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SEMIFAR Models, with Applications to Commodities, Exchange Rates and the Volatility of Stock Market Indices

Author

Listed:
  • Beran, Jan
  • Feng, Yuanhua
  • Franke, Günter
  • Hess, Dieter
  • Ocker, Dirk

Abstract

The distinction between stationarity, difference stationarity, deterministic trends as well as between short- and long-range dependence has a major impact on statistical conclusions, such as confidence intervals for population quantities or point and interval forecasts. In this paper, recent results on so-called SEMIFAR models introduced by Beran(1999) are summarized and their potential usefulness for economic time series analysis is illustrated by analyzing several commodities, exchange rates, the volatility of stock market indices and some simulated series. SEMIFAR models provide a unified approach that allows for simultaneous modelling of and distinction between deterministic trends, difference stationarity and stationarity with short- and long-range dependence. An iterative data-driven algorithm combines MLE and kernel estimation. Predictions combine stochastic prediction of the random part with functional extrapolation of the deterministic part.

Suggested Citation

  • Beran, Jan & Feng, Yuanhua & Franke, Günter & Hess, Dieter & Ocker, Dirk, 1999. "SEMIFAR Models, with Applications to Commodities, Exchange Rates and the Volatility of Stock Market Indices," CoFE Discussion Papers 99/18, University of Konstanz, Center of Finance and Econometrics (CoFE).
    • Handle: RePEc:zbw:cofedp:9918
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    References listed on IDEAS

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    Cited by:

    1. Beran, Jan & Feng, Yuanhua, 1999. "Local Polynomial Estimation with a FARIMA-GARCH Error Process," CoFE Discussion Papers 99/08, University of Konstanz, Center of Finance and Econometrics (CoFE).
    2. Beran, Jan & Ocker, Dirk, 1999. "Volatility of Stock Market Indices - An Analysis based on SEMIFAR Models," CoFE Discussion Papers 99/14, University of Konstanz, Center of Finance and Econometrics (CoFE).

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