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Estimating The Return Of The Financial Titles Of The Companies From The Manufacturing Industry, Listed On The Bucharest Stock Exchange

Author

Listed:
  • BALTES Nicolae

    (Lucian Blaga University of Sibiu)

  • DRAGOE Alexandra-Gabriela-Maria

    (Lucian Blaga University of Sibiu)

Abstract

The paper presents a method of estimating the maximum expected loss of value that can be recorded by holding a portfolio of financial titles for a certain period of time, through the VaR model, using Monte Carlo simulation method. The research was based on the closing prices of 33 companies from the manufacturing industry in Romania, listed on the Bucharest Stock Exchange. The results of the paper showed that, for a portfolio composed of the financial titles of the 33 studied companies, in which it will be invested an amount of 1.000.000 lei, the maximum daily loss, estimated during the studied period of 01.01.2016-31.12.2016, was 2.513,47 lei.

Suggested Citation

  • BALTES Nicolae & DRAGOE Alexandra-Gabriela-Maria, 2017. "Estimating The Return Of The Financial Titles Of The Companies From The Manufacturing Industry, Listed On The Bucharest Stock Exchange," Revista Economica, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 69(3), pages 19-28, August.
    • Handle: RePEc:blg:reveco:v:69:y:2017:i:3:p:19-28
    as

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    File URL: http://economice.ulbsibiu.ro/revista.economica/archive/69302baltes&dragoe.pdf
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    References listed on IDEAS

    as
    1. Engle, Robert F. & Manganelli, Simone, 2001. "Value at risk models in finance," Working Paper Series 75, European Central Bank.
    2. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2013. "Financial Risk Measurement for Financial Risk Management," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 1127-1220, Elsevier.
    3. David E. Allen & Robert Powell, 2009. "Transitional credit modelling and its relationship to market value at risk: an Australian sectoral perspective," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 49(3), pages 425-444, September.
    4. Francis X. Diebold (ed.), 2012. "Financial Risk Measurement and Management," Books, Edward Elgar Publishing, number 14102.
    5. Miguel A. Ferreira, 2005. "Evaluating Interest Rate Covariance Models Within a Value-at-Risk Framework," Journal of Financial Econometrics, Oxford University Press, vol. 3(1), pages 126-168.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    financial titles; Value at Risk (VaR); Monte Carlo method; closing price; market risk;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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