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Berkes István
A gazdaságtan és a pénzügy nemlineáris idősorainak statisztikai elemzése. (Statistical Analysis of Nonlinear Time Series in Economy and Finance.)

TÉMAKIÍRÁS

Intézmény: Budapesti Műszaki és Gazdaságtudományi Egyetem
matematika- és számítástudományok
Matematika- és Számítástudományok Doktori Iskola

témavezető: Berkes István
helyszín: BME Matematika Intézet/MTA Rényi
helyszín rövidítés: BME


A kutatási téma leírása:

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.

előírt nyelvtudás: angol
felvehető hallgatók száma: 1

Jelentkezési határidő: 2016-12-31

 
Minden jog fenntartva © 2007, Országos Doktori Tanács - a doktori adatbázis nyilvántartási száma az adatvédelmi biztosnál: 02003/0001. Program verzió: 1.2318 ( 2016. XI. 26. )