Value-at-Risk Forecasts Based on Decomposed Return Series: The Short Run Matters

Theo Berger ()
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Theo Berger: University of Bremen

A chapter in Operations Research Proceedings 2015, 2017, pp 503-509 from Springer

Abstract: Abstract We apply wavelet decomposition to decompose financial return series into a time frequency domain and assess the relevant frequencies for adequate daily Value-at-Risk (VaR) forecasts. Our results indicate that the frequencies that describe the short-run information of the underlying time series comprise the necessary information for daily VaR forecasts.

Keywords: Wavelet Decomposition; Return Series; Financial Time Series; Volatility Forecast; Maximum Overlap Discrete Wavelet Transformation (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:spr:oprchp:978-3-319-42902-1_68

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DOI: 10.1007/978-3-319-42902-1_68

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