Results 151 to 160 of about 71,229 (282)
Outlier detection in GARCH models [PDF]
We present a new procedure for detecting multiple additive outliers in GARCH(1,1) models at unknown dates. The outlier candidates are the observations with the largest standardized residual. First, a likelihood-ratio based test determines the presence and timing of an outlier.
Jurgen A. Doornik, Marius Ooms
openaire +5 more sources
ABSTRACT This paper investigates the intricate relationship between climate policy uncertainty (CPU) and energy market dynamics, focusing on fossil‐based and renewable/low‐carbon energy assets. Utilising a comprehensive dataset spanning from April 1987 to December 2023, comprising monthly observations of CPU, stock market returns, spot oil prices and ...
Dimitrios Asteriou, Anastasia Dimiski
wiley +1 more source
We present a core‐shell structure‐induced surface reconstruction treatment for PbS quantum dots (QDs). Highly detective photodetectors and imagers in the short‐wave infrared region are demonstrated based on the surface‐reconstructed QDs with reduced dark current density and improved QD stacking configuration, as shown by grazing‐incidence small‐angle X‐
Fan Fang +12 more
wiley +1 more source
Predicting Tail-related Risk Measures: The Consequences of Using GARCH Filters for non-GARCH Data [PDF]
We investigate the consequences for value-at-risk and expected short-fall purposes of using a GARCH filter on various mis-specified processes.
Amine JALAL, Michael ROCKINGER
core
Abstract This paper examines the link between climate risk, energy consumption, and financial market performance in a sample of emerging countries over the period 2000–2024. The objective is to model the dynamic interactions between these three dimensions, in order to understand the extent to which energy dependence and exposure to climate risks ...
Abdelkader Mohamed Derbali
wiley +1 more source
From Reactive to Proactive Volatility Modeling With Hemisphere Neural Networks
ABSTRACT We revisit maximum likelihood estimation (MLE) for macroeconomic density forecasting through a novel neural network architecture with dedicated mean and variance hemispheres. Our architecture features several key ingredients making MLE work in this context.
Philippe Goulet Coulombe +2 more
wiley +1 more source
Volatility models with innovations from new maximum entropy densities at work [PDF]
Generalized autoregressive conditional heteroskedasticity (GARCH) processes have become very popular as models for financial return data because they are able to capture volatility clustering as well as leptokurtic unconditional distributions which ...
Fischer, Matthias J. +2 more
core
Forecasting Related Time Series
ABSTRACT A collection of time series are “related” if they follow similar stochastic processes and/or they are statistically dependent. This paper proposes a related time series (RTS) forecasting model that exploits these relationships. The model's foundation is a set of univariate Gaussian autoregressions, one for each series, which are then augmented
Ulrich K. Müller, Mark W. Watson
wiley +1 more source
Revisiting EWMA in High‐Frequency‐Based Portfolio Optimization: A Comparative Assessment
ABSTRACT This paper compares the statistical and economic performance of state‐of‐the‐art high‐frequency (HF) based multivariate volatility models with a simpler, widely used alternative, the Exponentially Weighted Moving Average (EWMA) filter. Using over two decades of 100 U.S.
Laura Capera Romero, Anne Opschoor
wiley +1 more source
By precisely engineering the geometry of a fiber‐based optical resonator, the interaction between confined light and external ultrasound waves is significantly amplified without sacrificing light confinement, boosting the ultrasound detection sensitivity beyond 0.5 mPa/√Hz.
Tai‐Anh La +2 more
wiley +1 more source

