EURO-Online login
- New to EURO? Create an account
- I forgot my username and/or my password.
- Help with cookies
(important for IE8 users)
883. Wavelet-Entropy Risk-Predictability Measure for financial time series
Invited abstract in session TA-63: Models for Financial Data and Risk Management, stream OR in Banking, Finance and Insurance: New Tools for Risk Management.
Tuesday, 8:30-10:00Room: S14 (building: 101)
Authors (first author is the speaker)
| 1. | Alessandro Mazzoccoli
|
| Economics, Roma TRE University | |
| 2. | Loretta Mastroeni
|
| Dept. of Economics, University of Roma TRE |
Abstract
Data has emerged as one of humanity's most critical resources. Specifically, the endeavor to forecast future events using data has garnered widespread attention. However, heightened volatility, infrequent occurrences, and rare events hinder data predictability, consequently elevating risk levels. Consequently, the inability to accurately predict future events exacerbates uncertainty and variability within a given scenario, signaling a subsequent rise in risk. In this paper, we examine data predictability by introducing a novel metric based on entropy and the wavelet transform. Notably, we demonstrate that data exhibit less predictability than anticipated due to the aforementioned fluctuations and low-frequency events. Moreover, we employ our methodology on real-world data, particularly focusing on commodity time series. Consequently, with this new metric, we ascertain a notable degree of unpredictability in the price time series under scrutiny, attributable to heightened volatility and the impact of low-frequency events.
Keywords
- Forecasting
- Risk Analysis and Management
- Financial Modelling
Status: accepted
Back to the list of papers