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

Beran, Jan; Feng, Yuanhua; Franke, Günter

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

Dateien

413_1.pdfGröße: 447.14 KBDownloads: 99

Datum

1999

Open Access-Veröffentlichung

Open Access Green

Sammlungen

Mathematik und Statistik

Publikationstyp

Working Paper/Technical Report

Publikationsstatus

Published

Zusammenfassung

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.

Fachgebiet (DDC)

510 Mathematik

Schlagwörter

SEMIFAR models, kernel estimation, fractional ARIMA, long-range dependence, trend

Zitieren

ISO 690

BERAN, Jan, Yuanhua FENG, Günter FRANKE, 1999. SEMIFAR Models with Applications to Commodities, Exchange Rates and the Volatility of Stock Market Indices

BibTex

@techreport{Beran1999SEMIF-540,
  year={1999},
  series={CoFE-Diskussionspapiere / Zentrum für Finanzen und Ökonometrie},
  title={SEMIFAR Models with Applications to Commodities, Exchange Rates and the Volatility of Stock Market Indices},
  number={1999/18},
  author={Beran, Jan and Feng, Yuanhua and Franke, Günter}
}

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