Results 21 to 30 of about 244 (54)
Bayesian GLS Identification of Autoregressive Moving Average Models
التجارة والتمويل, 2023 In this article, a new Bayesian approach is used to identify the autoregressive moving average models. Employing approximation error is the foundation of the suggested Bayesian methodology.
Howaida Elsayed
semanticscholar +1 more source
Functional generalized autoregressive conditional heteroskedasticity [PDF]
, 2015 Heteroskedasticity is a common feature of financial time series and is commonly addressed in the model building process through the use of ARCH and GARCH processes. More recently multivariate variants of these processes have been in the focus of research
Aue, Alexander +2 more
core +2 more sources
Afrika Statistika, 2018 In this paper, we consider a Bayesian analysis of a change in the mean of independent gaussian samples in the presence of a single outlier. An unconditional Bayesian significance test for testing change versus no change is performed under consideration ...
Abdeldjalil Slama, H. Fellag
semanticscholar +1 more source
Time series aggregation, disaggregation and long memory [PDF]
, 2007 We study the aggregation/disaggregation problem of random parameter AR(1) processes and its relation to the long memory phenomenon. We give a characterization of a subclass of aggregated processes which can be obtained from simpler, "elementary", cases ...
A. Philippe +12 more
core +5 more sources
Auto-tail dependence coefficients for stationary solutions of linear stochastic recurrence equations and for GARCH(1,1) [PDF]
, 2010 We examine the auto-dependence structure of strictly stationary solutions of linear stochastic recurrence equations and of strictly stationary GARCH(1, 1) processes from the point of view of ordinary and generalized tail dependence coefficients.
B Basrak +6 more
core +1 more source
Volatility filtering in estimation of kurtosis (and variance)
Dependence Modeling, 2019 The kurtosis of the distribution of financial returns characterized by high volatility persistence and thick tails is notoriously difficult to estimate precisely.
Anatolyev Stanislav
doaj +1 more source
Estimating the COGARCH(1,1) model - a first go [PDF]
, 2005 We suggest moment estimators for the parameters of a continuous time GARCH(1,1) process based on equally spaced observations. Using the fact that the increments of the COGARCH(1,1) process are ergodic, the resulting estimators are consistent.
Haug, Stephan +3 more
core +2 more sources
Dependence Modeling, 2019 The relationship between the European stock market and the crude oil depends on the significance of the different industries in the European economy. The literature points to a structural change after the 2008 crisis without getting into details of which
Ferreiro Javier Ojea
doaj +1 more source
Copula multivariate GARCH model with constrained Hamiltonian Monte Carlo
Dependence Modeling, 2019 The Copula Multivariate GARCH (CMGARCH) model is based on a dynamic copula function with time-varying parameters. It is particularly suited for modelling dynamic dependence of non-elliptically distributed financial returns series.
Burda Martin, Bélisle Louis
doaj +1 more source
Stationarity and geometric ergodicity of a class of nonlinear ARCH models [PDF]
, 2006 A class of nonlinear ARCH processes is introduced and studied. The existence of a strictly stationary and $\beta$-mixing solution is established under a mild assumption on the density of the underlying independent process.
Sa\"{ı}di, Youssef +1 more
core +2 more sources