Identifying the underlying components of high-frequency data : pure vs jump diffusion processes
Year of publication: |
2025
|
---|---|
Authors: | Hizmeri, Rodrigo ; Izzeldin, Marwan ; Urga, Giovanni |
Published in: |
Journal of empirical finance. - [Erscheinungsort nicht ermittelbar] : Elsevier Science, ISSN 0927-5398, ZDB-ID 1496810-1. - Vol. 81.2025, Art.-No. 101594, p. 1-20
|
Subject: | Brownian motion | Finite jumps | High-frequency data | Infinite jumps | Microstructure noise | Price staleness | Stochastischer Prozess | Stochastic process | Volatilität | Volatility | Börsenkurs | Share price | Marktmikrostruktur | Market microstructure | Zeitreihenanalyse | Time series analysis | Optionspreistheorie | Option pricing theory | Noise Trading | Noise trading | Nichtparametrisches Verfahren | Nonparametric statistics | Monte-Carlo-Simulation | Monte Carlo simulation |
-
Identifying the underlying components of high-frequency data : pure vs jump diffusion processes
Hizmeri, Rodrigo, (2025)
-
Testing for jumps based on high-frequency data : a method exploiting microstructure noise
Liu, Guangying, (2020)
-
Jump variation estimation with noisy high frequency financial data via wavelets
Zhang, Xin, (2016)
- More ...
-
Identifying the underlying components of high-frequency data : pure vs jump diffusion processes
Hizmeri, Rodrigo, (2025)
-
Forecasting the Realized Variance in the Presence of Intraday Periodicity
Dumitru, Ana-Maria, (2019)
-
The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility
Hizmeri, Rodrigo, (2019)
- More ...