Data has become one of the most crucial sources of human life. In particular, the ability to predict the future through data is a widely studied topic. In finance, as an instance, increased volatility, fluctuations, low-frequency events, and rare events negatively affect the predictability of data, thus increasing the level of risk. As a consequence, the inability to make accurate predictions on future events increases the uncertainty and variability of a given scenario, indicating a consequent increase in risk. In this paper, we analyze data predictability introducing a new measure based on entropy and the wavelet transform. In particular, we show that the data are less predictable than one might expect due to the mentioned fluctuations and low-frequency events. Furthermore, we apply our tool to real data, in particular to time series of commodities. As a result, thanks to this new measure, we can observe that the price time series under analysis exhibit a significant level of unpredictability due to increased volatility, fluctuations, and the influence of low-frequency events.

Mastroeni, L., Mazzoccoli, A., Vellucci, P. (2024). Studying the impact of fluctuations, spikes and rare events in time series through a wavelet entropy predictability measure. PHYSICA. A, 641 [10.1016/j.physa.2024.129720].

Studying the impact of fluctuations, spikes and rare events in time series through a wavelet entropy predictability measure

Mastroeni, Loretta;Mazzoccoli, Alessandro
;
Vellucci, Pierluigi
2024-01-01

Abstract

Data has become one of the most crucial sources of human life. In particular, the ability to predict the future through data is a widely studied topic. In finance, as an instance, increased volatility, fluctuations, low-frequency events, and rare events negatively affect the predictability of data, thus increasing the level of risk. As a consequence, the inability to make accurate predictions on future events increases the uncertainty and variability of a given scenario, indicating a consequent increase in risk. In this paper, we analyze data predictability introducing a new measure based on entropy and the wavelet transform. In particular, we show that the data are less predictable than one might expect due to the mentioned fluctuations and low-frequency events. Furthermore, we apply our tool to real data, in particular to time series of commodities. As a result, thanks to this new measure, we can observe that the price time series under analysis exhibit a significant level of unpredictability due to increased volatility, fluctuations, and the influence of low-frequency events.
2024
Mastroeni, L., Mazzoccoli, A., Vellucci, P. (2024). Studying the impact of fluctuations, spikes and rare events in time series through a wavelet entropy predictability measure. PHYSICA. A, 641 [10.1016/j.physa.2024.129720].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/470511
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact