Similar Search Results

Modelling asymmetric dependence using copula functions : an application to value-at-risk in the energy sector

Bastianin, Andrea - 2009

have been analyzed: (i) are there nonnormalities in the marginals? (ii) are there nonnormalities in the dependence … the univariate, as well as at the multivariate level. When considering the dependence structure of the data I have found … exercise has shown that models based on Normal marginals and/or with symmetric dependence structure fail to deliver accurate …

Persistent link: https://www.econbiz.de/10008810287

Fat-tails in VAR models

Chiu, Ching Wai Jeremy; Mumtaz, Haroon; Pinter, Gabor - 2014

We confirm that standard time-series models for US output growth, inflation, interest rates and stock market returns feature non-Gaussian error structure. We build a 4-variable VAR model where the orthogonolised shocks have a Student t-distribution with a time-varying variance. We find that in...

Persistent link: https://www.econbiz.de/10010339759

VAR models with fat tails and dynamic asymmetry

Kiss, Tamás; Mazur, Stepan; Nguyen, Hoang; Österholm, Pär - 2024

In this paper, we extend the standard Gaussian stochastic-volatility Bayesian VAR by employing the generalized hyperbolic skew Student's t distribution for the innovations. Allowing the skewness parameter to vary over time, our specification permits flexible modelling of innovations in terms of...

Persistent link: https://www.econbiz.de/10015084442

A generalised stochastic volatility in mean VAR

Mumtaz, Haroon - 2018

This paper introduces a VAR with stochastic volatility in mean where the residuals of the volatility equations and the observation equations are allowed to be correlated. This implies that exogeneity of shocks to volatility is not assumed apriori and structural shocks can be identified ex-post...

Persistent link: https://www.econbiz.de/10011812167

A generalised stochastic volatility in mean VAR : an updated algorithm

Mumtaz, Haroon - 2020

In this note we present an updated algorithm to estimate the VAR with stochastic volatility proposed in Mumtaz (2018). The model is re-written so that some of the Metropolis Hastings steps are avoided.

Persistent link: https://www.econbiz.de/10012243290

Adaptive models and heavy tails

Delle Monache, Davide; Petrella, Ivan - 2014

This paper proposes a novel and flexible framework to estimate autoregressive models with time-varying parameters. Our setup nests various adaptive algorithms that are commonly used in the macroeconometric literature, such as learning-expectations and forgetting-factor algorithms. These are...

Persistent link: https://www.econbiz.de/10010382183

Trends cycles and seasons : econometric methods of signal extraction

Pollock, David Stephen G. - 2017

Persistent link: https://www.econbiz.de/10011581633

Bayesian predictive distributions of oil returns using mixed data sampling volatility models

Virbickaite, Audrone; Nguyen, Hoang; Minh-Ngoc Tran - 2023

This study explores the benefits of incorporating fat-tailed innovations, asymmetric volatility response, and an extended information set into crude oil return modeling and forecasting. To this end, we utilize standard volatility models such as Generalized Autoregressive Conditional...

Persistent link: https://www.econbiz.de/10014252427

Forecasting co-volatilities via factor models with asymmetry and long memory in realized covariance

Asai, Manabu; McAleer, Michael - 2014

Persistent link: https://www.econbiz.de/10010348322

On the use of spectral value decomposition for the construction of composite indices

Farnia, Luca - 2019

High dimensional composite index makes experts' preferences in setting weights a hard task. In the literature, one of the approaches to derive weights from a data set is Principal Component or Factor Analysis that, although conceptually different, they are similar in results when FA is based on...

Persistent link: https://www.econbiz.de/10011999119