Results 121 to 130 of about 5,171,937 (249)

Robust Estimation and Inference for Time‐Varying Unconditional Volatility

Journal of Time Series Analysis, EarlyView.
ABSTRACT We derive a general and robust estimator of a large class of parametric specifications of time‐varying unconditional volatility of financial returns, both univariate and multivariate, and establish the Consistency and Asymptotic Normality (CAN) of the estimator.
Adam Lee, Rickard Sandberg, Genaro Sucarrat   +2 more
wiley   +1 more source

Copie-Buch von Geh.-Rath. Professor Dr. phil. R. Lipschitz Bonn. No. 1 / Brief an Herr College [Leopold Kronecker]!

, 1885
Adressat mutmaßlich ermittelt durch Inhalt des Briefes von Rudolf Lipschitz an Hermann Bonitz vom 23.12.1884.R.
Lipschitz, Rudolf
core  

Best uniform approximation of semi-Lipschitz functions by extensions

Journal of Numerical Analysis and Approximation Theory, 2007
In this paper we consider the problem of best uniform approximation of a real valued semi-Lipschitz function \(F\) defined on an asymmetric metric space \((X,d),\) by the elements of the set \(\mathcal{E}_{d}(\left. F\right\vert _{Y})\) of all extensions
Costică Mustăţa
doaj   +2 more sources

Sequential Outlier Detection in Nonstationary Time Series

Journal of Time Series Analysis, EarlyView.
ABSTRACT A novel method for sequential outlier detection in nonstationary time series is proposed. The method tests the null hypothesis of “no outlier” at each time point, addressing the multiple testing problem by bounding the error probability of successive tests, using extreme‐value theory. The asymptotic properties of the test statistic are studied
Florian Heinrichs, Patrick Bastian, Holger Dette   +2 more
wiley   +1 more source

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

Testing Distributional Granger Causality With Entropic Optimal Transport

Journal of Time Series Analysis, EarlyView.
ABSTRACT We develop a novel nonparametric test for Granger causality in distribution based on entropic optimal transport. Unlike classical mean‐based approaches, the proposed method directly compares the full conditional distributions of a response variable with and without the history of a candidate predictor.
Tao Wang
wiley   +1 more source

Marchenko–Pastur Laws for Daniell Smoothed Periodograms

Journal of Time Series Analysis, EarlyView.
ABSTRACT Given a sample X0,…,Xn−1$$ {X}_0,\dots, {X}_{n-1} $$ from a d$$ d $$‐dimensional stationary time series (Xt)t∈ℤ$$ {\left({X}_t\right)}_{t\in \mathbb{Z}} $$, the most commonly used estimator for the spectral density matrix F(θ)$$ F\left(\theta \right) $$ at a given frequency θ∈[0,2π)$$ \theta \in \left[0,2\pi \right) $$ is the Daniell smoothed ...
Ben Deitmar
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, Bouchra R. Nasri, Bruno N. Rémillard   +2 more
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

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, Alexander Back, Timo Teräsvirta   +2 more
wiley   +1 more source