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Times series Factorial models with incertitute measures on ARMA processes and its application to final data

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  • Terraza Virginie

    ()
    (Luxembourg School of Finance, University of Luxembourg)

  • Toque Carole

    ()
    (Luxembourg School of Finance, University of Luxembourg)

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    Abstract

    In this paper, we propose a non-parametric structural approach in order to define new pertinent criterion in the selection process of time series. This approach combines a technical analysis of oscillators derived from Wilder (1978) and the Shannon (1948) theory of information, with factorial techniques of visualization. In identifying classes of times series, using reference graphic models and pertinent criteria to better select appropriate models, this structural approach must be a first process to forecast models on significant entropies. First, we apply this approach on simulated ARMA processes, to show significant groupings and oppositions explained by entropies, and to return some well known properties of autocorrelations functions. In the second one, we use the methodology to derive groups of funds based on their ratings. We observe that the Luxembourg funds are characterized by reductions of incertitude measured on Europerformance ratings against the French funds which are characterized by reductions of incertitude on Morningstar ratings, according their performance with incertitude reductions measured on daily returns.

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    Bibliographic Info

    Paper provided by Luxembourg School of Finance, University of Luxembourg in its series LSF Research Working Paper Series with number 08-07.

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    Date of creation: 2008
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    Handle: RePEc:crf:wpaper:08-07

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    Keywords: fund’s rating; performance; factor analysis; incertitude measures;

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    1. Carhart, Mark M, 1997. " On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    2. Drost, Feike C & Nijman, Theo E, 1993. "Temporal Aggregation of GARCH Processes," Econometrica, Econometric Society, vol. 61(4), pages 909-27, July.
    3. Shiller, Robert J. & Perron, Pierre, 1985. "Testing the random walk hypothesis : Power versus frequency of observation," Economics Letters, Elsevier, vol. 18(4), pages 381-386.
    4. Granger, C.W.J. & Siklos, P.L., 1993. "Systematic Sampling, Temporal Aggregation, Seasonal Adjustment, and Cointegration: Theory and Evidence," Working Papers 93001, Wilfrid Laurier University, Department of Economics.
    5. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    6. Steven Cook, 2001. "Temporal aggregation and time deformation," Applied Economics Letters, Taylor & Francis Journals, vol. 8(6), pages 363-365.
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