(untitled) (Research, Society and Development, 2022)
The presence of jumps has an important impact on forecasting volatility of financial assets. These jumps can be understood as a large local structural changes in the price series and are often associated at a behavioral issue of investors, usually caused by macroeconomics news announcements. The wavelets approach can be used in these situations once it detects jumps locations efficiently. However, it is common that this detection to be performed only at the finest level since this is where the noises are expected to be located. In this context, this work explored the presence of jumps in the different levels of decomposition of the studied time serie, to determine the estimation of the variation due to jumps in the variability of the price process. For this, an analysis was carried out from the series of log-prices of PETROBRAS shares (PETR4), at a frequency of 1 minute, in a period with a strong fall evidenced by an intervention in the presidency of the state-owned company. The methodology used showed that, particularly for this mentioned price drop, the variability due to jumps is impacted in a way that its estimate more than triples when also considering the low frequency levels, corresponding to investment horizons ranging from minutes to 1 to 2 hours of trading, which also highlights the length of time the news effect takes to dilute in the stock market.