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The versatility of spectrum analysis for forecasting financial time series

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  • Pierre Rostan
  • Alexandra Rostan

Abstract

The versatility of the one†dimensional discrete wavelet analysis combined with wavelet and Burg extensions for forecasting financial times series with distinctive properties is illustrated with market data. Any time series of financial assets may be decomposed into simpler signals called approximations and details in the framework of the one†dimensional discrete wavelet analysis. The simplified signals are recomposed after extension. The final output is the forecasted time series which is compared to observed data. Results show the pertinence of adding spectrum analysis to the battery of tools used by econometricians and quantitative analysts for the forecast of economic or financial time series.

Suggested Citation

  • Pierre Rostan & Alexandra Rostan, 2018. "The versatility of spectrum analysis for forecasting financial time series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(3), pages 327-339, April.
    • Handle: RePEc:wly:jforec:v:37:y:2018:i:3:p:327-339
    DOI: 10.1002/for.2504
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    Cited by:

    1. Pierre Rostan & Alexandra Rostan, 2023. "The benefit of the Covid‐19 pandemic on global temperature projections," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2079-2098, December.
    2. Flavio Barboza & Geraldo Nunes Silva & José Augusto Fiorucci, 2023. "A review of artificial intelligence quality in forecasting asset prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1708-1728, November.
    3. Pierre Rostan & Alexandra Rostan, 2024. "How Australia's economy gained momentum because of Covid‐19," Australian Economic Papers, Wiley Blackwell, vol. 63(1), pages 36-58, March.
    4. Pierre Rostan & Alexandra Rostan & John Wall, 2024. "Measuring the Resilience to the Covid-19 Pandemic of Eurozone Economies with Their 2050 Forecasts," Computational Economics, Springer;Society for Computational Economics, vol. 63(3), pages 1137-1157, March.
    5. Kim C. Raath & Katherine B. Ensor, 2023. "Wavelet-L2E Stochastic Volatility Models: an Application to the Water-Energy Nexus," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 150-176, May.
    6. Joanna Bruzda, 2020. "The wavelet scaling approach to forecasting: Verification on a large set of Noisy data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 353-367, April.
    7. Montagnon, C.E., 2021. "Forecasting by splitting a time series using Singular Value Decomposition then using both ARMA and a Fokker Planck equation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).

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