Empirical‐Process Limit Theory and Filter Approximation Bounds for Score‐Driven Time Series Models
Journal of Time Series Analysis, EarlyView.ABSTRACT This article examines the filtering and approximation‐theoretic properties of score‐driven time series models. Under specific Lipschitz‐type and tail conditions, new results are derived, leading to maximal and deviation inequalities for the filtering approximation error using empirical process theory.
Enzo D'Innocenzo
wiley +1 more source
On Testing for Independence Between Generalized Error Models of Several Time Series
Journal of Time Series Analysis, EarlyView.ABSTRACT We define generalized innovations associated with generalized error models having arbitrary distributions, that is, distributions that can be mixtures of continuous and discrete distributions. These models include stochastic volatility models and regime‐switching models with possibly zero‐inflated regimes.
Kilani Ghoudi +2 more
wiley +1 more source
Quantifying the Linguistic Complexity of Pan-Homophonic Events in Stock Market Volatility Dynamics. [PDF]
Entropy (Basel)Zhang Y, Tian J, Zou Y, Zhang X, Cai X.
europepmc +1 more source
Penalized Convex Estimation in Dynamic Location Models
Journal of Time Series Analysis, EarlyView.ABSTRACT This paper studies L1$$ {L}^1 $$‐penalized estimation for location models yt=mt+ϵt$$ {y}_t={m}_t+{\epsilon}_t $$, where mt$$ {m}_t $$ is defined by a possibly non‐Markovian recursion and ϵt$$ {\epsilon}_t $$ is a martingale difference sequence with possibly time‐varying conditional variance.
Reda Alami Chentoufi
wiley +1 more source
Moving Aggregate Modified Autoregressive Copula‐Based Time Series Models (MAGMAR‐Copulas)
Journal of Time Series Analysis, EarlyView.ABSTRACT Copula‐based time series models can model univariate and stationary time series in a flexible way by decomposing the joint distribution of consecutive observations into a copula and the stationary distribution. Implicitly, this approach assumes a finite Markov order. In reality, a time series may not follow the Markov property.
Sven Pappert
wiley +1 more source
Cross-market volatility spillovers between China and the United States: A DCC-EGARCH-t-Copula framework with out-of-sample forecasting. [PDF]
PLoS OneZeng J, Wu J.
europepmc +1 more source
Parametric Time‐Variation in the Unconditional Volatility: Estimation and Inference
Journal of Time Series Analysis, EarlyView.ABSTRACT We propose modeling time‐variation in the unconditional volatility by augmenting the standard GARCH model by a deterministic time‐varying intercept. The model, called the additive time‐varying (ATV‐)GARCH model, can be interpreted as a reduced form of a model including covariates and can be derived from a multiplicative decomposition of ...
Niklas Ahlgren +2 more
wiley +1 more source
Prediction, lag and mixture effects of meteorology and pollutants on the incidence of pulmonary tuberculosis in Jining City, China. [PDF]
BMC Public HealthCao H, Liu W, Yuan J, Wang W, Hou W.
europepmc +1 more source
Detecting Multiple Change Points in Linear Models With Heteroscedasticity
Journal of Time Series Analysis, EarlyView.ABSTRACT The problem of detecting change points in the parameters of a linear regression model with errors and covariates exhibiting heteroscedasticity is considered. Asymptotic results for weighted functionals of the cumulative sum (CUSUM) processes of model residuals are established when the model errors are weakly dependent and non‐stationary ...
Lajos Horváth +2 more
wiley +1 more source
A Predictive and Adaptive Virtual Exposure Framework for Spider Fear: A Multimodal VR-Based Behavioral Intervention. [PDF]
Healthcare (Basel)Mohamed HG +6 more
europepmc +1 more source