. Taylor, Stephen J. (1986), Modelling Financial Time Series, Chichester: John Wiley & Sons.Google Scholar
  29. Tsay, Ruey S. (1987), “Conditional Heteroscedastic Time Series Models”, Journal of the American Statistical Association, 82(398), pp. 590–604.CrossRefGoogle Scholar
  30. Weiss, Andrew A. (1984), “ARMA Models with ARCH Errors”, Journal of Time Series Analysis, 5(2), pp. 129–143.CrossRefGoogle Scholar
  31. Weiss, Andrew A. (1986), “Asymptotic Theory for ARCH Models: Estimation and Testing”, Econometric Theory, 2(1), pp. 107–131.CrossRefGoogle Scholar
  32. Zakoian, Jean-Michel (1994), “Threshold Heteroskedastic Models”, Journal of Economic Dynamics and Control, 18(5), pp. 931–955.CrossRefGoogle Scholar

Copyright information

© Swiss Society of Economics and Statistics 2014

Authors and Affiliations

  1. 1.Liechtenstein InstituteGermany

Personalised recommendations