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Analysing and Optimising Bank Real Estate Portfolio by Using Impulse Response Function, Mahalanobis Distance and Financial Turbulence

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DOI: 10.4236/ojbm.2015.33032    2,048 Downloads   2,523 Views  
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ABSTRACT

This paper analyses one of the main factors that cause financial crisis and that are real estate portfolio management in banks. VAR and SVAR models were introduced and impulse response functions were obtained. The aforementioned function demonstrated how residential prices reacted to shock. Afterwards, financial turbulence index based on Mahalanobis distance and correlation between real estate prices in Austria, Germany and Switzerland was calculated and its relation to stock prices in EURO area. Financial turbulence demonstrated the lagging effect of financial crisis originating from USA. Data were taken from St. Louis FED database. Having calculated correlations, portfolio was created consisting of REITs, ETFs and stocks. It was optimised and efficient frontiers were found for different portfolio weightings. It was proved that the best way to optimise real estate portfolio was to invest in Swiss real estate as prices were growing and to hedge with Austrian real estate or some variations of ETFs.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Vukovic, O. (2015) Analysing and Optimising Bank Real Estate Portfolio by Using Impulse Response Function, Mahalanobis Distance and Financial Turbulence. Open Journal of Business and Management, 3, 326-344. doi: 10.4236/ojbm.2015.33032.

References

[1] Pfaff, B. (2008) VAR, SVAR and SVEC Models: Implementation within R Package vars.
[2] Hamilton, J.D. (1994) Time Series Analysis. Princeton University Press, Princeton, 293.
[3] Hamilton, J.D. (1994) Difference Equations. Time Series Analysis. Princeton University Press, Princeton, 5.
[4] Williams, D. (1991) Probability with Martingales. Cambridge University Press, Cambridge.
http://dx.doi.org/10.1017/CBO9780511813658
[5] Dung, N.T. and Thao, T.H. (2010) An Approximate Approach to Fractional Stochastic Integration and Its Application.
[6] Howitt, P. and Clower, R. (2000) The Emergence of Economic Organization. Journal of Economic Behavior & Organization, 41, 55-84. http://dx.doi.org/10.1016/S0167-2681(99)00087-6
[7] Sanderson, R. (2013) Does Monetary Policy Cause Randomness or Chaos? A Case Study from the European Central Bank. A Case Study from the European Central Bank. Banks and Bank Systems, 8, 55-61.
[8] Priestley, M.B. (1981) Spectral Analysis and Time Series. Academic Press, Waltham.
[9] Lütkepohl, H. (2008) Impulse Response Function. The New Palgrave Dictionary of Economics. 2nd Edition. http://dx.doi.org/10.1057/9780230226203.0767
[10] Stöckl, S. and Hanke, M. (2013) Financial Applications of the Mahalanobis Distance.
[11] Gonçalves, C.P. (2012) Financial Turbulence, Business Cycles and Intrinsic Time in an Artificial Economy. Algorithmic Finance, 1, 141-156. http://dx.doi.org/10.2139/ssrn.2002698
[12] Hacker, R.S. and Hatemi, J.A. (2008) Optimal Lag-Length Choice in Stable and Unstable VAR Models under Situations of Homoscedasticity and ARCH. Journal of Applied Statistics, 35, 601-615.
http://dx.doi.org/10.1080/02664760801920473
[13] Rebonato, R. (N.D.) Theory and Practice of Model Risk Management.
[14] St. Louis FED Website. https://www.stlouisfed.org

  
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