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Ajustarea seriilor de timp financiare,Partea întâi
[Smoothing of financial time series, Part 1]

Author

Listed:
  • Stefanescu, Răzvan
  • Dumitriu, Ramona

Abstract

The financial time series smoothing could facilitate the identification of some important characteristics such as the trend, the cyclic or the seasonal pattern. It could be also useful in forecasting the evolutions of some financial variables. In this paper we approach some smoothing techniques, such as the simple or the centered moving average.

Suggested Citation

  • Stefanescu, Răzvan & Dumitriu, Ramona, 2017. "Ajustarea seriilor de timp financiare,Partea întâi [Smoothing of financial time series, Part 1]," MPRA Paper 78329, University Library of Munich, Germany, revised 15 Apr 2017.
  • Handle: RePEc:pra:mprapa:78329
    as

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    References listed on IDEAS

    as
    1. Aadland, David, 2005. "Detrending time-aggregated data," Economics Letters, Elsevier, vol. 89(3), pages 287-293, December.
    2. Campbell, John Y. & Hentschel, Ludger, 1992. "No news is good news *1: An asymmetric model of changing volatility in stock returns," Journal of Financial Economics, Elsevier, vol. 31(3), pages 281-318, June.
    3. Francis X. Diebold & Jose A. Lopez, 1995. "Forecast evaluation and combination," Research Paper 9525, Federal Reserve Bank of New York.
    4. Watson, Mark W., 1986. "Univariate detrending methods with stochastic trends," Journal of Monetary Economics, Elsevier, vol. 18(1), pages 49-75, July.
    5. Nelson, Charles R. & Plosser, Charles I., 1982. "Trends and random walks in macroeconmic time series : Some evidence and implications," Journal of Monetary Economics, Elsevier, vol. 10(2), pages 139-162.
    6. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    7. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    8. Stefanescu, Răzvan & Dumitriu, Ramona, 2007. "Bazele Statisticii [Basic Statistics]," MPRA Paper 53048, University Library of Munich, Germany, revised 09 Sep 2007.
    9. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    10. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
    11. Stefanescu, Razvan & Dumitriu, Ramona, 2015. "Conţinutul analizei seriilor de timp financiare [The Essentials of the Analysis of Financial Time Series]," MPRA Paper 67175, University Library of Munich, Germany.
    12. Stefanescu, Răzvan & Dumitriu, Ramona, 2016. "Statistica descriptivă a seriilor de timp financiare [Descriptive statistics of the financial time series]," MPRA Paper 72268, University Library of Munich, Germany.
    13. Fildes, Robert, 1992. "The evaluation of extrapolative forecasting methods," International Journal of Forecasting, Elsevier, vol. 8(1), pages 81-98, June.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Financial Time Series; Smoothing; Forecasting;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G00 - Financial Economics - - General - - - General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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