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Drawdown-based risk indicators for high-frequency financial volumes

Author

Listed:
  • Guglielmo D’Amico

    (Gabriele D’Annunzio University of Chieti-Pescara)

  • Bice Di Basilio

    (Gabriele D’Annunzio University of Chieti-Pescara)

  • Filippo Petroni

    (Gabriele D’Annunzio University of Chieti-Pescara)

Abstract

In stock markets, trading volumes serve as a crucial variable, acting as a measure for a security’s liquidity level. To evaluate liquidity risk exposure, we examine the process of volume drawdown and measures of crash-recovery within fluctuating time frames. These moving time windows shield our financial indicators from being affected by the massive transaction volume, a characteristic of the opening and closing of stock markets. The empirical study is conducted on the high-frequency financial volumes of Tesla, Netflix, and Apple, spanning from April to September 2022. First, we model the financial volume time series for each stock using a semi-Markov model, known as the weighted-indexed semi-Markov chain (WISMC) model. Second, we calculate both real and synthetic drawdown-based risk indicators for comparison purposes. The findings reveal that our risk measures possess statistically different distributions, contingent on the selected time windows. On a global scale, for all assets, financial risk indicators calculated on data derived from the WISMC model closely align with the real ones in terms of Kullback–Leibler divergence.

Suggested Citation

  • Guglielmo D’Amico & Bice Di Basilio & Filippo Petroni, 2024. "Drawdown-based risk indicators for high-frequency financial volumes," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-40, December.
    • Handle: RePEc:spr:fininn:v:10:y:2024:i:1:d:10.1186_s40854-023-00593-0
    DOI: 10.1186/s40854-023-00593-0
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