Evaluation of Hedge Fund Returns Value at Risk Using GARCH Models [PDF]
The aim of this research paper is to evaluate hedge fund returns Value-at-Risk by using GARCH models. To perform the empirical analysis, one uses the HFRX daily performance hedge fund strategy subindexes and spans the period March 2003 – March 2008.
Sabrina Khanniche
core
Journal of Product Innovation Management, EarlyView.ABSTRACT Despite their transformative potential, Industrial Internet of Things (IIoT) platforms often fail to evolve into scalable ecosystems. Research on IIoT platforms attributes failure to discrete factors such as governance misalignment or technological complexity and rarely considers how failure unfolds.
Philipp Kernstock +3 more
wiley +1 more source
Quasi-Maximum Exponential Likelihood Estimation of Conditional Quantiles for GARCH Models Based on High-Frequency Augmented Data. [PDF]
Entropy (Basel)Zhang Z, Zhao S, Cheng J, Wang A.
europepmc +1 more source
The relationship between inflation, output growth, and their uncertainties: Nonlinear Multivariate GARCH-M evidence [PDF]
In this paper, we propose a nonlinear multivariate GARCH-M model. We have illustrated the actual modelling by applying the models to inflation and output growth variables and found that the effects of real and nominal uncertainties are regime-dependent ...
Tolga Omay
core
Journal of Sleep Research, Volume 34, Issue 2, April 2025.Summary The interplay of daily life factors, including mood, physical activity, or light exposure, influences sleep architecture and quality. Laboratory‐based studies often isolate these determinants to establish causality, thereby sacrificing ecological validity.
Anna M. Biller +8 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
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
Strategic Risk Based Forecasting of Brent Crude Oil Prices: A Comparative Analysis of Econometric and Machine Learning Models. [PDF]
Entropy (Basel)Yılmaz TE, Zehir C.
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