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pro vyhledávání: '"SOULIER, Philippe"'
We develop and justify methodology to consistently test for long-horizon return predictability based on realized variance. To accomplish this, we propose a parametric transaction-level model for the continuous-time log price process based on a pure j
Externí odkaz:
http://arxiv.org/abs/2202.00793
Autor:
Soulier, Philippe
The goal of this paper is to investigate the tools of extreme value theory originally introduced for discrete time stationary stochastic processes (time series), namely the tail process and the tail measure, in the framework of continuous time stocha
Externí odkaz:
http://arxiv.org/abs/2004.00325
We study the extremes of branching random walks under the assumption that the underlying Galton-Watson tree has infinite progeny mean. It is assumed that the displacements are either regularly varying or they have lighter tails. In the regularly vary
Externí odkaz:
http://arxiv.org/abs/1909.08948
Publikováno v:
In Stochastic Processes and their Applications June 2023 160:120-160
We consider stationary time series $\{X_j, j \in Z\} whose finite dimensional distributions are regularly varying with extremal independence. We assume that for each $h \geq 1$, conditionally on $X_0$ to exceed a threshold tending to infinity, the co
Externí odkaz:
http://arxiv.org/abs/1804.10948
The goal of this paper is an exhaustive investigation of the link between the tail measure of a regularly varying time series and its spectral tail process, independently introduced in Owada and Samorodnitsky (2012) and Basrak and Segers (2009). Our
Externí odkaz:
http://arxiv.org/abs/1710.08358
Autor:
Planinić, Hrvoje, Soulier, Philippe
The tail measure of a regularly varying stationary time series has been recently introduced. It is used in this contribution to reconsider certain properties of the tail process and establish new ones. A new formulation of the time change formula is
Externí odkaz:
http://arxiv.org/abs/1706.04767
We prove a sequence of limiting results about weakly dependent stationary and regularly varying stochastic processes in discrete time. After deducing the limiting distribution for individual clusters of extremes, we present a new type of point proces
Externí odkaz:
http://arxiv.org/abs/1609.00687
We consider a stationary regularly varying time series which can be expressedas a function of a geometrically ergodic Markov chain. We obtain practical conditionsfor the weak convergence of the tail array sums and feasible estimators ofcluster statis
Externí odkaz:
http://arxiv.org/abs/1511.04903
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