Thesis supervisor: István Berkes
Location of studies (in Hungarian): BME Matematika Intézet/MTA Rényi Abbreviation of location of studies: BME
Description of the research topic:
Statistical analysis of financial data has received considerable attention in the literature during the last 20 years. Several models have been suggested to capture special features of
financial processes and most of these models have the property that the conditional variance (or the conditional scaling) of the considered process depends on the past. The best known and most
often used examples are the autoregressive conditionally heteroscedastic (ARCH) process introduced by Engle (1982) and its generalizations, like the GARCH, exponential GARCH, augmented GARCH models and their variations serving special purposes. ARCH
processes and their generalizations have been a crucial tool to model asset returns, exchange rates and to describe time-varying volatility and the lasting effect of shocks in financial time series.
Besides their economical importance, these models are very important for the 'pure' mathematical theory as well: the study of ARCH and related processes gives a profound insight into the structure of nonlinear times series, in particular to processes exhibiting strong dependence. We suggest various topics in this field such as parameter estimation, asymptotic properties, dependence structure, change point problems and inference for functional data sets.
Required language skills: angol Number of students who can be accepted: 1
Deadline for application: 2015-05-31
2024. IV. 17. ODT ülés Az ODT következő ülésére 2024. június 14-én, pénteken 10.00 órakor kerül sor a Semmelweis Egyetem Szenátusi termében (Bp. Üllői út 26. I. emelet).
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