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Forecasting Value-at-Risk in turbulent stock markets via the local regularity of the price process

Author

Listed:
  • Massimiliano Frezza

    (University of Cassino and Southern Lazio)

  • Sergio Bianchi

    (Sapienza University of Rome
    New York University)

  • Augusto Pianese

    (University of Cassino and Southern Lazio)

Abstract

A new computational approach based on the pointwise regularity exponent of the price time series is proposed to estimate Value at Risk. The forecasts obtained are compared with those of two largely used methodologies: the variance-covariance method and the exponentially weighted moving average method. Our findings show that in two very turbulent periods of financial markets the forecasts obtained using our algorithm decidedly outperform the two benchmarks, providing more accurate estimates in terms of both unconditional coverage and independence and magnitude of losses.

Suggested Citation

  • Massimiliano Frezza & Sergio Bianchi & Augusto Pianese, 2022. "Forecasting Value-at-Risk in turbulent stock markets via the local regularity of the price process," Computational Management Science, Springer, vol. 19(1), pages 99-132, January.
    • Handle: RePEc:spr:comgts:v:19:y:2022:i:1:d:10.1007_s10287-021-00412-w
    DOI: 10.1007/s10287-021-00412-w
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